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[Display omitted] Nanobubbles (NBs) are classified in two distinct categories: surface and bulk. Surface NBs are readily observed using atomic force microscopy (AFM), while the existence of bulk NBs has been a subject of debate, conflicting with the diffusion theory's predictions. Current methodologies for identifying bulk NBs yield inconclusive results. In this study, Langmuir Blodgett (LB) technique and AFM, are utilized to visualize NB imprints on anionic, cationic and zwitterionic lipid films deposited on glass-slide substrates. Our analysis of Langmuir monolayers compression isotherms reveals the impact of bulk NBs on lipid monolayer development. AFM scans of the deposited lipid films consistently show NB imprints. Notably, cationic and zwitterionic film depositions exhibit NB formations from the 1st layer, whereas in anionic films, these formations are observed only after the 3rd layer. These results suggest that the origin of these imprinted formations may be attributed to bulk NBs. [ABSTRACT FROM AUTHOR]

[Display omitted] The smallest nanodrop tractable with macroscopic notions such as the interfacial energy could be determined by comparing heterogeneous nucleation observations and capillary theory predictions at decreasing drop diameters d p. This is done here for the condensation of n -butanol vapors on polyethylene glycol nano-globules (3 nm ≤ d p ≤ 9 nm). We use published activation probability measurements P(w,d p), where w is the accurately controlled saturation ratio of n -butanol vapor in a gas stream exiting a saturator. The maximal saturation ratio achieved in the nucleation region by cooling this gas-vapor stream in the apparatus of Gallar et al. satisfies S max = Cw. The key unknown constant C and the preexponential term K governing the nucleation rate are determined by assuming that classical theory applies to the largest particles used. This yields P(S max ,d p) data, directly comparable with capillary theory with perfect wetting. Excellent agreement is found above 5 nm for the critical dependence S max (d p) resulting from the constraint P(S max ,d p) = 0.5. The entire P(S max ,d p) curves also agree closely between 5 and 7 nm. Smaller particles depart only slightly from theory, even at d p = 3 nm. Capillary theory hence describes accurately the heterogeneous nucleation process above 3–5 nm, provides a reliable method to determine S max , and yields experimentally the nucleation rate constant K. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new nonlocal modal hydrodynamic theory for fluid–structure interactions (FSI) of light, flexible cantilever beams and plates undergoing small amplitude vibrations in Newtonian, incompressible, viscous, heavy fluids otherwise at rest. For low aspect ratio flexible structures and high mode numbers, three dimensional (3D) and nonlocal fluid effects become prominent drivers of the coupled dynamics, to the point that existing local hydrodynamic theories based on two dimensional (2D) fluid approximations become inadequate to predict the system response. On the other hand, our approach is based on a rigorous, yet efficient, 3D treatment of the hydrodynamic loading on cantilevered thin structures. The off-line solution of the FSI problem results in the so-called nonlocal modal hydrodynamic function matrix, that is, the representation of the nonlocal hydrodynamic load operator on a basis formed by the structural modes. Our theory then integrates the nonlocal hydrodynamics within a fully coupled structural modal model in the frequency domain. We compare and discuss our theory predictions in terms of frequency response functions, mode shapes, hydrodynamic loads, quality factors, added mass ratios with the predictions of the classical local approaches, for different actuation scenarios, identifying the limitations of the hypotheses underlying existing treatments. Importantly, we also validate our new model with experiments conducted on flexible square plates. While computationally efficient, our fully coupled theory is exact up to numerical truncation and can bridge knowledge gaps in the design and analysis of FSI systems based on low aspect ratio flexible beams and plates. [ABSTRACT FROM AUTHOR]

[Display omitted] Understanding ionic solutions in single-digit nanoconfinement is crucial to explain the behavioral transition of confined solutions. This is particularly the case when the system length scale crosses the classical key length scales describing energetics and equilibrium of ionic solutions next to surfaces. Experimentally probing nanoconfinement would open large perspectives to test modelling or theory predictions. Here, using a new test vehicle that consists in 3 and 5 nm-height silica nanochannels associated with an original characterization technique based on the interface hard X-ray reflectivity analysis, we directly probed the transport of solutions containing cations having increasing kosmotropic properties (XCl 2 with X: Ba < Ca < Mg) and obtained their distributions inside the nanochannels. We observed that cation adsorption decreases with the size of the confinement and that small cation adsorption is favored. In addition, nanochannel clogging occurs when ions tend to form ion pairs. These ion pairs may play the role of nano-sized prenucleation clusters leading to phase precipitation. These results evidence the specific ion effect in single-digit nanoconfinement that may result in dramatic changes of solution properties. In this line, our new method opens new perspectives for the characterization of ionic solutions and of interfaces in single-digit nanoconfinement. [ABSTRACT FROM AUTHOR]

Whenever resources are limited and indivisible, fighting will evolve as a means to resolve ownership. Among such resources are mates, and individuals (usually males) of many species compete agonistically with rivals in order to gain access to potential mates. However, securing access is not necessarily enough to guarantee a mating or, if a mating is obtained, to guarantee that it is effective for securing reproductive success. Thus, in addition to fighting, individuals participate in a wealth of behaviours to maximize their reproductive success, from courtship to sperm competition to mate guarding. In recent years, the striking parallels between fighting and mating behaviour have become a subject of discussion. In particular, insights have been drawn from the predictions of contest theory to help us understand the use of repetitive signalling in courtship. Here, we take this discussion further, highlighting similarities between fighting and mating in the use of dynamic repeated behaviours, which function to (1) advertise quality and (2) convince or coerce an individual to relinquish the contested resource (gametes in terms of mating). We focus specifically on a performance trait of emerging interest in the field of animal contests, skill. We identify behaviours used throughout the mating process in which skill is likely to be of importance for securing success, and highlight key questions for future study. • Fighting and mating both involve the performance of repeated behaviours. • Performance rate (vigour) and skill are of known importance for fighting success. • Here, we discuss the possible role of skill in determining mating success. • We highlight functional similarities between fighting and mating behaviours. • We then identify mating behaviours for which a role for skill is strongly implied. [ABSTRACT FROM AUTHOR]

• This article introduced the stiffness characteristics of the joint surface between the mechanical connection structures of the spindle-tool holder, tool holder-spring clamp, and spring clamp-tool of the spindle system in the robot machining system into the prediction of machining stability. • This paper proposed a method for establishing a tool tip frequency response prediction model for robot milling systems, which improves the accuracy of theoretical prediction of the tool tip dynamic characteristics of robot milling systems under different postures, Compared to the method of obtaining tool tip frequency response of robot milling system through experiments to support milling stability prediction, the method proposed in this paper effectively improves the efficiency of robot milling stability prediction. • The prediction results of the proposed tool tip frequency response prediction model were imported, and a two-degree-of-freedom chatter prediction model considering the regenerative effect is established, and the robot milling chatter is predicted considering redundant degrees of freedom. The accuracy of prediction results is verified by milling experiment. The change of tool tip frequency response caused by the posture dependence of robot dynamic is one of the key problems that make it difficult to accurately predict the milling stability of robot. In this paper, a tool tip frequency response prediction method considering the interface stiffness characteristics of spindle-tool system is proposed for stability prediction of robotic milling under the condition of posture variation. Firstly, the interface stiffness models of spindle-toolholder, toolholder-spring clip and spring clip-tool are established based on Yoshimura's unit area method. Then, The dynamics model for the robot body and the interface stiffness models for spindle-tool system are imported into the finite element analysis model of the spindle system, so that the prediction of the tool tip frequency response is realized by harmonic response analysis. Compared with the experimental results, the maximum error of the natural frequency was not more than 2 %, and the maximum error of the amplitude was not more than 12 %. Finally, the 2 DOF robot milling stability prediction model is established. Then the robot milling chatter is predicted considering redundant degrees of freedom from the perspective of regenerative chatter prediction theory, and the accuracy of prediction results is verified by milling experiment. [ABSTRACT FROM AUTHOR]

In many domains, committees bargain over a sequence of policies and a policy remains in effect until a new agreement is reached. In this paper, I argue that, in order to assess the consequences of veto power, it is important to take into account this dynamic aspect. I analyze an infinitely repeated divide-the-dollar game with an endogenous status quo policy. I show that full appropriation by the veto player is the only stable policy when legislators are sufficiently impatient; and that, irrespective of legislators' patience and the initial division of resources, there is always an equilibrium where policy eventually gets arbitrarily close to full appropriation by the veto player. In this equilibrium, increasing legislators' patience or decreasing the veto player's proposal power makes convergence to this outcome slower and the veto player supports reforms that decrease his allocation. The main predictions of the theory find support in controlled laboratory experiments. [ABSTRACT FROM AUTHOR]

The process of sol-to-gel transition involves two kinetics regimes that lead to a gel regime as described by the Ideal Gel Point (IGP) theory. We present investigations of the kinetics of the colloidal sol-to-gel transition by combining small angle static light scattering (SASLS) and dynamic light scattering (DLS) techniques. Dilute monomer volume fractions were used to allow for a full investigation of the gelation to obtain all possible kinetic regimes. Our data verify the predictions of a kinetic theory, the ideal gel point (IGP) theory, where three regimes of kinetics are expected. We observe the first regime, the well-known cluster-dilute regime, with a kinetic exponent of z = 1. Followed by a cluster-dense regime with an enhanced kinetics and z ≃ 2. Finally, a gelation regime is observed where the aggregate growth slows and ceases to grow at the IGP predicted size, R g,G. These results quantitatively verify the IGP theory. We conclude that kinetic description provides a complete theory of the gelation process from sol to gel. [ABSTRACT FROM AUTHOR]

The nature of the relationship between deductive and inductive reasoning is a hotly debated topic. A key question is whether there is a single dimension of evidence underlying both deductive and inductive judgments. Following Rips (2001), Rotello and Heit (2009) and Heit and Rotello (2010) implemented one- and two-dimensional models grounded in signal detection theory to assess predictions for receiver operating characteristic data (ROCs), and concluded in favor of the two-dimensional model. Recently, Lassiter and Goodman (2015) proposed a different type of one-dimensional model, the Probability Threshold Model (PTM), that they concluded offered a good account of data collected over a range of decision modals (e.g., How likely, possible, or necessary is the argument conclusion?). Here, we apply the PTM and the signal detection models to ROCs from 3 large experiments in which participants made judgments about arguments varying in terms of modals introduced by Lassiter and Goodman (2015). Two independent variables that are theoretically important for the study of inductive reasoning, namely premise-conclusion similarity (as utilized in Heit & Rotello, 2010) and number of premises (as utilized in Rotello & Heit, 2009), are also varied in Experiments 1 and 2, respectively. In all cases, the PTM provides the poorest fit both quantitatively and qualitatively; the two-dimensional signal detection model is preferred. [ABSTRACT FROM AUTHOR]

Highlights • We studied the role of temporal cognition in decision-making under time pressure. • People with an accurate representation of time made more cautious decisions. • We further found evidence that people collapse decision bounds under time pressure. • We found no relation between temporal cognition and collapsing decision bounds. • This result is contrary to predictions from theory of optimal decision-making. Abstract The time available to inform decisions is often limited, for example because of a response deadline. In such circumstances, accurate knowledge of the amount of time available for a decision is crucial for optimal choice behavior. However, the relation between temporal cognition and decision-making under time pressure is poorly understood. Here, we test how the precision of the internal representation of time affects choice behavior when decision time is limited by a deadline. We show that participants with a precise internal representation of time respond more cautiously in decision-making. Furthermore, we provide an empirical test of theoretical accounts of decision-making that argue that it is optimal to commit to a decision based on increasingly less evidence as the deadline approaches (so-called 'collapsing decision bounds'). These theories entail that the speed of collapse of the decision bound should depend on the precision of the internal representation of the deadline. However, although we find evidence that participants collapse decision bounds, we found no relation between the amount of collapse and the internal representation of time. [ABSTRACT FROM AUTHOR]

Highlights • The Species-Area Relation measures how many species S inhabit islands of area A. • Previous modeling work has focused on the scaling of the mean of S with A: < S > ∞ Az. • Empirical species-area curves display scatter of data around the < S > ∞ Az prediction. • The variance of S is affected by community dynamics and habitat heterogeneity. • The origin of the fluctuations is traced to ecological processes and heterogeneity. Abstract The Species–Area Relation (SAR), which describes the increase in the number of species S with increasing area A , is under intense scrutiny in contemporary ecology, in particular to probe its reliability in predicting the number of species going extinct as a direct result of habitat loss. Here, we focus on the island SAR, which is measured across a set of disjoint habitat patches, and we argue that the SAR portrays an average trend around which fluctuations are to be expected due to the stochasticity of community dynamics within the patches, external perturbations, and habitat heterogeneity across different patches. This probabilistic interpretation of the SAR, though already implicit in the theory of island biogeography and manifest in the scatter of data points in plots of empirical SAR curves, has not been investigated systematically from the theoretical point of view. Here, we show that the two main contributions to SAR fluctuations, which are due to community dynamics within the patches and to habitat heterogeneity between different patches, can be decoupled and analyzed independently. To investigate the community dynamics contribution to SAR fluctuations, we explore a suite of theoretical models of community dynamics where the number of species S inhabiting a patch emerges from diverse ecological and evolutionary processes, and we compare stationary predictions for the coefficient of variation of S , i.e. the fluctuations of S with respect to the mean. We find that different community dynamics models diverge radically in their predictions. In island biogeography and in neutral frameworks, where fluctuations are only driven by the stochasticity of diversification and extinction events, relative fluctuations decay when the mean increases. Computational evidence suggests that this result is robust in the presence of competition for space or resources. When species compete for finite resources, and mass is introduced as a trait determining species' birth, death and resource consumption rates based on empirical allometric scalings, relative fluctuations do not decay with increasing mean S due to the occasional introduction of new species with large resource demands causing mass extinctions in the community. Given this observation, we also investigate the contribution of external disturbance events to fluctuations of S in neutral community dynamics models and compare this scenario with the community dynamics in undisturbed non-neutral models. Habitat heterogeneity within a single patch, in the context of metapopulation models, causes variability in the number of coexisting species which proves negligible with respect to that caused by the stochasticity of the community dynamics. The second contribution to SAR fluctuations, which is due to habitat heterogeneity among different patches, introduces corrections to the coefficient of variation of S. Most importantly, inter-patches heterogeneity introduces a constant, lower bound on the relative fluctuations of S equal to the coefficient of variation of a habitat variable describing the heterogeneity among patches. Because heterogeneity across patches is inevitably present in natural ecosystems, we expect that the relative fluctuations of S always tend to a constant in the limit of large mean S or large patch area A , with contributions from community dynamics, inter-patches heterogeneity or both. We provide a theoretical framework for modelling these two contributions and we show that both can affect significantly the fluctuations of the SAR. [ABSTRACT FROM AUTHOR]

Highlights • The amino acid compositions of the toxins were analyzed. • The biological properties were compared between the toxins and the non-toxins. • The random forest was used to predict the toxins by using reduced amino acid compositions. Abstract The animal toxin proteins are one of the disulfide rich small peptides that detected in venomous species. They are used as pharmacological tools and therapeutic agents in medicine for the high specificity of their targets. The successful analysis and prediction of toxin proteins may have important signification for the pharmacological and therapeutic researches of toxins. In this study, significant differences were found between the toxins and the non-toxins in amino acid compositions and several important biological properties. The random forest was firstly proposed to predict the animal toxin proteins by selecting 400 pseudo amino acid compositions and the dipeptide compositions of reduced amino acid alphabet as the input parameters. Based on dipeptide composition of reduced amino acid alphabet with 13 reduced amino acids, the best overall accuracy of 85.71% was obtained. These results indicated that our algorithm was an efficient tool for the animal toxin prediction. [ABSTRACT FROM AUTHOR]

Highlights • We create and verify a biophysically-detailed model of the olfactory blub external tufted cell. • Depolarizing (but not hyperpolaryzing) inputs received during the inter-burst interval can advance burst timing. • Specific currents controlling each burst characteristic are identified. • A heterogeneous population produces reliable responses to a range of periodic inputs, even though individual cells are entrained by inputs with higher, rather than lower frequencies. Abstract We introduce a novel detailed conductance-based model of the bursting activity in external tufted (ET) cells of the olfactory bulb. We investigate the mechanisms underlying their bursting, and make experimentally-testable predictions. The ionic currents included in the model are specific to ET cells, and their kinetic and other parameters are based on experimental recordings. We validate the model by showing that its bursting characteristics under various conditions (e.g. blocking various currents) are consistent with experimental observations. Further, we identify the bifurcation structure and dynamics that explain bursting behavior. This analysis allows us to make predictions of the response of the cell to current pulses at different burst phases. We find that depolarizing (but not hyperpolarizing) inputs received during the interburst interval can advance burst timing, creating the substrate for synchronization by excitatory connections. It has been hypothesized that such synchronization among the ET cells within one glomerulus might help coordinate the glomerular output. Next we investigate model parameter sensitivity and identify parameters that play the most prominent role in controlling each burst characteristic, such as the burst frequency and duration. Finally, the response of the cell to periodic inputs is examined, reflecting the sniffing-modulated input that these cell receive in vivo. We find that individual cells can be better entrained by inputs with higher, rather than lower, frequencies than the intrinsic bursting frequency of the cell. Nevertheless, a heterogeneous population of ET cells (as may be found in a glomerulus) is able to produce reliable periodic population responses even at lower input frequencies. [ABSTRACT FROM AUTHOR]

Highlights • Latitudes of maximum thermal fatigue vary with solar declination and thermal inertia. • Thermal fatigue is not limited to the equator but occurs in a broad band around it. • Eccentricity and obliquity can cause asymmetry in thermal fatigue. Abstract The latitudinal dependence of regolith formation by thermal fatigue is studied for variable solar declination and surface thermal inertia. We find that regolith generation takes place in a surprisingly wide band around the equator and make predictions for the regolith distribution on the target asteroids of the upcoming Hayabusa2 and OSIRIS-REx missions. [ABSTRACT FROM AUTHOR]

Abstract The kinetic theory of rarefied gases and numerical schemes based on the Boltzmann equation, have evolved to the cornerstone of non-equilibrium gas dynamics. However, their counterparts in the dense regime remain rather exotic for practical non-continuum scenarios. This problem is partly due to the fact that long-range interactions arising from the attractive tail of molecular potentials, lead to a computationally demanding Vlasov integral. This study focuses on numerical remedies for efficient stochastic particle simulations based on the Enskog–Vlasov kinetic equation. In particular, we devise a Poisson type elliptic equation which governs the underlying long-range interactions. The idea comes through fitting a Green function to the molecular potential, and hence deriving an elliptic equation for the associated fundamental solution. Through this transformation of the Vlasov integral, efficient Poisson type solvers can be readily employed in order to compute the mean field forces. Besides the technical aspects of different numerical schemes for treatment of the Vlasov integral, simulation results for evaporation of a liquid slab into the vacuum are presented. It is shown that the proposed formulation leads to accurate predictions with a reasonable computational cost. Highlights • Reviewing a thorough treatment of molecular interactions in the kinetic framework. • Translating the n-body problem underlying the mean-field description of long range interactions to a Poisson type PDE. • Validating the devised solution algorithm for liquids and dense gases in Couette and evaporation scenarios. [ABSTRACT FROM AUTHOR]

Highlights • Positive emotional memory required prediction error for destabilization. • Novel visuospatial word search task was effective at decreasing free recall. • Recognition memory was unaffected by visuospatial interference task. Abstract Prediction error is recognized as a necessary boundary condition for memory reactivation and reconsolidation. Furthermore, behavioral manipulations (e.g., visuospatial interference tasks, like Tetris) have been shown to be effective at disrupting reactivated memory. In the present study, participants created a memory with positive valence by viewing an uplifting video of a young boy who built an arcade out of cardboard boxes. Two weeks later, memory for the video was reactivated with a prediction error (i.e., new information was added) or without a prediction error (i.e., no new information was added). Following memory reactivation, participants completed a novel visuospatial interference task (i.e., a complex word search) or a control task (i.e., sitting quietly). One week following reactivation, participants completed two memory tests (i.e., free recall and recognition). Results showed that the visuospatial interference task was effective in reducing free recall, but only in the group that received a prediction error during retrieval. No other significant differences were observed, including on the recognition test. These results expand the literature on reconsolidation by showing that destabilization of memory with a positive emotional valence requires a prediction error and that a novel visuospatial interference task (i.e., complex word search) is an effective behavioral manipulation for decreasing free recall. [ABSTRACT FROM AUTHOR]

Abstract Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Highlights • We highlight two sources of confusion that affect the interpretation of multivariate decoding results. • Confusion 1: The dual use of multivariate decoding for real-world predictions and interpretation in terms of brain function. • Confusion 2: A mixture of statistical and conceptual frameworks of classical univariate analysis and multivariate decoding. • We show six differences between univariate analysis and multivariate decoding and a different meaning of signal and noise. • We use four illustrative examples to reveal these confusions and the assumptions of multivariate decoding for interpretation. [ABSTRACT FROM AUTHOR]

Abstract The success of Question and Answering (Q&A) communities mainly depends on the contribution of experts. However, there is a bottleneck for machine to identify these experts as soon as they participate in a community due to lack of enough activities during users' early participation. To tackle that, we bring human's business experience to potential expert prediction by combining machine learning and visual analytics. In this work, we propose a visual analytics system to identify potential experts semi-automatically. After the machine learning algorithm gives the result of the expert probability, analysts can locate a set of interested users whose expert probability is ambiguous and check the user information and behavior patterns of those users via the design of multi-dimension data visualization. Finally, our system models analysts' knowledge of the community members' identities, and then abstracts the knowledge quantificationally for machine learning algorithm. Thus, analysts can modify machine learning algorithm and the prediction process smoothly. A quantitative evaluation with real data has been studied to demonstrate the effectiveness of our system. [ABSTRACT FROM AUTHOR]

Two low-order methods for predicting the broadband noise downstream of a fan exit guide vane (FEGV) due to interaction with rotor wake turbulence are compared. The noise is predicted with a two-step approach in each method. The methods share the exact same first step. The unsteady vane surface pressure is computed using a strip approach. At each strip, the 2D flat-plate cascade response to a gust in the wave-number, frequency domain is calculated. An integral approach is used for this calculation. The two methods then differ in the second step. One method integrates the unsteady surface pressure together with the Green's function for a cylindrical annulus to obtain the downstream duct acoustic pressure modes. The other method computes rectilinear acoustic modes relevant to the cascade at each strip. In the second method, the total power is obtained by adding the acoustic power downstream of each strip. Both methods neglect mean flow swirl effects on acoustic propagation. The fan broadband noise workshop RC1 and FC2 cases are used as a platform to compare the two methods. When the FEGV inflow turbulent parameters are taken from different sources (e.g. hot-wire, CFD) the predictions from both methods respond similarly. The trends predicted with both methods based on turbulence level or rotor speed are similar. The trends with vane count predicted by the two methods do not match and it is unclear which prediction is correct. The actual spectrum predicted by the Green's function method for all of the cases is shown to be slightly closer to experimental results. [ABSTRACT FROM AUTHOR]

In this paper, a convected frequency-domain approach for acoustic scattering prediction is suggested, for sources and scattering surfaces in a uniform constant flow. Frequency-domain moving-medium formulations are used for prediction of the incident acoustic pressure and acoustic pressure gradient. The latter is required for evaluation of the hardwall boundary condition in a moving medium and allows a convected definition of the equivalent sources. The scattering approach is validated by the analytical case of scattering of a pulsating monopole source by a rigid sphere. The applicability of the methodology to moving-medium problems is demonstrated for rotating and pulsating monopole point sources in a uniform flow, located near an infinite flat scattering surface. The suggested approach allows frequency-domain scattering predictions for sources in a uniform constant flow of any subsonic velocity, enabling direct inclusion of convection effects on incident and scattered acoustics. The hardwall boundary condition is thus evaluated directly in a moving medium. The need for a Lorentz transform is obviated, overcoming the complexity it introduces and the limitations it imposes. [ABSTRACT FROM AUTHOR]

A novel hybrid approach is presented that can more accurately predict monthly rainfall in a tropical climate by integrating a linear stochastic model with a powerful non-linear extreme learning machine method. This new hybrid method was then evaluated by considering four general scenarios. In the first scenario, the modeling process is initiated without preprocessing input data as a base case. While in other three scenarios, the one-step and two-step procedures are utilized to make the model predictions more precise. The mentioned scenarios are based on a combination of stationarization techniques (i.e., differencing, seasonal and non-seasonal standardization and spectral analysis), and normality transforms (i.e., Box-Cox, John and Draper, Yeo and Johnson, Johnson, Box-Cox-Mod, log, log standard, and Manly). In scenario 2, which is a one-step scenario, the stationarization methods are employed as preprocessing approaches. In scenario 3 and 4, different combinations of normality transform, and stationarization methods are considered as preprocessing techniques. In total, 61 sub-scenarios are evaluated resulting 11013 models (10785 linear methods, 4 nonlinear models, and 224 hybrid models are evaluated). The uncertainty of the linear, nonlinear and hybrid models are examined by Monte Carlo technique. The best preprocessing technique is the utilization of Johnson normality transform and seasonal standardization (respectively) ( R 2  = 0.99; RMSE  = 0.6; MAE  = 0.38; RMSRE  = 0.1, MARE  = 0.06, UI  = 0.03 & UII  = 0.05). The results of uncertainty analysis indicated the good performance of proposed technique (d-factor = 0.27; 95PPU = 83.57). Moreover, the results of the proposed methodology in this study were compared with an evolutionary hybrid of adaptive neuro fuzzy inference system (ANFIS) with firefly algorithm (ANFIS-FFA) demonstrating that the new hybrid methods outperformed ANFIS-FFA method. [ABSTRACT FROM AUTHOR]

Population abundance is fundamental in ecology and conservation biology, and provides essential information for predicting population dynamics and implementing conservation actions. While a range of approaches have been proposed to estimate population abundance based on existing data, data deficiency is ubiquitous. When information is deficient, a population estimation will rely on labor intensive field surveys. Typically, time is one of the critical constraints in conservation, and management decisions must often be made quickly under a data deficient situation. Hence, it is important to acquire a theoretical justification for survey methods to meet a required estimation precision. There is no such theory available in a spatially explicit context, while spatial considerations are critical to any field survey. Here, we develop a spatially explicit theory for population estimation that allows us to examine the estimation precision under different survey designs and individual distribution patterns (e.g. random/clustered sampling and individual distribution). We demonstrate that clustered sampling decreases the estimation precision when individuals form clusters, while sampling designs do not affect the estimation accuracy when individuals are distributed randomly. Regardless of individual distribution, the estimation precision becomes higher with increasing total population abundance and the sampled fraction. These insights provide theoretical bases for efficient field survey designs in information deficiency situations. [ABSTRACT FROM AUTHOR]

Uploading all IoT Big Data to a centralized cloud for data analytics is infeasible because of the excessive latency and bandwidth limitation of the Internet. A promising approach to addressing the challenges for data analytics in IoT is “edge cloud” that pushes various computing and data analysis capabilities to multiple edge clouds. MapReduce provides an efficient way to deal with a large amount of data. When performing data analysis, a challenge is to predict the performance of MapReduce jobs. In this paper, we propose and evaluate IoTDeM, which is an extended IoT Big Data-oriented model for predicting MapReduce performance in multiple edge clouds. IoTDeM is able to predict MapReduce jobs’ total execution time in a general implementation scenario with varying reduce amounts and cluster scales in Hadoop 2, rather than Hadoop 1. The extended model is based on historical job execution records and Locally Weighted Linear Regression (LWLR) techniques to predict the execution time of each job. Through extracting more representative features to represent a job, the IoTDeM model selects a cluster scale as a crucial parameter to further extend LWLR model. In the environment of Hadoop 2 with Ceph as the storage system, the experiments verify IoTDeM can effectively predict the total execution time of MapReduce applications, with the average relative error of less than 10%. [ABSTRACT FROM AUTHOR]

Opportunistic Internet of Things (OppIoT) is a paradigm, technology, and system that promotes the opportunistic exploitation of interactions between IoT devices to achieve increased connectivity, reliability, network capacity, and overall network lifetime. The increased demand for identifying such opportunistic exploitation is illustrated by IoT scenarios, where the goal is to recognize when an opportunity for communication is possible, thereby allowing for data forwarding and routing. In an OppIoT system, devising a routing scheme is a challenging task due to the difficulty in guaranteeing the existence of connectivity between devices (nodes) and in identifying an intermediate node as a packet forwarder towards its destination. Considering that opportunistic networks (oppNets) are a subclass of OppIoT and considering IoT scenarios where the opportunistic exploitation of IoT devices is possible even in case the device’s presence is uncertain or may change over time, this paper proposes a novel routing scheme for OppNets (called Location Prediction-based Forwarding for Routing using Markov Chain (LPFR-MC)) that can also be used in IoT scenarios. The proposed LPFR-MC scheme considers the node’s present location and the angle formed by it and the corresponding source (resp. destination) to predict the node’s next location or region using a Markov chain and to determine the probability of a node moving towards the destination. Simulation results are provided, showing that the proposed LPFR-MC outperforms the existing traditional protocols in terms of message delivery probability, hop count, number of messages dropped, message overhead ratio, and average buffer time. [ABSTRACT FROM AUTHOR]

Severe fire greatly increases soil erosion rates and overland-flow in forest land. Soil erosion prediction models are essential for estimating fire impacts and planning post-fire emergency responses. We evaluated the performance of a) the Revised Universal Soil Loss Equation (RUSLE), modified by inclusion of an alternative equation for the soil erodibility factor, and b) the Disturbed WEPP model, by comparing the soil loss predicted by the models and the soil loss measured in the first year after wildfire in 44 experimental field plots in NW Spain. The Disturbed WEPP has not previously been validated with field data for use in NW Spain; validation studies are also very scarce in other areas. We found that both models underestimated the erosion rates. The accuracy of the RUSLE model was low, even after inclusion of a modified soil erodibility factor accounting for high contents of soil organic matter. We conclude that neither model is suitable for predicting soil erosion in the first year after fire in NW Spain and suggest that soil burn severity should be given greater weighting in post-fire soil erosion modelling. [ABSTRACT FROM AUTHOR]

Outside several notable exceptions, few studies have examined variations in bias crime occurrences across American communities, and how community-level factors may differentially shape violent and non-violent bias crimes across victim groups. Drawing from ecological theories of crime, this study asks, (1) what are the structural predictors of the likelihood of bias homicide occurrences? and (2) how do structural predictors differ across bias victim groups? To answer these questions, data on bias homicide are derived from the United States Extremist Crime Database (ECDB) for the years 1990 through 2014 and paired with socio- structural variables from the United States Census Bureau. Results are discussed relative to the goals of understanding where fatal bias crimes are more likely to occur as a means of informing law enforcement and policymakers interested in preventing and responding to this specific form of crime. [ABSTRACT FROM AUTHOR]

A complete fault diagnosis for the rolling bearing is proposed in this paper. Variable predictive model class discrimination (VPMCD) is a conventional pattern recognition method; however, in practice, when the fault diagnosis method is applied to small samples or in multi-correlative feature space, the stability of the VPM constructed based on the least squares (LS) method is not sufficient. Based on affinity propagation (AP) clustering, RReliefF, and sequential forward search, the ARSFS is proposed to select the significant subset of original feature set and to reduce the dimension and multiple correlations of the feature space. Further, this paper uses two kinds of Gaussian Neural Network, namely the Radial Basis Function Neural Network (RBF) and the Generalized Regression Neural Network (GRNN), instead of the LS method to construct predictive models of VPMCD, called AOR-VPMCD. Compared with the conventional VPMCD and its improvements, based on sufficient experiments, the entire process presented in this paper can effectively identify the fault of the rolling bearing. [ABSTRACT FROM AUTHOR]

A semi-empirical model is proposed to predict the sound absorption of an acoustical unit consisting of a rigid-porous material layer with a perforated facing under the normal incidence at high sound pressure levels (SPLs) of pure tones. The nonlinearity of the perforated facing and the porous material, and the interference between them are considered in the model. The sound absorptive performance of the acoustical unit is tested at different incident SPLs and in three typical configurations: 1) when the perforated panel (PP) directly contacts with the porous layer, 2) when the PP is separated from the porous layer by an air gap and 3) when an air cavity is set between the porous material and the hard backing wall. The test results agree well with the corresponding theoretical predictions. Moreover, the results show that the interference effect is correlated to the width of the air gap between the PP and the porous layer, which alters not only the linear acoustic impedance but also the nonlinear acoustic impedance of the unit and hence its sound absorptive properties. [ABSTRACT FROM AUTHOR]

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information. [ABSTRACT FROM AUTHOR]

Toxicogenomics held great promise as an approach to enable early detection of toxicities induced by xenobiotics; however, there remain questions regarding the impact of the discipline on pharmaceutical nonclinical safety assessment. To understand the current state of toxicogenomics in the sector, an industry group surveyed companies to determine the frequency of toxicogenomics use in in vivo studies at various stages of drug discovery and development and to assess how toxicogenomics use has evolved over time. Survey data were compiled during 2016 from thirteen pharmaceutical companies. Toxicogenomic analyses were infrequently conducted in the development phase and when performed were done to address specific mechanistic questions. Prior to development, toxicogenomics use was more frequent; however, there were significant differences in approaches among companies. Across all phases, gaining mechanistic insight was the most frequent reason cited for pursing toxicogenomics with few companies using toxicogenomics to predict toxicities. These data were consistent with the commentary submitted in response to survey questions asking companies to describe the evolution of their toxicogenomics strategy. Overall, these survey data indicate that toxicogenomics is not widely used as a predictive tool in the pharmaceutical industry but is used regularly by some companies and serves a broader role in mechanistic investigations and as a complement to other technologies. [ABSTRACT FROM AUTHOR]

Prediction of skin sensitisation potential and potency by non-animal methods is the target of many active research programmes. Although the aim is to predict sensitisation potential and potency in humans, data from the murine local lymph node assay (LLNA) constitute much the largest source of quantitative data on in vivo skin sensitisation. The LLNA has been the preferred in vivo method for identification of skin sensitising chemicals and as such is potentially valuable as a benchmark for assessment of non-animal approaches. However, in common with all predictive test methods, the LLNA is subject to false positives and false negatives with an overall level of accuracy said variously to be approximately 80% or 90%. It is also necessary to consider the extent to which, for true positives, LLNA potency correlates with human potency. In this paper LLNA potency and human potency are compared so as to express quantitatively the correlation between them, and reasons for non-agreement between LLNA and human potency are analysed. This leads to a better definition of the applicability domain of the LLNA, within which LLNA data can be used confidently to predict human potency and as a benchmark to assess the performance of non-animal approaches. [ABSTRACT FROM AUTHOR]

Wright's conjecture states that the origin is the global attractor for the delay differential equation y ′ ( t ) = − α y ( t − 1 ) [ 1 + y ( t ) ] for all α ∈ ( 0 , π 2 ] when y ( t ) > − 1 . This has been proven to be true for a subset of parameter values α . We extend the result to the full parameter range α ∈ ( 0 , π 2 ] , and thus prove Wright's conjecture to be true. Our approach relies on a careful investigation of the neighborhood of the Hopf bifurcation occurring at α = π 2 . This analysis fills the gap left by complementary work on Wright's conjecture, which covers parameter values further away from the bifurcation point. Furthermore, we show that the branch of (slowly oscillating) periodic orbits originating from this Hopf bifurcation does not have any subsequent bifurcations (and in particular no folds) for α ∈ ( π 2 , π 2 + 6.830 × 10 − 3 ] . When combined with other results, this proves that the branch of slowly oscillating solutions that originates from the Hopf bifurcation at α = π 2 is globally parametrized by α > π 2 . [ABSTRACT FROM AUTHOR]

The Vehicle-Bridge-Interaction (VBI) force, i.e., the normal contact force of a tire, is a key component in the VBI mechanism. The VBI force measurement can facilitate experimental studies of the VBI as well as input-output bridge structural identification. This paper introduces an innovative method for calculating the interaction force by using dynamic tire pressure monitoring. The core idea of the proposed method combines the ideal gas law and a basic force model to build a relationship between the tire pressure and the VBI force. Then, unknown model parameters are identified by the Extended Kalman Filter using calibration data. A signal filter based on the wavelet analysis is applied to preprocess the effect that the tire rotation has on the pressure data. Two laboratory tests were conducted to check the proposed method’s validity. The effects of different road irregularities, loads and forward velocities were studied. Under the current experiment setting, the proposed method was robust to different road irregularities, and the increase in load and velocity benefited the performance of the proposed method. A high-speed test further supported the use of this method in rapid bridge tests. Limitations of the derived theories and experiment were also discussed. [ABSTRACT FROM AUTHOR]

A health monitoring system for a transmission shaft is proposed. The solution is based on the real-time identification of the physical characteristics of the transmission shaft i.e. stiffness and damping coefficients, by using a physical oriented model and linear recursive identification. The efficacy of the suggested condition monitoring system is demonstrated on a prototype transient engine testing facility equipped with a transmission shaft capable of varying its physical properties. Simulation studies reveal that coupling shaft faults can be detected and isolated using the proposed condition monitoring system. Besides, the performance of various recursive identification algorithms is addressed. The results of this work recommend that the health status of engine dynamometer shafts can be monitored using a simple lumped-parameter shaft model and a linear recursive identification algorithm which makes the concept practically viable. [ABSTRACT FROM AUTHOR]

Glyco-Mapper is a novel systems biology product quality prediction tool created using a new framework termed: Discretized Reaction Network Modeling using Fuzzy Parameters (DReaM-zyP). Within Glyco-Mapper, users fix the nutrient feed composition and the glycosylation reaction fluxes to fit the model glycoform to the reference experimental glycoform, enabling cell-line specific glycoform predictions as a result of cell engineering strategies. Glyco-Mapper accurately predicts glycoforms associated with genetic alterations that result in the appearance or disappearance of one or more glycans with an accuracy, sensitivity, and specificity of 96%, 85%, and 97%, respectively, for publications between 1999 and 2014. The modeled glycoforms span a large range of glycoform engineering strategies, including the altered expression of glycosylation, nucleotide sugar transport, and metabolism genes, as well as an altered nutrient feeding strategy. A glycoprotein-producing CHO cell line reference glycoform was modeled and a novel Glyco-Mapper prediction was experimentally confirmed with an accuracy and specificity of 95% and 98%, respectively. Glyco-Mapper is a product quality prediction tool that provides a streamlined way to design host cell line genomes to achieve specific product quality attributes. [ABSTRACT FROM AUTHOR]

An analytical method for determination of dimensions of longitudinal crack in monolithic beams, based on frequency measurements, has been extended to model L and inverted T cracks. Such cracks including longitudinal crack arise in beams made of layered isotropic or composite materials. A new formulation for modelling cracks in bi-material beams is presented. Longitudinal crack segment sizes, for L and inverted T cracks, varying from 2.7% to 13.6% of length of Euler-Bernoulli beams are considered. Both forward and inverse problems have been examined. In the forward problems, the analytical results are compared with finite element (FE) solutions. In the inverse problems, the accuracy of prediction of crack dimensions is verified using FE results as input for virtual testing. The analytical results show good agreement with the actual crack dimensions. Further, experimental studies have been done to verify the accuracy of the analytical method for prediction of dimensions of three types of crack in isotropic and bi-material beams. The results show that the proposed formulation is reliable and can be employed for crack detection in slender beam like structures in practice. [ABSTRACT FROM AUTHOR]

Abstract The function of a particular RNA molecule within an organic system is principally determined by its structure. The current physical methods available for structure determination are time consuming and expensive. Hence, computational methods for structure prediction are often used. The prediction of the structure of a large single sequence of RNA needs a lot of research work. In the present work, a method is introduced to improve the prediction of large single sequence RNA secondary structure obtained by Mfold program using the concept of minimum free energy. The Mfold program contains a constraint option that allows forcing some helices in the predicted structure. In our method, some of the firstly formed hairpins that are expected, by a statistical study, to be present in the real structure are forced in the Mfold predicted structure. The results show improvement, toward the real structure, in the Mfold predicted structure and this gives evidence to the RNA kinetic folding. [ABSTRACT FROM AUTHOR]

We study a legislative bargaining game in which failure to agree in a given round may result in a breakdown of negotiations. In that case, each player receives an exogenous ‘disagreement value’. We characterize the set of stationary subgame perfect equilibria under all q-majority rules. Under unanimity rule, equilibrium payoffs are strictly increasing in disagreement values. Under all less-than-unanimity rules, expected payoffs are either decreasing or first increasing and then decreasing in disagreement values. We conduct experiments involving three players using majority and unanimity rule, finding qualitative support for several, but not all, of our main predictions. Having a high disagreement value is indeed an advantage under unanimity rule. Under majority rule, the player with the highest disagreement value is more likely to be excluded, but this does not consistently result in a lower average payoff. [ABSTRACT FROM AUTHOR]

Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC 50 ) data is available. Polynomial scoring functions were built using as explanatory variables the energy terms present in the MolDock and PLANTS scoring functions. Prediction performance was tested and the supervised machine learning models showed improvement in the prediction power, when compared with PLANTS and MolDock scoring functions. In addition, the machine-learning model was applied to predict binding affinity of CDK2, which showed a better performance when compared with AutoDock4, AutoDock Vina, MolDock, and PLANTS scores. [ABSTRACT FROM AUTHOR]

We compared the predictive validity of two types of Frame-of-Reference personality measures to each other and to a baseline generic measure. Each version of the measures used a unique response-format referred to as frequency-based estimation that allowed the behavioral consistency of responses to be gauged. Generic personality scales, tagged scales with “at school”, and completely modified scales were compared in their prediction of academic performance, counterproductive academic behavior, and participant reactions. Results showed that completely contextualized measures were the most predictively valid and, contrary to our expectations, behavioral consistency did not moderate the relationships. Face validity and to a lesser extent perceived predictive validity improved with increasing contextualization. We discuss the implications of our results for personality assessment in applied settings. [ABSTRACT FROM AUTHOR]

Recent years have witnessed unprecedented accumulation of DNA sequences and therefore protein sequences (predicted from DNA sequences), due to the advances of sequencing technology. One of the major sources of the hypothetical proteins is the metagenomics research. Current annotation of metagenomes (collections of short metagenomic sequences or assemblies) relies on similarity searches against known gene/protein families, based on which functional profiles of microbial communities can be built. This practice, however, leaves out the hypothetical proteins, which may outnumber the known proteins for many microbial communities. On the other hand, we may ask: what can we gain from the large number of metagenomes made available by the metagenomic studies, for the annotation of metagenomic sequences as well as functional annotation of hypothetical proteins in general? Here we propose a community profiling approach for predicting functional associations between proteins: two proteins are predicted to be associated if they share similar presence and absence profiles (called community profiles) across microbial communities. Community profiling is conceptually similar to the phylogenetic profiling approach to functional prediction, however with fundamental differences. We tested different profile construction methods, the selection of reference metagenomes, and correlation metrics, among others, to optimize the performance of this new approach. We demonstrated that the community profiling approach alone slightly outperforms the phylogenetic profiling approach for associating proteins in species that are well represented by sequenced genomes, and combining phylogenetic and community profiling further improves (though only marginally) the prediction of functional association. Further we showed that community profiling method significantly outperforms phylogenetic profiling, revealing more functional associations, when applied to a more recently sequenced bacterial genome. [ABSTRACT FROM AUTHOR]

Does personality predict how people feel in different types of situations? The present research addressed this question using data from several thousand individuals who used a mood tracking smartphone application for several weeks. Results from our analyses indicated that people’s momentary affect was linked to their location, and provided preliminary evidence that the relationship between state affect and location might be moderated by personality. The results highlight the importance of looking at person-situation relationships at both the trait- and state-levels and also demonstrate how smartphones can be used to collect person and situation information as people go about their everyday lives. [ABSTRACT FROM AUTHOR]

Research on collective action problems and the provision of public goods has yielded an array of important insights into why people sacrifice for their groups, but ignores the processes that bring people into those groups in the first place. The literature is also silent on the fact that groups providing similar public goods often compete with one another to attract new members. We extend the reach of theories of collective action problems to bring them to bear on these interrelated issues. Results from four experiments support our predictions about when group members compete to attract new adherents by sacrificing more for group goals. Further, we find that prospective members join more productive groups at much higher rates and then contribute to them at high levels. These processes give way to cumulative advantage, whereby initially productive groups continually attract new members. Thus competition for members can yield large inequalities in the size and success of collective action groups. [ABSTRACT FROM AUTHOR]

The analysis and prediction of air pollutants are of great significance in environmental research today since airborne pollution is a substantial threat, especially in urban agglomerations of China. To develop more effective warning systems and management advice, the authorities and city dwellers need more accurate forecasts of the air pollution. Most previous analysis systems were based on costly observation apparatus at fixed sites, forecasting models were usually built on observations within a certain range, and some observations contained biases. In this paper, a novel and effective framework, termed HML-AFNN, was successfully developed to analyse and forecast the concentration of particular matter (PM 2.5 ) for a selected number of forward time steps. In a simulation of the trajectory of air pollutants, the high-dimension association rules (HDAR) approach considered the tempo-spatial relations, as well as the meteorological and geographical factors of the ambient regions, as parameters. In addition, the learning vector quantization (LVQ) network was adopted to select the appropriate inputs to improve the efficiency of the training process. Moreover, an adaptive fuzzy neural network (AFNN), a combination of neural and fuzzy logic, was utilized to analyse and predict the PM 2.5 concentration. The experiment results of our study on two major urban agglomerations of China, the Jing-Jin-Ji area and Pearl River Delta, over a period of more than one year demonstrated that the developed hybrid HML-AFNN model outperforms a plain AFNN, an HM-AFNN model without LVQ and the least squares support vector machines (LS-SVM); this superior performance can be determined from the values of several error indexes, including MAE, MAPE and band errors. This hybrid model, which has robust and accurate results, shows the potential to be a political and administrative method to issue effective early warnings and to design suitable abatement strategies. [ABSTRACT FROM AUTHOR]

Invasive species are increasing in number, extent and impact worldwide. Effective invasion management has thus become a core socio-ecological challenge. To tackle this challenge, integrating spatial-temporal dynamics of invasion processes with modelling approaches is a promising approach. The inclusion of dynamic processes in such modelling frameworks (i.e. dynamic or hybrid models, here defined as models that integrate both dynamic and static approaches) adds an explicit temporal dimension to the study and management of invasions, enabling the prediction of invasions and optimisation of multi-scale management and governance. However, the extent to which dynamic approaches have been used for that purpose is under-investigated. Based on a literature review, we examined the extent to which dynamic modelling has been used to address invasions worldwide. We then evaluated how the use of dynamic modelling has evolved through time in the scope of invasive species management. The results suggest that modelling, in particular dynamic modelling, has been increasingly applied to biological invasions, especially to support management decisions at local scales. Also, the combination of dynamic and static modelling approaches (hybrid models with a spatially explicit output) can be especially effective, not only to support management at early invasion stages (from prevention to early detection), but also to improve the monitoring of invasion processes and impact assessment. Further development and testing of such hybrid models may well be regarded as a priority for future research aiming to improve the management of invasions across scales. [ABSTRACT FROM AUTHOR]

In a dynamic contest where it is costly to compete, a player who is behind must decide whether to surrender or keep fighting in the face of bleak odds. We experimentally examine the game theoretic prediction of last stand behavior in a multi-battle contest with a winning prize and losing penalty, as well as the contrasting prediction of surrendering in the corresponding contest with no penalty. We find varied evidence in support of these hypotheses in the aggregate data, but more conclusive evidence when scrutinizing individual player behavior. Players' realized strategies tend to conform to one of several “types”. We develop a taxonomy to classify player types and study how types interact and how their incidence varies across treatments. Although last stand and surrendering behaviors arise at rates responsive to the importance of losing penalties, the most prominent behavior is escalation. [ABSTRACT FROM AUTHOR]

We study the information aggregation properties of unanimous voting rules in the laboratory. In line with theoretical predictions, we find that majority rule with veto power dominates unanimity rule. We also find that the strategic voting model is a fairly good predictor of subject behavior. Finally, we exploit a framing effect to study how the presence of less sophisticated agents affects Veto's welfare properties. [ABSTRACT FROM AUTHOR]

The citrus sector seeks rapid, economical, environmentally-friendly and non-destructive technologies for monitoring external and internal changes in physical quality taking place in fruit during on-tree growth, thus allowing fruit quality to be evaluated at any stage of fruit development. The use of portable near-infrared spectroscopy (NIRS) sensors based on micro-electro-mechanical system (MEMS) technology, in conjunction with chemometric data treatment models, has already been studied for quality-control purposes in two citrus species: oranges and mandarins. The critical challenge is to develop robust and accurate universal models based on hundreds of highly heterogeneous citrus samples in order to design quality prediction models applicable to all fruits belonging to the genus Citrus, rather than models that can only be applied successfully to a single citrus species. This study evaluated and compared the performance of Modified Partial Least Squares (MPLS) and LOCAL regression algorithms for the prediction of major physical-quality parameters in all citrus fruits. Results showed that, while models developed using both linear (MPLS) and non-linear regression techniques (LOCAL) yielded promising results for the on-tree quality evaluation of citrus fruits, the LOCAL algorithm additionally increased the predictive capacity of models constructed for all the main parameters tested. These findings confirm that NIRS technology, used in conjunction with large databases and local regression strategies, increases the robustness of models for the on-tree prediction of citrus fruit quality; this will undoubtedly be of benefit to the citrus industry. [ABSTRACT FROM AUTHOR]

An algorithm to estimate the evolution of learning curves on the whole of a training data base, based on the results obtained from a portion and using a functional strategy, is introduced. We approximate iteratively the sought value at the desired time, independently of the learning technique used and once a point in the process, called prediction level, has been passed. The proposal proves to be formally correct with respect to our working hypotheses and includes a reliable proximity condition. This allows the user to fix a convergence threshold with respect to the accuracy finally achievable, which extends the concept of stopping criterion and seems to be effective even in the presence of distorting observations. Our aim is to evaluate the training effort, supporting decision making in order to reduce the need for both human and computational resources during the learning process. The proposal is of interest in at least three operational procedures. The first is the anticipation of accuracy gain, with the purpose of measuring how much work is needed to achieve a certain degree of performance. The second relates the comparison of efficiency between systems at training time, with the objective of completing this task only for the one that best suits our requirements. The prediction of accuracy is also a valuable item of information for customizing systems, since we can estimate in advance the impact of settings on both the performance and the development costs. Using the generation of part-of-speech taggers as an example application, the experimental results are consistent with our expectations. [ABSTRACT FROM AUTHOR]

Prediction error based multi-layer data embedment schemes conceal secrets into several high frequency errors by modifying their prediction error histogram (PEH). It is investigated that k -times data embedment into n / k errors of PEH produces higher embedding payload, while maintaining better stego-image quality compared to those for embedding into n distinct errors for a single time only. This paper proposes a novel multi-cycle embedment scheme in which data is embedded into the errors of a defined range in each of its k cycles. Experiments were conducted to examine the performance of the proposed scheme comparing the multi-layer vs. multi-cycle embedding schemes individually and jointly. The scheme explores the points at which significantly better payloads can be obtained at the lower image distortions. Substantial improved performance were obtained during investigations, especially while large volume data embedment. The proposed scheme can embed massive and hybrid data of type text, numeric, image and audio. [ABSTRACT FROM AUTHOR]