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The purpose of this paper is to present a new core hypothesis on laughter. It has been built by putting together ideas from several disciplines: neurodynamics, evolutionary neurobiology, social networks, and communication studies. The hypothesis focusses on the social nature of laughter and contributes to ascertain its evolutionary origins in connection with the cognitive and social-emotional functions it performs.

An in-depth examination of laughter in the social communication context and along the life cycle of the individual is performed. This instinctive behaviour that appears as a “virtual”, non-physical form of “grooming” would serve as a bond-making instrument in human groups. Further, the neurodynamic events underlying laughter production – and particularly the form of the neural entropy gradients – are congruent with a sentic hypothesis about the different emotional contents of laughter and their specific effects on bonding dynamics.

The new behavioural and neurodynamic tenets introduced about this unusual sound feature of our species justify the ubiquitous presence it has in social interactions at large and along the life cycle of the individual. Laughter, far from being a curious evolutionary relic or a rather inconsequential innate behaviour, should be considered as a highly efficient tool for inter-individual problem solving and for maintenance of social bonds.

Laughter, the authors would conclude, has been evolutionarily kept and augmented as an optimized tool for unconscious cognitive-emotional problem solving, and at the same time as a useful way to preserve the essential fabric of social bonds in close-knit groups and within human societies at large.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

This paper aims to investigate the natural convection fluid flow and heat transfer in a finned/multi-pipe cavity.

The cavity is filled with the CuO-water nanofluid. The Koo–Kleinstreuer–Li model is used to estimate the dynamic viscosity and consider Brownian motion. On the other hand, the effect of the shapes of nanoparticles on the thermal conductivity and related heat transfer rate is presented.

In the present investigation, the governing parameters are Rayleigh number, CuO nanoparticle concentration in pure water and the thermal arrangements of internal active fins and solid bodies. Impacts of these parameters on the nanofluid flow, heat transfer rate, total/local entropy generation and heatlines are presented. It is concluded that adding nanoparticles to the pure fluid has a significant positive influence on the heat transfer performance. In addition, the average Nusselt number and total entropy generation have direct a relationship with the Rayleigh number. The thermal arrangement of the internal bodies and fins is a good controlling tool to determine the desired magnitude of heat transfer rate.

The originality of this paper is to use the lattice Boltzmann method in simulating the nanofluid flow and heat transfer within a cavity included with internal active bodies and fins.

The purpose of this paper is to carry out a comprehensive review of some latest studies devoted to natural convection phenomenon in the enclosures because of its significant industrial applications.

Geometries of the enclosures have considerable influences on the heat transfer which will be important in energy consumption. The most useful geometries in engineering fields are treated in this literature, and their effects on the fluid flow and heat transfer are presented.

A great variety of geometries included with different physical and thermal boundary conditions, heat sources and fluid/nanofluid media are analyzed. Moreover, the results of different types of methods including experimental, analytical and numerical are obtained. Different natures of natural convection phenomenon including laminar, steady-state and transient, turbulent are covered. Overall, the present review enhances the insight of researchers into choosing the best geometry for thermal process.

A comprehensive review on the most practical geometries in the industrial application is performed.

The purpose of this paper is to present a new technique based on the combination of wavelet packet transform (WPT) and artificial neural networks (ANNs) for predicting the ice accretion on the surface of an airfoil.

Wavelet packet decomposition is used to reduce the number of input vectors to ANN and to improve the training convergence. An ANN is developed with five variables (velocity, temperature, liquid water content, median volumetric diameter and exposure time) taken as input data and one dependent variable (the decomposed ice shape) given as the output. For the purpose of comparison, three different ANNs, back-propagation network (BP), radial basis function network (RBF) and generalized regression neural network (GRNN), are trained to simulate the wavelet packet coefficients as a function of the in-flight icing conditions.

The predicted ice accretion shapes are compared with the corresponding results from previously published NASA experimentation, LEWICE and the Fourier-expansion-based method. It is found that the BP network has an advantage on predicting the rime ice, and the RBF network is relatively suitable for the glaze ice, while the GRNN can be applied for both without classifying the specimens. Results also show an advantage of WPT in performing the analysis of ice accretion information and the prediction accuracy is improved as well.

The proposed method is open to further improvement and investment due to its small computational resource requirement and efficient performance.

The simulation method combining ANN and WPT outlined here can lay the foundation for further research relating to ice accretion prediction under different ice cloud conditions.

In this chapter, we overview different neuroenhancement techniques that could be applied for accelerating the learning process in a number of tasks that are associated with occupational roles. The techniques range from: (1) pharmaceutical and invasive methods with limited applicability to the healthy population, due to possible side effects and obtrusiveness; (2) game-based brain training that shows task-specific potential, but may not generalize; and (3) a promising new research direction in which the goal is to “train” the brain to reach an optimal cognitive state for performing a given task, and remain in this state by self-regulation. However, in order to accomplish this goal of brain training, the neurological markers that best discriminate good task performance need to be identified. We also review a number of initial studies in this chapter which have analyzed such markers in a variety of training-related applications for different occupations, such as military/security (e.g., marksmanship, deadly force judgment and decision making, submarine piloting and navigation, phishing detection), medicine (e.g., robot-assisted surgery), banking (e.g., financial traders), sports (e.g., golf, archery, and baseball), or entertainment (e.g., musicians and actors). The promising results of these early studies are fueling interest in neuroscience-based technology and methods in the rapidly developing field of organizational neuroscience (e.g., leadership research). We conclude the chapter with a discussion of future research directions.

The purpose of this paper is to ask whether a first‐order‐cybernetics concept, Shannon's Information Theory, actually allows a far‐reaching mathematics of perception allegedly derived from it, Norwich et al.'s “Entropy Theory of Perception”.

All of The Entropy Theory, 35 years of publications, was scrutinized for its characterization of what underlies Shannon Information Theory: Shannon's “general communication system”. There, “events” are passed by a “source” to a “transmitter”, thence through a “noisy channel” to a “receiver”, that passes “outcomes” (received events) to a “destination”.

In the entropy theory, “events” were sometimes interactions with the stimulus, but could be microscopic stimulus conditions. “Outcomes” often went unnamed; sometimes, the stimulus, or the interaction with it, or the resulting sensation, were “outcomes”. A “source” was often implied to be a “transmitter”, which frequently was a primary afferent neuron; elsewhere, the stimulus was the “transmitter” and perhaps also the “source”. “Channel” was rarely named; once, it was the whole eye; once, the incident photons; elsewhere, the primary or secondary afferent. “Receiver” was usually the sensory receptor, but could be an afferent. “Destination” went unmentioned. In sum, the entropy theory's idea of Shannon's “general communication system” was entirely ambiguous.

The ambiguities indicate that, contrary to claim, the entropy theory cannot be an “information theoretical description of the process of perception”.

Scrutiny of the entropy theory's use of information theory was overdue and reveals incompatibilities that force a reconsideration of information theory's possible role in perception models. A second‐order‐cybernetics approach is suggested.

Estimation of the rental price of a residential property is important to real estate investors, financial institutions, buyers and the government. These estimates provide information for assessing the economic viability and the tax accruable, respectively. The purpose of this study is to develop a neural network model for estimating the rental prices of residential properties in Cape Town, South Africa.

Data were collected on 14 property attributes and the rental prices were collected from relevant sources. The neural network algorithm was used for model estimation and validation. The data relating to 286 residential properties were collected in 2018.

The results show that the predictive accuracy of the developed neural network model is 78.95 per cent. Based on the sensitivity analysis of the model, it was revealed that balcony and floor area have the most significant impact on the rental price of residential properties. However, parking type and swimming pool had the least impact on rental price. Also, the availability of garden and proximity of police station had a low impact on rental price when compared to balcony.

In the light of these results, the developed neural network model could be used to estimate rental price for taxation. Also, the significant variables identified need to be included in the designs of new residential homes and this would ensure optimal returns to the investors.

A number of studies have shown that crime influences the value of residential properties. However, to the best of the authors’ knowledge, there is limited research investigating this relationship within the South African context.

The purpose of this paper is to use a text-based sentiment indicator to explain variations in direct property market liquidity in the USA.

By means of an artificial neural network, market sentiment is extracted from 66,070 US real estate market news articles from the S&P Global Market Intelligence database. For training of the network, a distant supervision approach utilizing 17,822 labeled investment ideas from the crowd-sourced investment advisory platform Seeking Alpha is applied.

According to the results of autoregressive distributed lag models including contemporary and lagged sentiment as independent variables, the derived textual sentiment indicator is not only significantly linked to the depth and resilience dimensions of market liquidity (proxied by Amihud’s (2002) price impact measure), but also to the breadth dimension (proxied by transaction volume).

These results suggest an intertemporal effect of sentiment on liquidity for the direct property market. Market participants should account for this effect in terms of their investment decisions, and also when assessing and pricing liquidity risk.

This paper not only extends the literature on text-based sentiment indicators in real estate, but is also the first to apply artificial intelligence for sentiment extraction from news articles in a market liquidity setting.