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We propose a simple model consisting of two separated markets: the market for good y and the market for good x. Purchasing information about consumer behavior in the former market helps the monopolist firm, in the latter market, to price-discriminate. Consumers differ in their income and in their level of myopia. Personal data market regulation could both increase consumers' awareness about the treatment of their data and allow them to have their data erased from the data holder. We find that the former aspect of the policy reduces the number of transactions, and hence tends to reduce total surplus, while the second typically boosts willingness to pay of consumers and has positive effects on surplus, provided that the share of high-income consumers is not too high. The overall effect of regulation on total welfare depends on the share of high-income and myopic consumers.

This issue of Research in Law and Economics covers several areas of important research by a variety of international scholars. It contains theoretical papers on privacy, the protection of personal data, the use of regulatory monitoring under legal standards vs. rules, a study of the properties of market efficiency in securities fraud litigation, as well as an analysis of nonexclusionary price floors. It also contains an empirical paper on the relationship between uncertainty of patent approval of artificial intelligence applications and the Supreme Court's decision in Alice Corp. v. CLS Bank International. Finally, Volume 30 contains a law-and-economics assessment of the Chinese financial system within the context of the trade-off between centralized control and rapid growth.

Volumetric air receivers experience high thermal stress as a consequence of the intense radiation flux they are exposed to when used for heat and/or power generation. This study aims to propose a proper design that is required for the absorber and its holder to ensure efficient heat transfer between the fluid and solid phases and to avoid system failure due to thermal stress.

The design and modeling processes are applied to both the absorber and its holder. A multi-channel explicit geometry design and a discrete model is applied to the absorber to investigate the conjugate heat transfer and thermo-mechanical stress levels present in the steady-state condition. The discrete model is used to calibrate the initial state of the continuum model that is then used to investigate the transient operating states representing cloud-passing events.

The steady-state results constitute promising findings for operating the system at the desired airflow temperature of 700°C. In addition, we identified regions with high temperatures and high-stress values. Furthermore, the transient state model is capable of capturing the heat transfer and fluid dynamics phenomena, allowing the boundaries to be checked under normal operating conditions.

Thermal stress analysis of the absorber and the steady/transient-state thermal analysis of the absorber/holder were conducted. Steady-state heat transfer in the explicit model was used to calibrate the initial steady-state of the continuum model.

The purpose of this paper is to develop a methodology for knowledge discovery in emergency response service databases based on police occurrence reports, generating information to help law enforcement agencies plan actions to investigate and combat criminal activities.

The developed model employs a methodology for knowledge discovery involving text mining techniques and uses latent Dirichlet allocation (LDA) with collapsed Gibbs sampling to obtain topics related to crime.

The method used in this study enabled identification of the most common crimes that occurred in the period from 1 January to 31 December of 2016. An analysis of the identified topics reaffirmed that crimes do not occur in a linear manner in a given locality. In this study, 40 per cent of the crimes identified in integrated public safety area 5, or AISP 5 (the historic centre of the city of RJ), had no correlation with AISP 19 (Copacabana – RJ), and 33 per cent of the crimes in AISP 19 were not identified in AISP 5.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary manner to the identification of criminal practices and their characteristics based on police occurrence reports stored in emergency response databases. The generated knowledge enables law enforcement experts to assess, reformulate and construct differentiated strategies for combating crimes in a given locality.

The production of knowledge from the emergency service database contributes to the government integrating information with other databases, thus enabling the improvement of strategies to combat local crime. The proposed model contributes to research on big data, on the innovation aspect and on decision support, for it breaks with a paradigm of analysis of criminal information.

The originality of the study lies in the integration of text mining techniques and LDA to detect crimes in a given locality on the basis of the criminal occurrence reports stored in emergency response service databases.

The purpose of this paper is to develop a method for the discovery of knowledge in emergency response databases based on police incident reports, generating information that identifies local criminal demands that allow the selection of the appropriate policing strategies portfolio to solve the problem.

The developed model uses a methodology for the discovery of knowledge involving text mining techniques using Latent Dirichlet Allocation (LDA) integrated with the ELECTRE I multicriteria method.

The developed method allowed the identification of the most common criminal demands that occurred from January 1 to December 31, 2016, in the policing areas studied. One of the crimes does not occur homogeneously in a particular locality. In this study, it was initially observed that 40 per cent of the crimes identified in the Integrated Public Safety Area 5, or AISP-5, (historical city center of RJ) had no correlation with AISP-19 (Copacabana - RJ), and 33 per cent of crimes crimes in AISP-19 were not identified in AISP-5. This finding guided the second part of the method that sought to identify which portfolio of policing strategies would be most appropriate for the identified demands. In this sense, using the ELECTRE I method, eight different scenarios were constructed where it can be identified that for each specific criminal demand set there is a set of policing strategies to be applied.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary way to the identification of criminal practices and their characteristics based on reports of police occurrences stored in emergency response databases. The knowledge generated through the identification of criminal demands allows law enforcement decision makers to evaluate and choose among the available policing strategies, which best suit the reality they study, and produce the reduction of criminal indices.

It is possible to infer that by choosing appropriate strategies to combat local crime, the proposed model will increase the population’s sense of safety through an effective reduction in crime.

The originality of the study lies in the integration of text mining techniques, LDA and the ELECTRE I method for detecting crime in a given location based on crime reports stored in emergency response databases, enabling identification and choice, from customized policing strategies to particular criminal demands.

The purpose of this paper is to devise a tool based on computational fluid dynamics (CFD) and machine learning (ML), for the assessment of potential airborne microbial transmission in enclosed spaces. A gated recurrent units neural network (GRU-NN) is presented to learn and predict the behaviour of droplets expelled through breaths via particle tracking data sets.

A computational methodology is used for investigating how infectious particles that originated in one location are transported by air and spread throughout a room. High-fidelity prediction of indoor airflow is obtained by means of an in-house parallel CFD solver, which uses a one equation Spalart–Allmaras turbulence model. Several flow scenarios are considered by varying different ventilation conditions and source locations. The CFD model is used for computing the trajectories of the particles emitted by human breath. The numerical results are used for the ML training.

In this work, it is shown that the developed ML model, based on the GRU-NN, can accurately predict the airborne particle movement across an indoor environment for different vent operation conditions and source locations. The numerical results in this paper prove that the presented methodology is able to provide accurate predictions of the time evolution of particle distribution at different locations of the enclosed space.

This study paves the way for the development of efficient and reliable tools for predicting virus airborne movement under different ventilation conditions and different human positions within an indoor environment, potentially leading to the new design. A parametric study is carried out to evaluate the impact of system settings on time variation particles emitted by human breath within the space considered.

The purpose of this paper is to identify the main factors influencing agri-food small and medium enterprises (SMEs) participation in business networks and to evaluate the impacts of these factors on network structure and the ensuing competitive strategy.

The study is articulated in four main steps. First, a critical literature review is conducted concerning the main approaches to firm competitive advantage and the role of stakeholder relationships. Then, three research questions are formulated and discussed in the light of two case studies describing the implementation of an innovative contractual solution in Italy (i.e. business network contracts). Finally, based on these findings, a set of more general “propositions” are stated and included in a provisional conceptual model that schematically depicts an integrated vision of the antecedents and mechanisms influencing SMEs business network structure and competitive strategy.

The study results pointed out the opportunity to adopt an integrated approach, combining resource-based view and stakeholder causal scope approaches. The provisional conceptual model proposed illustrates the role of both external and internal resources and relational constructs to shaping network structure and competitive strategy.

The study’s contribution is twofold. First, the empirical study shed light on opportunities and limits of two business networks with different backgrounds, approaches and outcomes towards value creation. Second, the conceptual framework proposed advances our understanding and knowledge of the factors and mechanisms influencing SMEs business network structure and competitive strategy.

This paper aims to deal with characterisation of the thermal performance of a hybrid tubular and cavity solar thermal receiver.

The coupled optical-flow-thermal analysis is carried out on the proposed receiver design. Modelling is performed in two and three dimensions for estimating heat loss by natural convection for an upward-facing cavity. Heat loss obtained in two dimensions by solving coupled continuity, momentum and energy equation inside the cavity domain is compared with the loss obtained using an established Nusselt number correlation for realistic receiver performance prediction.

It is found that radiation emission from a heated cavity wall to the ambient is the dominant mode of heat loss from the receiver. The findings recommend that fluid flow path must be designed adjacent to the surface exposed to irradiation of concentrated flux to limit conduction heat loss.

On-sun experimental tests need to be performed to validate the numerical study.

Numerical analysis of receivers provides guidelines for effective and efficient solar thermal receiver design.

Pressurised air receivers designed from this method can be integrated with Brayton cycles using air or supercritical carbon-dioxide to run a turbine generating electricity using a solar heat source.

The present paper proposes a novel method for coupling the flux map from ray-tracing analysis and using it as a heat flux boundary condition for performing coupled flow and heat transfer analysis. This is achieved using affine transformation implemented using extrusion coupling tool from COMSOL Multiphysics software package. Cavity surface natural convection heat transfer coefficient is obtained locally based on the surface temperature distribution.

This study aims to determine how taxes can be used to bridge income inequality gap in sub-Saharan Africa (SSA).

A panel data set of 36 SSA countries was analysed using generalised method of moments.

The results suggest that an increase in direct taxes relative to indirect taxes has a positive significant impact on income inequality. This is mostly due to the progressive nature of direct taxes as compared to indirect taxes.

This research contributes to the scant literature on how specific tax components affect income inequality, especially in developing countries.