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Advances in Big Data, artificial Intelligence and data-driven innovation bring enormous benefits for the overall society and for different sectors. By contrast, their misuse can lead to data workflows bypassing the intent of privacy and data protection law, as well as of ethical mandates. It may be referred to as the ‘creep factor’ of Big Data, and needs to be tackled right away, especially considering that we are moving towards the ‘datafication’ of society, where devices to capture, collect, store and process data are becoming ever-cheaper and faster, whilst the computational power is continuously increasing. If using Big Data in truly anonymisable ways, within an ethically sound and societally focussed framework, is capable of acting as an enabler of sustainable development, using Big Data outside such a framework poses a number of threats, potential hurdles and multiple ethical challenges. Some examples are the impact on privacy caused by new surveillance tools and data gathering techniques, including also group privacy, high-tech profiling, automated decision making and discriminatory practices. In our society, everything can be given a score and critical life changing opportunities are increasingly determined by such scoring systems, often obtained through secret predictive algorithms applied to data to determine who has value. It is therefore essential to guarantee the fairness and accurateness of such scoring systems and that the decisions relying upon them are realised in a legal and ethical manner, avoiding the risk of stigmatisation capable of affecting individuals’ opportunities. Likewise, it is necessary to prevent the so-called ‘social cooling’. This represents the long-term negative side effects of the data-driven innovation, in particular of such scoring systems and of the reputation economy. It is reflected in terms, for instance, of self-censorship, risk-aversion and lack of exercise of free speech generated by increasingly intrusive Big Data practices lacking an ethical foundation. Another key ethics dimension pertains to human-data interaction in Internet of Things (IoT) environments, which is increasing the volume of data collected, the speed of the process and the variety of data sources. It is urgent to further investigate aspects like the ‘ownership’ of data and other hurdles, especially considering that the regulatory landscape is developing at a much slower pace than IoT and the evolution of Big Data technologies. These are only some examples of the issues and consequences that Big Data raise, which require adequate measures in response to the ‘data trust deficit’, moving not towards the prohibition of the collection of data but rather towards the identification and prohibition of their misuse and unfair behaviours and treatments, once government and companies have such data. At the same time, the debate should further investigate ‘data altruism’, deepening how the increasing amounts of data in our society can be concretely used for public good and the best implementation modalities.

The purpose of this paper is to present a conceptual framework of cool for social marketing through a comprehensive literature review and integrating extant literature on cool.

A comprehensive search and review of extant literature across social marketing, business disciplines, arts, psychology, social sciences and humanities was undertaken to develop an understanding of cool and its relevance to social marketing. The review permitted developing a comprehensive set of characteristics that are associated with cool.

A conceptual framework of cool organised according to the following dimensions is presented and discussed: deviating from norm, self-expressive, indicative of maturity, subversive, pro-social, evasive, and attractive.

This paper advances theoretical knowledge in the social marketing domain by offering a conceptual framework of cool, and by suggesting a set of guidelines to develop cool social marketing programs.

This paper aims to present the basic assumptions for creation of social Fröhlich condensate and attract attention of other researchers (both from physics and socio-political science) to the problem of modeling of stability and order preservation in highly energetic society coupled with social energy bath of high temperature.

The model of social Fröhlich condensation and its analysis are based on the mathematical formalism of quantum thermodynamics and field theory (applied outside of physics).

The presented quantum-like model provides the consistent operational model of such complex socio-political phenomenon as Fröhlich condensation.

The model of social Fröhlich condensation is heavily based on theory of open quantum systems. Its consistent elaboration needs additional efforts.

Evidence of such phenomenon as social Fröhlich condensation is demonstrated by stability of modern informationally open societies.

Approaching the state of Fröhlich condensation is the powerful source of social stability. Understanding its informational structure and origin may help to stabilize the modern society.

Application of the quantum-like model of Fröhlich condensation in social and political sciences is really the novel and original approach to mathematical modeling of social stability in society exposed to powerful information radiation from mass-media and Internet-based sources.

This study aims to answer the following questions: How do consumers’ perceptions of brand coolness affect brand relationship outcomes and how do brand coolness effects differ between product brands and service brands?

A quantitative survey was used to collect data from 1,500 consumers assigned to assess one of 20 popular product and service brands in Vietnam. Partial least square structural equation modeling was used to analyze the data.

Data analysis reveals that both dimensions of brand coolness (i.e. self-oriented and other-oriented coolness) exert positive impacts on brand relationship outcomes (i.e. brand satisfaction, brand love and brand advocacy) through brand attitude (i.e. the evaluative mechanism) and self-brand connection (i.e. the identity mechanism). While the identity mechanism of brand coolness effects is more prominent in product brands, the evaluative mechanism is more pronounced for service brands.

This research provides practical guidance for brand managers to build strong customer relationships by leveraging their brand coolness and the mechanisms underlying coolness effects. This study suggests a tailored application of brand coolness dimensions to different branded entities.

This research contributes to the brand coolness literature by validating a two-dimensional brand coolness structure encompassing self-oriented and other-oriented coolness, in accordance with a value-based conceptualization of the concept. For mass brand studies, this study recommends the exclusion of rebellious and subcultural attributes, as well as the utility of pre-determined brands as evaluated objects, in measuring brand coolness. This study also illuminates dual mediation mechanisms and moderation of the branded entity underlying brand coolness effects on consumer–brand relationships.

This paper aims to show that a simplified surface fitting model can be efficient in determining the energy consumption during milk cooling by an on-farm direct expansion bulk milk cooler (DXBMC). The study reveals that milk volume and the temperature gradient between the room and the final milk temperature can effectively be used for predicting the energy consumption within 95% confidence bounds.

A data acquisition system comprised a Landis and Gyr E650 power meter, TMC6-HE temperature sensors, and HOBO UX120-006M 4-channel analog data logger was designed and built for monitoring of the DXBMC. The room temperature where the DXBMC is housed was measured using a TMC6-HE temperature sensor, connected to a Hobo UX120-006M four-channel analog data logger which was configured to log at one-minute intervals. The electrical energy consumed by the DXBMC was measured using a Landis and Gyr E650 meter while the volume of milk was extracted from on the farm records.

The results showed that the developed model can predict the electrical energy consumption of the DXBMC within an acceptable accuracy since 80% of the variation in the electrical energy consumption by the DXBMC was explained by the mathematical model. Also, milk volume and the temperature gradient between the room and final milk temperature in the BMC are primary and secondary contributors, respectively, to electrical energy consumption by the DXBMC. Based on the system that has been monitored the findings reveal that the DXBMC was operating within the expected efficiency level as evidenced by the optimized electrical energy consumption (EEC) closely mirroring the modelled EEC with a determination coefficient of 0.95.

Only one system was monitored due to unavailability of funding to deploy several data acquisition systems across the country. The milk blending temperatures, effects of the insulation of the DXBMC, were not taken into account in this study.

The developed model is simple to use, cost effective and can be applied in real-time on the dairy farm which will enable the farmer to quickly identify an increase in the cooling energy per unit of milk cooled.

The developed easy to use model can be used by dairy farmers on similar on-farm DXBMC; hence, they can devise ways to manage their energy consumption on the farm during the cooling of milk and foster some energy efficiency initiatives.

The implementation of the developed model can be useful to dairy farmers in South Africa. Through energy optimization, the maintenance of the DXBMC can be determined and scheduled accordingly.

This paper aims to test the inverse analysis, based on the standard least-square method, which will finally lead to find the appropriate parameters of modelling of the brain cooling process.

To test the presented in this paper method of inverse analysis the numerical simulations of the bioheat transfer process in the neonatal body were performed. To model the bioheat transfer the Pennes bioheat equation and the modified Fiala model were applied.

The performed tests of the inverse analysis proved that it is possible to estimate the proper parameters of the process using this tool, but always with the small mistake.

The presented method still requires a lot of tests. The test with the data from real measurements can be very valuable.

The determination of the proper parameters of the bioheat transfer in the neonatal body can finally be used to perform the numerical simulations of the brain cooling process.

The performance of the numerical simulations of the brain cooling process in the proper way can finally helps protect newborns’ health and life.

In the paper the attempt of the inverse analysis in order to determine the parameters of bioheat transfer in the newborn's body is made.