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Socio-cultural impacts of tourism have been widely researched within a social exchange theoretical framework, yet it seems that this theory could be neglecting those more emotional elements, which at the same time could be considered key to understand how tourism affects the society. Therefore, the purpose of this paper is to look at the social exchange theory (SET) from a different perspective and focus on those less rational influencing factors that could be shaping residents’ attitudes towards tourism.

Based on six main constructs, a quantitative survey-based research is developed in the small city of Girona with the purpose to broaden the versality of the theory, with the support of in-depth exploratory interviews. Thus, an exhaustive study of the influences that attachment to communities, involvement in the tourism industry and personal benefits derived from tourism could have on residents’ perceptions is developed.

This paper suggests that those smaller tourism destinations might mirror themselves in those close big tourism destinations and thus residents’ opinions and attitudes seem to be influenced by the situations lived in these larger tourism destinations. Moreover, this paper stresses on the importance of interactions and relationships between tourists and residents to boost personal benefits from tourism. Finally, results show how those attached citizens tend to support tourism development, which could be explained by the proudness they feel when others value what for them is home.

This paper contributes to the current understanding of attitudes towards tourism within the SET framework, especially relating to covering those more emotional elements of social interactions. Moreover, there seems to be a gap in current research relating to small urban destinations that is aimed to be covered in this research.

This article sets out to show how principles and questions about method that underlie a way of interpreting the discipline of conservation and restoration can find results in research and studies, aiming at achieving even conscious reuse process. The occasion is the very recent research performed on the former Church of Saints Gerolamo and Francesco Saverio in Genoa, Italy, the Jesuit church annexed to the 17th-century College of the order. It is a small Baroque jewel in the heart of the ancient city, former University Library and actually abandoned, forgotten for years, inaccessible and awaiting a new use.

The two-year work carried out on the monumental building was conducted according to a study and research methodology developed and refined over the years within the activities of the School of Specialisation in Architectural Heritage and Landscape of the University of Genoa. It is a multidisciplinary and rigorous approach, which aims to train high-level professionals, up-to-date and aware of the multiple problems that interventions on existing buildings, especially of a monumental nature, involve.

The biennal study has been carried out within the activities of the Post-Graduate Programme in Architectural Heritage and Landscape of the University of Genoa. The work methodology faces the challenges of the contemporary complexity, raised by the progressive broadening of the concept of cultural “heritage” and by the problems of its conservation, its active safeguard and its reuse: safety in respect of seismic risk, fire and hydro geological instability, universal accessibility – cognitive, physical and alternative – resource efficiency, comfort and savings in energy consumption, sustainability, communication and involvement of local communities and stakeholders.

The goals of the work were the following: understanding of the architectural heritage, through the correlated study of its geometries, elements and construction materials, surfaces, structures, spaces and functions; understanding of the transformations that the building has undergone over time, relating the results of historical reconstructions from indirect sources and those of direct archaeological analysis; assessment of the state of conservation of the building recognising phenomena of deterioration, damage, faults and deficits that affect materials, construction elements, systems and structures; identification of the causes and extent of damage, faults and deficits, assessing the vulnerability and level of exposure of the asset to the aggression of environmental factors and related risks; evaluation of the compatibility between the characteristics of the available spaces, the primary needs of conservation, the instance of regeneration and possible new uses; the definition of criteria and guidelines for establishing the planning of conservation, restoration and redevelopment interventions.

This research aims to study self-defense behaviors from coronavirus disease 2019 (COVID-19) and to investigate factors affecting the prevention and control behavior of COVID-19 among personnel at Rajamangala University of Technology Thanyaburi.

The sample was 405 personnel of Rajamangala University of Technology Thanyaburi. The sample was calculated by using the Yamane formula at a confidence level of 95. The researcher collected the data between July 1 and 15, 2020. Questionnaire items were developed following the health belief model (HBM). The questionnaire contains basic information, knowledge of COVID-19, perception of COVID-19 and COVID-19 preventive behaviors. Data were analyzed by descriptive statistics, correlation coefficients and multiple regression analysis at the statistical significance level of 0.05.

The results showed that the sample had an average knowledge about COVID-19 of 8.93, the perceived of risk and severity of COVID-19 was presented average of 4.22 and 3.48. The perceived of benefits and barriers of COVID-19 showed average of 4.31 and 2.72 and mean of COVID-19 prevention and control behaviors was 2.41. The multiple regression analysis showed that the model can explain the various self-defense behaviors from COVID-19 of 11.30%. Perception of the benefits of COVID-19 had a statistically significant effect on self-defense behaviors from COVID-19 at the level 0.05 (Beta = 0.232, 95% CI: 1.233–3.395, p < 0.001).

Based on the results, the relationship between HBM and COVID-19 prevention behavior can be clearly seen. This study found the perception of benefit toward COVID-19 affected prevention practice. Thus, using HBM could be useful in improving preventive behaviors of COVID-19.

Forests support human livelihoods and mitigate against climate change, yet they are at a risk of irreversible loss due to high degradation rates. The success of forest conservation mechanisms depends on involvement and support by forest dependent communities. In this paper, the authors assess forest dependent household's willingness to pay (WTP) labour or cash for a conservation programme seeking to restore degraded forestland in Mount Elgon Forest, Kenya.

Data were collected from 919 households in Mt Elgon forest reserve, Kenya. A double bounded contingent valuation approach was used to examine households' WTP and an ordered probit model to estimate the determinants of WTP.

The findings of the study show a higher WTP for conservation through labour days (12 days/month, equivalent to 1800 KES/month) compared to cash (KES 450/month). Forest dependence has a significant influence on households' willingness to support conservation activities. A higher WTP was observed amongst households with higher vulnerability (high shocks value, low asset value and those in the poorest wealth categories) implying that they are more willing to contribute for forest conservation.

While emerging literature on WTP for forest conservation is growing, few studies have paid attention on the influence of forest dependence on WTP for forest conservation. There are limited studies on use of in-kind contribution as a payment vehicle for WTP. The study's findings show a high WTP in form of labour suggesting the importance of embracing in-kind contribution as a mechanism of supporting forest conservation in contexts of developing countries.

In recent years, owing to the rapidly increasing labor costs, the demand for robots in daily services and industrial operations has been increased significantly. For further applications and human–robot interaction in an unstructured open environment, fast and accurate tracking and strong disturbance rejection ability are required. However, utilizing a conventional controller can make it difficult for the robot to meet these demands, and when a robot is required to perform at a high-speed and large range of motion, conventional controllers may not perform effectively or even lead to the instability.

The main idea is to develop the control law by combining the SMC feedback with the ADRC control architecture to improve the robustness and control quality of a conventional SMC controller. The problem is formulated and solved in the framework of ADRC. For better estimation and control performance, a generalized proportional integral observer (GPIO) technique is employed to estimate and compensate for unmodeled dynamics and other unknown time-varying disturbances. And benefiting from the usage of GPIO, a new SMC law can be designed by synthesizing the estimation and its history.

The employed methodology introduced a significant improvement in handling the uncertainties of the system parameters without compromising the nominal system control quality and intuitiveness of the conventional ADRC design. First, the proposed method combines the advantages of the ADRC and SMC method, which achieved the best tracking performance among these controllers. Second, the proposed controller is sufficiently robust to various disturbances and results in smaller tracking errors. Third, the proposed control method is insensitive to control parameters which indicates a good application potential.

High-performance robot tracking control is the basis for further robot applications in open environments and human–robot interfaces, which require high tracking accuracy and strong disturbance rejection. However, both the varied dynamics of the system and rapidly changing nonlinear coupling characteristic significantly increase the control difficulty. The proposed method gives a new replacement of PID controller in robot systems, which does not require an accurate dynamic system model, is insensitive to control parameters and can perform promisingly for response rapidity and steady-state accuracy, as well as in the presence of strong unknown disturbances.

In this work, the authors present some of the key results found during early efforts to model the COVID-19 outbreak inside a UK prison. In particular, this study describes outputs from an idealised disease model that simulates the dynamics of a COVID-19 outbreak in a prison setting when varying levels of social interventions are in place, and a Monte Carlo-based model that assesses the reduction in risk of case importation, resulting from a process that requires incoming prisoners to undergo a period of self-isolation prior to admission into the general prison population.

Prisons, typically containing large populations confined in a small space with high degrees of mixing, have long been known to be especially susceptible to disease outbreaks. In an attempt to meet rising pressures from the emerging COVID-19 situation in early 2020, modellers for Public Health England’s Joint Modelling Cell were asked to produce some rapid response work that sought to inform the approaches that Her Majesty’s Prison and Probation Service (HMPPS) might take to reduce the risk of case importation and sustained transmission in prison environments.

Key results show that deploying social interventions has the potential to considerably reduce the total number of infections, while such actions could also reduce the probability that an initial infection will propagate into a prison-wide outbreak. For example, modelling showed that a 50% reduction in the risk of transmission (compared to an unmitigated outbreak) could deliver a 98% decrease in total number of cases, while this reduction could also result in 86.8% of outbreaks subsiding before more than five persons have become infected. Furthermore, this study also found that requiring new arrivals to self-isolate for 10 and 14 days prior to admission could detect up to 98% and 99% of incoming infections, respectively.

In this paper we have presented models which allow for the studying of COVID-19 in a prison scenario, while also allowing for the assessment of proposed social interventions. By publishing these works, the authors hope these methods might aid in the management of prisoners across additional scenarios and even during subsequent disease outbreaks. Such methods as described may also be readily applied use in other closed community settings.

These works went towards informing HMPPS on the impacts that the described strategies might have during COVID-19 outbreaks inside UK prisons. The works described herein are readily amendable to the study of a range of addition outbreak scenarios. There is also room for these methods to be further developed and built upon which the timeliness of the original project did not permit.

Values of parameters such as temperature, humidity, number of plastic products and the location of plastic injection moulds are required to determine the efficiency of plastic injection moulds with a view to improving the quality of the outputs. This article determined the appropriate sensors for the measurement of these essential parameters in the most suitable form of representation of the data to aid a proficient analysis of the data.

The outputs of these sensors were obtained by connecting the sensors to the general-purpose input/output (GPIO) pins of a Raspberry Pi and writing a Python programme for the connected GPIO pins. The values of the outputs of these sensors were represented in a graphical form. The connection of the Raspberry Pi and the sensors were done with a full-sized breadboard and jumper wires. A computer-aided design (CAD) of the connections was produced using Fritzing software.

The appropriate sensors determined are MLX90614 infrared thermometer sensor, DHT11 humidity sensor, pixy2 vision sensor and Neo-6m GPS sensor. This study proposed that the sensors analytic system be applied on an industrial plastic injection mould to measure and display the various parameters of the injection moulds for the purpose of understanding and improving the performance of the injection mould

An electronic system that provides the continuous values of essential parameters of a plastic injection mould in operation.

Social networks (SNs) have recently evolved from a means of connecting people to becoming a tool for social engineering, radicalization, dissemination of propaganda and recruitment of terrorists. It is no secret that the majority of the Islamic State in Iraq and Syria (ISIS) members are Arabic speakers, and even the non-Arabs adopt Arabic nicknames. However, the majority of the literature researching the subject deals with non-Arabic languages. Moreover, the features involved in identifying radical Islamic content are shallow and the search or classification terms are common in daily chatter among people of the region. The authors aim at distinguishing normal conversation, influenced by the role religion plays in daily life, from terror-related content.

This article presents the authors' experience and the results of collecting, analyzing and classifying Twitter data from affiliated members of ISIS, as well as sympathizers. The authors used artificial intelligence (AI) and machine learning classification algorithms to categorize the tweets, as terror-related, generic religious, and unrelated.

The authors report the classification accuracy of the K-nearest neighbor (KNN), Bernoulli Naive Bayes (BNN) and support vector machine (SVM) [one-against-all (OAA) and all-against-all (AAA)] algorithms. The authors achieved a high classification F1 score of 83\%. The work in this paper will hopefully aid more accurate classification of radical content.

In this paper, the authors have collected and analyzed thousands of tweets advocating and promoting ISIS. The authors have identified many common markers and keywords characteristic of ISIS rhetoric. Moreover, the authors have applied text processing and AI machine learning techniques to classify the tweets into one of three categories: terror-related, non-terror political chatter and news and unrelated data-polluting tweets.

While the disruptive potential of artificial intelligence (AI) has been receiving growing consensus with regards to its positive influence on entrepreneurship, there is a clear lack of systematization in academic literature pertaining to this correlation. The current research seeks to explore the impact of AI on entrepreneurship as an enabler for entrepreneurs, taking into account the crucial application of AI within all Industry 4.0 technological paradigms, such as smart factory, the Internet of things (IoT), augmented reality (AR) and blockchain.

A systematic literature review was used to analyze all relevant studies forging connections between AI and entrepreneurship. The cluster interpretation follows a structure that we called the “AI-enabled entrepreneurial process.”

This study proves that AI has profound implications when it comes to entrepreneurship and, in particular, positively impacts entrepreneurs in four ways: through opportunity, decision-making, performance, and education and research.

The framework's practical value is linked to its applications for researchers, entrepreneurs and aspiring entrepreneurs (as well as those acting entrepreneurially within established organizations) who want to unleash the power of AI in an entrepreneurial setting.

This research offers a model through which to interpret the impact of AI on entrepreneurship, systematizing disconnected studies on the topic and arranging contributions into paradigms of entrepreneurial and managerial literature.