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The development of part quality virtual sensors requires knowledge and observability of cutting conditions and in particular tool wear as tool are consumables. This paper presents an unsupervised anomalies detection approach to assess tool wear from standard machine load sensors in order to evaluate a nonquality risk metric. The developed methodology combines physics and business rules with density estimators to analyse the behaviour of axes and spindle loads. Industrial data from an automotive production line are used to illustrate the methodology application. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) are considered the best alternative vehicular technology having the potential to cater climatic change and energy security issues of the nations. Despite various promotional policy measures, the penetration of EVs is also observed very low, especially in the developing economies in the Indian subcontinent. This demands a comprehensive analysis of individuals' EV adoption characteristics and the review of recent literature indicated that most of the research on EV adoption solely focuses on either the psychological aspects leading to its purchase, ignoring the available alternatives or on the conventional choice modelling techniques which do not explicitly consider the psychological motivation to choose the innovative technology. Therefore, this study integrates both behaviour modelling and conventional choice modelling in order to unfold the important determinants of EV adoption in developing economies. This study was conducted in India and the results of Hybrid Choice Modelling (HCM) highlighted that latent attitudes such as marketing perceptions, EV performance, policy perceptions, and EV usage constraints are significant factors influencing the EV adoption intention of individuals. In addition, the study also revealed that on-road price, EV adoption intention, willingness to pay extra for EVs, monthly household income, and age are also important predictors for choice between EVs and conventional vehicles. The study also pinpoints that pricing the electric SUV cars below INR 17 lakhs would make people consider choosing it over the conventional one. • Integrated the latent attitudes of individuals with conventional choice attributes. • Pricing electric cars (SUVs) below INR 17 lakhs enhance their faster diffusion. • A wide network of dealership showrooms will improve the perception of EV markets. • Performance of EV in terms of top speed, power, and acceleration affects the choice. • Lower income groups and elders can be the early adopters of EVs. [ABSTRACT FROM AUTHOR]

In today's online market, recommendation systems have become universal and are an aspect of any online shopping portal. The traditional approach uses the subscriber's historical knowledge, and this technique is not adequate for resolving problems with a cold start. These issues include recommendations for non-registered users or newly added customers and new items added. Session-based recommendations based on recurrent neural networks are gaining popularity for product recommendations. This is due to recurrent neural networks' ability to record sequential feature data more effectively throughout the current session, which results in more similarity between consumer behaviour sequences. Nevertheless, most state-of-the-art recurring neural networking systems completely ignore the long-term details of multiple sessions and concentrate solely on short-term communication in a single session. This paper presents a hybrid time-centric prediction model to address research issues that learn the customers' short and long-term behaviours. Experiments on the recsys challenge data set are carried out to assess the efficiency of the hybrid time-centric prediction models over the existing hybrid models in terms of HitRate and Mean-Reciprocal Rate. [ABSTRACT FROM AUTHOR]

Occupants perform various actions to satisfy their physical and non-physical needs in buildings. These actions greatly affect building operations and thus energy use. Clearly understanding and accurately modelling occupant behaviour in buildings are crucial to guide energy-efficient building design and operation, and to reduce the gap between design and actual energy performance of buildings. To study and understand occupant behaviour, a cross-sectional questionnaire survey is one of the most useful tools to gain insights on general behaviour patterns and drivers, and to find connections between human, social and local comfort parameters. In this study, 33 projects were reviewed from the energy-related occupant behaviour research literature that employed cross-sectional surveys or interviews for data collection from the perspective of findings, limitations and methodological challenges. This research shows that future surveys are needed to bridge the gaps in literature but they would need to encompass a multidisciplinary approach to do so as until now only environmental and engineering factors were considered in these studies. Insights from social practice theories and techniques must be acquired to deploy robust and unbiased questionnaire results, which will provide new, more comprehensive knowledge in the field, and therefore occupant behaviour could be better understood and represented in building performance simulation to support design and operation of low or net-zero energy buildings.

Understanding users' individual differences may provide clues to help identify computer users who are prone to act insecurely. We examine factors that impact home users' reported computer security behaviour. We conducted two online surveys with a total of 650 participants to investigate the relationship between self-reported security behaviour and users' knowledge, motivation, confidence, risk propensity and sex-typed characteristics. We found that all of these factors impacted security behaviour, with knowledge as the most important predictor. We further show that a user's affinity to feminine or masculine characteristics is a better determinant of security behaviour than using binary male/female descriptors. Our study enabled us to confirm earlier results in the literature in a non-organisational setting, and to extend the literature by studying additional factors and by comparing the relative importance of each factor as a predictor of security behaviour. [ABSTRACT FROM AUTHOR]

Psychological frameworks are rarely used to understand irrigation adoption behaviour in developing countries. A Bayesian belief network (BBN) model was developed that integrated socio-economic characteristics and psychological factors to understand farmer behaviours with respect to irrigation practices in four districts of Maharashtra, India. Strong norms, risk perceptions of water scarcity, and attitude play roles in the adoption of irrigation technology and practices. Critically, it was found that no one factor can explain adoption behaviour; rather, an ensemble of factors is needed to understand farmer behaviour. A farmer who is highly educated, middle-aged, and moderately wealthy with a significant level of family help and an open well as their main water source, while receiving low promotional information related to water scarcity and irrigation adoption, is most likely to adopt irrigation technology. The application of the BBN in this study enables stakeholders and policymakers to better understand the linkages between different factors and behaviour. [ABSTRACT FROM AUTHOR]

This article showcases the adaptability of existing mobility devices for the Algerian disabled population. It aims to develop a behavior model of disabled Algerian persons through (1) development of a theoretical model based on literature review and (2) improvement of this model by using local collected data from our developed questionnaire. The majority of existing mobility devices for disabled persons are adapted developed countries context. The Algerian context (people and environment) has its own specifications and needs. Researchers does not address the disabled people behavior modelling in Algeria, due to the unavailability of data and the difficulty of their collection. In order to overcome this problem and develop adapted mobility devices for this particular population, the deployment of a questionnaire to collect local data, becomes necessary. First, a behavior model is deduced from the literature review then a questionnaire is developed and deployed to complete the missing field of this model. The deployment questionnaire panel is composed of 260 persons including users, their families and staff working in disabled person's aid organization. The main result of this paper consist in a specific behavior model of Algerian disabled persons. This population are looking for reliable and smart wheelchair (97%) to be able to overcome their daily mobility difficulties due to the infrastructure and the global economic context. The proposed model can generate adapted wheelchair for Algerian context. The results of the questionnaire are an important step to understand disabilities in Algeria and developing countries. Development of a behavior model for the disabled persons displacement. Development and deployment of questionnaire for disabled persons in Algeria. Review of disabled person situation and existing wheelchair in Algeria. Identification of Algerian disabled persons requirement and demonstrate that are different from European one. Definition of recommendation set for the design of wheelchair adapted to Algerian context. [ABSTRACT FROM AUTHOR]

This work focuses on decision making for automated driving vehicles in interaction rich scenarios like traffic merges in a flexibly assertive yet safe manner. We propose a Q-learning based approach, that takes in active intention inferences as additional inputs besides the directly observed state inputs. The outputs of Q-function are processed to select a decision by a modulation function, which can control how assertively or defensively the agent behaves.

Cyber physical production systems (CPPSs) exemplify the application of cyber physical systems in the manufacturing industry and are the basis for realising Industry 4.0. However, the adaptivity, reconfigurability, and interoperability of CPPSs result in high complexity and uncertainty in their design and implementation. To rectify this problem, this paper proposes a behavioural model-driven modelling method for lifecycle support of CPPSs. To this end, the general framework and operating mechanism of the proposed model are first specified and a meta-modelling language is developed based on object-oriented timed coloured knowledge Petri net. Subsequently, the hierarchical modelling and platform application of the CPPS behaviour is realised, and a laboratory-scale case study that demonstrates the efficacy of the proposed approach is presented. [ABSTRACT FROM AUTHOR]

• In comparison, WPB is less likely to be engaged by individuals than other PEB. • Contextual factors can be considered as pre-conditions to engage in WPB. • Individuals' engagement levels can be increased by structural interventions. Despite being widely recognised as a sustainable way to reduce waste generation, the implementation of prevention policies has been far from successful. Unlike recycling, waste prevention depends on the interplay of different behaviours, motivations, and structural conditions. Increasing people's engagement in waste prevention depends on identifying the direct and indirect impacts on behavioural costs and promoting long-term behavioural change. This study analyses the individuals' behaviour in Campinas to compare, through an integrated method, their engagement level on pro-environmental and waste prevention actions, considering sociodemographic factors and the local context. A questionnaire survey was conducted for 14 months with a final sample of 888 respondents. Among the 20 most difficult actions, 25% were waste-related, and 20% were linked to consumption behaviours. Results also show a significant interference of age and contextual factors in facilitating or hindering pro-environmental and waste prevention actions. House type, for example, can be considered a pre-condition to implement home composting, but it is not decisive. Waste prevention behaviour is composed of mostly private actions hidden from monitoring processes. These findings show that expensive structural improvements can be outperformed by implementing specific educational programs that address social aspects. [ABSTRACT FROM AUTHOR]

The health and economic impacts of infectious diseases such as COVID-19 affect all levels of a community from the individual to the governing bodies. However, the spread of an infectious disease is intricately linked to the behaviour of the people within a community since crowd behaviour affects individual human behaviour, while human behaviour affects infection spread, and infection spread affects human behaviour. Capturing these feedback loops of behaviour and infection is a well-known challenge in infectious disease modelling. Here, we investigate the interface of behavioural science theory and infectious disease modelling to explore behaviour and disease (BaD) transmission models. Specifically, we incorporate a visible protective behaviour into the susceptible-infectious-recovered-susceptible (SIRS) transmission model using the socio-psychological Health Belief Model to motivate behavioural uptake and abandonment. We characterize the mathematical thresholds for BaD emergence in the BaD SIRS model and the feasible steady states. We also explore, under different infectious disease scenarios, the effects of a fully protective behaviour on long-term disease prevalence in a community, and describe how BaD modelling can investigate non-pharmaceutical interventions that target-specific components of the Health Belief Model. This transdisciplinary BaD modelling approach may reduce the health and economic impacts of future epidemics.

Massive open online courses (MOOCs) show highly irregular participation behaviour among users. In this study, using data from Computer Science 50x of HarvardX, we investigated one extreme, yet common strategy to foresee the endgame: taking the final problem set at the beginning of the course. We found such a strategy to be the only dominant trajectory alternative to following the sequence prescribed by the syllabus. Whereas all students who took and passed the final problem set at the beginning of the course subsequently completed the course, those who took and failed the final problem set at the beginning of the course finished the fewest number of milestones, even fewer than those who never attempted the final problem set. Moreover, students with a lower prior programming proficiency were more likely than better prepared students both to take the final problem set early and to fail it. This study revealed the disconcerting phenomenon that many students dropped out of a MOOC because, apparently, their confidence was crushed even before they learned any course content. The study suggests that future MOOC practices and policies should offer informative and constructive syllabi to accommodate students' need for previewing the endgame. [ABSTRACT FROM AUTHOR]

Classical artificial potential approach of motion planning is extended for emulating human driving behaviour in two dimensions. Different stimulus parameters including type of ego‐vehicle, type of obstacles, relative velocity, relative acceleration, and lane offset are used. All the surrounding vehicles are considered to influence drivers' decisions. No emphasis is laid on vehicle control; instead, an ego vehicle is assumed to reach the desired state. The study is on human‐like driving behaviour modelling. The developed motion planning algorithm formulates repulsive and attractive potentials in a data‐driven way in contrast to the classical arbitrary formulation. Interaction between the stimulus parameters is explicitly considered by using multivariate cumulative distribution functions. Comparison of two‐dimensional (lateral and longitudinal) performance indicators with a baseline model and generative adversarial networks indicate the effectiveness and suitability of the developed motion planning algorithm in the mixed traffic environment. [ABSTRACT FROM AUTHOR]

The aim of this paper was to analyse and model the behaviour decision-making of rescue crews in multi-helicopter collaborative search and rescue mission. Firstly, based on decision field theory, a dynamic behavioural decision-making model was put forward considering personal behaviour decision-making preference. Besides, considering physical characteristics, safety requirements and rescue crews' behaviour decision-making, a multi-helicopter collaborative search and rescue behaviour model was established. Then, based on the survey of four general aviation helicopter search and rescue companies, the search and rescue efficiency by teams composed of different decision-making preferences was simulated based on distributed ant colony algorithm in experiments. Results showed that rescue crews with different personal preferences have different behaviour characteristics. Besides, teams composed of mixed preferences are more efficient than teams composed of single preferences, and the most optimal composition way is when the positive type is slightly more than the conservative type and balanced type. [ABSTRACT FROM AUTHOR]

The great number of social network users and the expansion of this kind of tool in the last years demand the storage of a great volume of information regarding user behaviour. In this article, we utilise interaction records from Facebook users and metrics from complex networks study, to identify different user behaviours using clustering techniques. We found three different user profiles regarding interactions performed in the social network: viewer, participant and content producer. Moreover, the groups we found were characterised by the C4.5 decision-tree algorithm. The 'viewer' mainly observes what happens in the network. The 'participant' interacts more often with the content, getting a higher value of closeness centrality. Therefore, users with a participant profile are responsible, for example, for the faster transmission of information in the virtual environment, a crucial function for the Facebook social network. We noted too that 'content producer' users had a greater quantity of publications in their pages, leading to a superior degree of input interactions than the other two profiles. Finally, we also verify that the profiles are not mutually exclusive, that is, the user of a profile can at determined moment perform the behaviour of another profile. [ABSTRACT FROM AUTHOR]

In recent years, more and more researchers have been investigating mathematical knowledge, as well as the cognitive skills that seem to be related to the improvement of mathematical thinking, numerical skills, mathematical logic and problem solving techniques. In this paper, we present the cognitive processes that are related to mathematical performance, such as working memory, anxiety, attention, spatial cognition, executive function and phonological awareness. In addition, we refer to metacognitive skills and their role in controlling and regulating cognitive processes, in order to improve mathematical performance. Finally, we present a new taxonomy of mathematical skills, the pyramid of mathematical cognition, as well as their gradual development through the appropriate cognitive and metacognitive mechanisms. [ABSTRACT FROM AUTHOR]

As human drivers, we instinctively employ our understanding of other road users’ behaviour for enhanced efficiency of our drive and safety of the traffic. In recent years, different aspects of assisted and autonomous driving have gotten a lot of attention from the research and industrial community, including the aspects of behaviour modelling and prediction of future state. In this paper, we address the problem of modelling and predicting agent behaviour and state in a roundabout traffic scenario. We present three ways of modelling traffic in a roundabout based on: (i) the roundabout geometry; (ii) mean path taken by vehicles inside the roundabout; and (iii) a set of reference trajectories traversed by vehicles inside the roundabout. The roundabout models are compared in terms of exit-direction classification and state (i.e., position inside the roundabout) prediction of query vehicles inside the roundabout. The exit-direction classification and state prediction are based on a particle-filter classifier algorithm. The results show that the roundabout model based on set of reference trajectories is better suited for both the exit-direction and state prediction.

State machines are a popular way of modelling the behaviour of systems, including process control systems. However, there are also some problems with their use in this domain, in particular in the sub-domains of the control of continuous or batch processes. The problems originate from the fact that the continuous and batch processes are, in most cases, slow in their reaction; consequently, their control sequences should have a corresponding duration. However, at present, all state machine models are based on instantaneous sequences, with only the loop (or do) sequence having a duration. This paper presents a new state machine behaviour model for procedural control entities in industrial process control systems. The main feature of the new concept of state machine processing is the durability of all action sequences and not only the "do" or "loop" sequence, as is the case in the existing state machine formalisms. The new concept enables modelling of control software for slow continuous or batch industrial processes in a more straightforward manner and, at the same time, on a higher level of abstraction than with using traditional state machine formalisms. The new concept is demonstrated and validated by means of a case study, which addresses a control problem from a real industrial project. The validation demonstrates that the proposed concept has a significant advantage. The concept is applicable to other classes of slow response real time systems as well. [ABSTRACT FROM AUTHOR]

The development of Internet of Things (IoT) and latest Information and Communication Technologies (ICT) have changed the nature of healthcare monitoring and health behaviour intervention in many applications. Water hygiene and water conservation behaviour intervention as important influence factors to human health are gaining much attentions for improving sustained sanitation practice. Based on face-to-face delivery, typical behaviour intervention method is costly and hardly to provide all day access to personalised intervention guidance and feedbacks. In this study, we presented a behavioural information system and water use behaviour model using IoT platform. Using Expanded Theory of Planned Behaviour (ETPB) and adopted structure equation model, this study offers a solution for understanding the behaviour intervention mechanism and methodology for developing empirical model. A case study of behaviour intervention model is presented by utilising residential water conservation behaviour data collected in China. Results suggested that cultural differences have significant influences on the understanding of intervention drivers, promoting projects and increasing awareness, which could improve the behaviour intervention efficiency and further facilitate the improvement of water hygiene practice. The performance evaluation of water saving dimension is discussed as well in the paper. [ABSTRACT FROM AUTHOR]

This paper proposes a method for on the fast fatigue limit estimation for composite materials using a simulation of a self-heating test. The experimental method based on the material self-heating was successfully applied for composite materials and allows to determine a material’s endurance limit in a few hours. In order to increase the potential of this experience, the thermomechanical modelling is proposed with the precise description of the intrinsic energy dissipation. The rise of material’s temperature is linked with development of plastic strains, material damage, viscoelastic proprieties of polymer matrix and intro-crack friction. Therefore, the collaborative behaviour model including the hysteresis loops is used to represent the visco-elastoplastic damage composite behaviour. The collaborative model consists of two sub-models. The first one describes an envelope of the loading curves and insures the computation of elastic and the in-elastic strains as well as the in-ply damage. The second part deals with modelling of hysteresis loops during unloading path using a fractional derivative approach. Just a few parameters are required to represent the hysteresis loops. Using a proposed modelling, the dissipation due to the in-ply damage propagation, the material hardening and the viscoelastic effects can be precisely calculated. The fatigue limit is determined by using thermodynamic simulation the quasi-static cyclic test with the dissipation measurements as an equivalent to self-heating test. The method is validated for composite materials with thermosetting and thermoplastic matrixes. [ABSTRACT FROM AUTHOR]

The monitoring of small houses and rooms has become possible due to the advances in IoT sensors, actuators and low power communication protocols in the last few years. As buildings are one of the biggest energy consuming entities, monitoring them has great interest for trying to avoid non-necessary energy waste. Moreover, human behaviour has been reported as being the main discrepancy source between energy usage simulations and real usage, so the ability to monitor and predict actions as opening windows, using rooms, etc. is gaining attention to develop stronger models which may lead to reduce the overall energy consumption of buildings, considering buildings thermal inertia and additional capabilities. In this paper, a case study is described in which four meeting rooms have been monitored to obtain information about the usage of the rooms and later use it to predict their future usage. The results show the possibility to deploy a simple and non-intrusive sensing system whose output could be used to develop advanced control strategies.

Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method.