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Various aspects related to comfort are described in this special issue. Some papers are focused on the environment, like smell, temperature, light, acoustics, space and some on an artefact touching the human, like the floor angle, the seat, a bed and light. However, in this special issue also topics like modelling and behavior get more attention. [ABSTRACT FROM AUTHOR]

BACKGROUND: Athletic fatigue is an inescapable issue in competitive sports. It belongs to a physiological response that is triggered when competitive athletes are trained to a critical point. OBJECTIVE: The study aims to explore the relationships involving boxers' social support, mental fatigue, coach leadership behaviors and psychological resilience. METHODS: 1050 boxers were selected in several provinces across China and investigated on the basis of the Social Support Questionnaire for Athletes, Mental Fatigue Scale, Psychological Resilience Scale, and Leadership Scale for Sport. RESULTS: Boxers' social support was negatively correlated with mental fatigue and psychological resilience, while it was positively correlated with coach leadership behaviors. Apart from direct effects on mental fatigue, other impacts are imposed by boxers' social support via mediating effects such as coach leadership behaviors and psychological resilience. The total effect value was -0.18, the direct effect value was -0.08, and the indirect effect value was -0.12; furthermore, coach leadership behaviors and psychological resilience play a mediating role, accounting for 65.57% of the total. CONCLUSION: In order to alleviate the stress from intense competitive training and abate mental fatigue, competitive athletes may be encouraged in subsequent training to seek all-sided social support for social interpersonal relationships. While clarifying the mechanism how the external environment affects individuals, this paper explains the principle of social support on athletes' psychological fatigue and identifies mutual influences between coaches and athletes. [ABSTRACT FROM AUTHOR]

In view of the defects and shortcomings of the traditional target detection and tracking algorithm in accurately detecting targets and targets in different scenarios, based on the current research status and technical level of target detection and tracking at home and abroad, this paper proposes a target detection algorithm and tracking method using neural network algorithm, and applies it to the athlete training model. Based on the Alex-Net network structure, this paper designs a three-layer convolutional layer and two layers of fully connected layers. The last layer is used as the input of the SVM classifier, and the target classification result is obtained by the SVM classifier. In addition, this article adds SPP-Layer between the convolutional layer and the fully connected layer, enabling the same dimension of the Feature Map to be obtained before the fully connected layer for different sized input images. The research results show that the proposed method has certain recognition effect and can be applied to athlete training. [ABSTRACT FROM AUTHOR]

For the acquisition of user behavior preference in social network, usually a data mining will be conducted on the nearest neighborhood users or latestprojects based on the user's historical behavior data, so as to find similar behaviorrelationship for quantitative analysis; it can also focus on the awareness on the user-related context information based on cognitive psychology, so as to find its internal links forthe potential mining. However, these methods ignore the intrinsic link between the browsing behavior and the preferred topics in the user link connection, resulting in the limited precision and accuracy of the preference acquisition. Inspired by the theory of complex network link prediction and the topic model, anacquisition method for users' browsing behavior preference was proposed in this paper. In the multi-dimensional network link environment, by measuring the importance of the node via network centrality and search the social network link via setting the similarity threshold, the real-time multi-link information and the big data about users' browsing under each link were acquired, then the data were filteredand cleaned by using adjustable parameters. On this basis, according to the least squares criterion the data underwent a fusion and were used to construct a data node distribution model for user browsing behavior, then the frequent feature items of user browsing behavior preference were extracted. Based on the extracted feature terms, the variational Bayes approximation reasoning method was used to construct the preference topic model. Finally, the hierarchical VSM model representation method was used to establish the preference acquisition model of user browsing behavior, and the model was updated in real time by user feedback processing mechanism. The experimental results on the real data set showed that the link search method and the preference topic model provided by this paper are accurate and efficient. Compared with the classical cooperative filtering method and the context-awareness method, the precision, accuracy and effectiveness of the preference acquisition model provided this paper are significantly improved, and its adaptability has been significantly strengthened. [ABSTRACT FROM AUTHOR]

Violence detection is a challenging task in the computer vision domain. Violence detection framework depends upon the detection of crowd behaviour changes. Violence erupts due to disagreement of an idea, injustice or severe disagreement. The aim of any country is to maintain law and order and peace in the area. Violence detection thus becomes an important task for authorities to maintain peace. Traditional methods have existed for violence detection which are heavily dependent upon hand crafted features. The world is now transitioning in to Artificial Intelligence based techniques. Automatic feature extraction and its classification from images and videos is the new norm in surveillance domain. Deep learning platform has provided us the platter on which non-linear features can be extracted, self-learnt and classified as per the appropriate tool. One such tool is the Convolutional Neural Networks, also known as ConvNets, which has the ability to automatically extract features and classify them in to their respective domain. Till date there is no survey of deciphering violence behaviour techniques using ConvNets. We hope that this survey becomes an exclusive baseline for future violence detection and analysis in the deep learning domain. [ABSTRACT FROM AUTHOR]

In this second part of a two part paper, we establish a local, uniform energy-approximation estimate for the solutions to a simplified model of thin inhomogeneities with open mid-curves. This local result plays a crucial role in the proof of the global, uniform approximation results established in the first part of this paper (Asymptotic Analysis (2019)). For more details about the model we also refer the reader to (Chinese Annals of Mathematics, Series B38 (2017) 293–344). [ABSTRACT FROM AUTHOR]

As a communication tool, social networks have become an important part of people's daily lives. The proliferation of data in the big data era has spurred waves of research. Twitter is one of the most popular sites among all social network platforms. Twitter API allows researchers to easily study user behaviour and influence. In this paper, we use data obtained through the Twitter API to study the influence maximization problem based on the relationship graph of the social network and information dissemination model. In the current independent cascade propagation model, the influence weights between nodes are based largely on the fixed-value assumption and the randomized probability of acceptance; however, this does not conform to real life. The influence weight between users is closely related to the strength of relationships, content of communication, and so on. Focusing on user relation-ships, we introduce an improved weighted cascade mode combined with a heuristic algorithm to find an approximate solution to the problem of influence maximization. Example analysis indicates that the improved weighted cascade model can obtain a more significant and influential node set compared to conventional methods. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) gaming (VRG) captures, examines, analysis, and renders the game data virtually instead of the users' devices over efficient networks. E.g., VR is crucial in visually guided surgery and manifold therapies. Video Quality Assessment (VQA) helps VRG software and devices execute high-quality inputs/outputs. This paper debates VRG processes, environments, VQA metrics, Quality of Experience (QoE), platforms, current methods, tools, use in medicine, existing operational gains/challenges, and highlights future directions. [ABSTRACT FROM AUTHOR]

Modern methods of monitoring help cow farmers save significantly monitoring time and improve cow health care efficiency. Behavioral changes when cows are sick may include increased or decreased daily activities such as increased lying or decreased walking time. Accelerometer advantages are low power consumption, small size, and lightweight. Thus, accelerometers have been widely used to monitor cow behavior. A cow monitoring system usually includes a central processor for receiving and processing information according to a behavioral classification algorithm through the cows' movements. This paper introduces an effective classification system for Southern Yellow cow behavior using three degrees of freedom (3-DoF) accelerometers. The proposed classifier applied GBDT algorithm (16 seconds window) with five features, offers the good performance while investigating with four Southern Yellow cattle. The classification achievement was assessed and compared to existing ones regarding sensitivity, accuracy, and positive predictive value. [ABSTRACT FROM AUTHOR]

Healthcare research involves handling personal health information. Information security policies are implemented in research institutions to ensure data subjects’ rights but are not always respected due to researchers’ neglect or unawareness. This paper is part of an action research project at Saint Joseph University in Lebanon aiming to increase researchers’ compliance with the university’s information security policy. An anonymous online questionnaire was administered to medical students to evaluate their knowledge and behavior regarding patient data handling in research projects. 38 responses were collected. Results show that most students collect patient data for research, and are frequently not aware of, and do not comply with, the existing information security policy. We also found correlations between low knowledge and non-compliant behaviors including clicking on links from unknown senders, leaving computers unattended, and sharing data insecurely. To address these issues, we plan to implement various Information Security Awareness interventions and compare their effectiveness. [ABSTRACT FROM AUTHOR]

This paper presents the main methodological issues met with during the research on adolescent everyday life information behavior in the context of making informed decisions, which was carried out in Croatia from 2018 to 2020. It does not focus on the research results, but rather on the methodological issues and concerns that had occurred stemming from the specific life period of the population being investigated, and on the ways the researchers delt with them. They are offered as potentially useful for consideration to those who will investigate adolescent information behavior in other contexts. Six main methodological dilemmas arose while planning the design of research methodology coming out from six questions: what would be the appropriate research approach and design to investigate adolescent information behavior in the context of making informed everyday life decisions, what would be the developmentally appropriate way to investigate the phenomena, how to gain access to the potential adolescent research participants, how to negotiate an adult researcher's position when interacting with adolescents, how to get adolescent participants' trust needed to collect rich data and what ethical issues need to be considered. We offer an overview of the dilemmas and concerns, as well as the ways they were dealt with, acknowledging the contribution of those scholars who had responded to similar challenges in the past. [ABSTRACT FROM AUTHOR]

In this paper, we study the following generalized quasilinear Schrödinger equation − div (ε 2 g 2 (u) ∇ u) + ε 2 g (u) g ′ (u) | ∇ u | 2 + V (x) u = K (x) | u | p − 2 u + | u | 22 ∗ − 2 u , x ∈ R N , where N ⩾ 3 , ε > 0 , 4 < p < 22 ∗ , g ∈ C 1 (R , R +) , V ∈ C (R N) ∩ L ∞ (R N) has a positive global minimum, and K ∈ C (R N) ∩ L ∞ (R N) has a positive global maximum. By using a change of variable, we obtain the existence and concentration behavior of ground state solutions for this problem with critical growth, and establish a phenomenon of exponential decay. Moreover, by Ljusternik–Schnirelmann theory, we also prove the existence of multiple solutions. [ABSTRACT FROM AUTHOR]

Today’s ubiquitous presence of sensors provides a large amount of data which can be analyzed to study human behavior. The last few years saw the birth and diffusion of a new class of sensing systems: smartphones. With a diverse range of embedded sensors, smartphones have now become a commodity, and their capabilities can be leveraged to collect data to be used in different domains, including study of human behavior. This paper presents a review of past research works where mobile phone sensors are used to detect various aspects characterizing human behavior. Methods for automatic recognition of the placement of a mobile phone are first described as useful tools to improve the accuracy of sensing systems. Activity detection, at different abstraction levels from basic body motions to high-level activities, is then surveyed extensively, including studies focusing on detection of transportation mode and characterization of health-related activities such as physical exercise and sleeping. Other related works reviewed in this paper are continuous sensing systems for lifelogging applications, techniques to identify the environment where a user is located, and behavior modeling methods that allow extracting common patterns from behavioral data, studying psychological profiles and predicting future behaviors. [ABSTRACT FROM AUTHOR]

In the early phase of Requirements Engineering (RE), Goal-Oriented Requirements Engineering (GORE) has been found to be a valuable tool. GORE plays a vital role in requirements analysis such as alternative selection decision-making process. This is carried out to determine the practicability and effectiveness of alternative approaches to arriving at quality goals. Most GORE models handle alternative selection based on an extremely coarse-grained qualitative approach, making it impossible to distinguish two alternatives. Many proposals are based on quantitative alternative choices, yet they do not offer a clear decision-making judgement. We propose a fuzzy-based quantitative approach to perform goal analysis using inter-actor dependencies in the i * framework, thereby addressing the ambiguity problems that arise in qualitative analysis. The goal analysis in the i * framework was performed by propagating the impact and weight values throughout the entire hierarchy of an actor. In this article, the Analytic Hierarchy Process (AHP) is adapted with GORE to discuss the evaluation of alternative strategies of the i * goal model of interdependent actors. By using a quantitative requirement prioritisation method such as the AHP, weights of importance are assigned to softgoals to obtain a multi-objective optimised function. The proposed hybrid method measures the degree of contribution of alternatives to the fulfillment of top softgoals. The integration of AHP with goal anlaysis helps to measure alternative options against each other based on the requirements problem. This approach also includes the sensitivity analysis, which helps to check the system behaviour for change in input parameter. Hence, it facilitates decision-making for the benefit of the requirements' analyst. To explain the proposed solution, this paper considers a telemedicine system case study from the existing literature. [ABSTRACT FROM AUTHOR]

Asymptotic approximations of voltage potentials in the presence of diametrically small inhomogeneities are well studied. In particular it is known that one may construct approximations that are accurate to any order (in the diameter) uniformly in the conductivity of the inhomogeneity. The corresponding problem for thin inhomogeneities is not so well understood, in particular as concerns uniformity of the approximations. If the conductivity degenerates to 0 or goes to infinity as the width of the inhomogeneity goes to zero, the voltage potential may converge to different limiting solutions, and so the construction of uniform approximations is not straightforward. For the case of thin two dimensional inhomogeneities with closed mid-curves such approximations were constructed and rigorously verified in (Chinese Annals of Mathematics, Series B38 (2017) 293–344). The analysis relied heavily on the regularity of the approximate solutions. In this two part paper we continue this line of research, by showing that the same approximations remain valid, even when the mid-curve is open, and the corresponding approximate solutions have singularities at the endpoints of the curve. [ABSTRACT FROM AUTHOR]

Ambient Assisted Living (AAL) systems are increasingly being deployed in real-world environments and for long periods of time. This significantly challenges current approaches that require substantial setup investment and cannot account for frequent, unpredictable changes in human behaviours, health conditions, and sensor deployments. The state-of-the-art methodology in studying human activity recognition is cultivated from short-term lab or testbed experimentation, i.e., relying on well-annotated sensor data and assuming no change in activity models. This paper propose a technique, EMILEA, to evolve an activity model over time with new types of activities. This technique novelly integrates two recent advances in continual learning: Net2Net – expanding the architecture of a model while transferring the knowledge from the previous model to the new model and Gradient Episodic Memory – controlling the update on the model parameters to maintain the performance on recognising previously learnt activities. This technique has been evaluated on two real-world, third-party, datasets and demonstrated promising results on enhancing the learning capacity to accommodate new activities that are incrementally introduced to the model while not compromising the accuracy on old activities. [ABSTRACT FROM AUTHOR]

Voice User Interfaces have become popular with the advent of Alexa, Google Home, Cortana and other commercial speech recognition interfaces; however, the privacy of the end users is compromised while using these interfaces in public. In addition, users can feel a bit awkward while using these interfaces with loud voice while they are outside their homes. Contextually, 'Hypnosis' and 'Hypnotherapy' have not been frequently applied as a way of human communications although the principle of suggestion induced behavior changes in an interesting approach to interact with machines. In this paper, GEORGIE a prototype AI was used to achieve a novel means of interaction inspired from the principles of hypnotherapy, which is a discrete interface ensuring that end-users' privacy is not compromised. It is envisaged that people who prefer secret communication and interaction might love to use this hypnotic computer interface (hypCI). The hypCI would be the novel means of human robot interface (HRI) or human computer interface (HCI). [ABSTRACT FROM AUTHOR]

Interface delamination plays an important role on the failure of multi-layer parts, influences of interface properties and coating thickness on interface fracture of coating/substrate systems attract great attention. In this paper, interface fracture behaviors of ceramic coating/substrate systems under three-point bending were modelled based on finite element simulations with interface cohesive elements. Influences of coating thickness, interfacial strength and interfacial fracture toughness on interface crack evolution were studied. The simulations indicate that interface crack initiates earlier for the thicker coating systems or the systems with the smaller interface fracture toughness, and the influence of interface strength is more complicated. The simulation results are in agreement with the experimental results and instructive to understand interface failure behaviors of coating/substrate systems deeply. [ABSTRACT FROM AUTHOR]

Question-and-answering (Q&A) sites are information systems that allow users to ask and answer questions. Users can learn by frequently discussing, answering questions, or exchanging opinions with other experts using Q&A systems. In addition, they can arrange the existing top answers using a number of upvotes and downvotes from experts and crowd wisdom. The number of knowledge-sharing sites has increased significantly in recent years. However, some Q&A sites began to shrink (Yahoo Answers) or were shut down (Google Answers). The main reason is low-quality answers because they do not connect visitors and experts with the right questions. In addition, a question may contain several subtopics with which the expert is unfamiliar. The recommendation of a list of experts closest to the question will lead to a long-tail problem. In this paper, we propose an expert group recommendation method for Q&A systems by taking into consideration users' behaviors and diversity criteria in the group. Users' behavior is analyzed to determine a group of experts or non-experts on specific topics. Diversity is an important factor in promoting the sustained comprehensible growth of Q&A sites and avoid following the crowd. Experiments on a Quora dataset show that our method achieves better results in terms of accuracy in comparison with other methods. [ABSTRACT FROM AUTHOR]

BACKGROUND: The theory of Structural Cognitive Modifiability and Mediated Learning Experience of Reuven Feuerstein states that individuals with brain impairment, because of congenital or acquired origin, may substantially and structurally improve their cognitive functioning, by a systematic intervention based on a specific, criteria-based type of interaction ("mediated learning"). Three application systems are based on it: a dynamic-interactive assessment of learning capacity and processes of learning, the LPAD (Learning Propensity Assessment Device); a cognitive intervention program called "Instrumental Enrichment Program", which trains cognitive, metacognitive and executive functions; and a program, which is oriented atworking in context, Shaping Modifying Environments. These programs have been applied in widely different target groups: from children and young adults with learning and developmental disabilities, at risk of school failure, or having failed at school, because of socio-economic disadvantage or congenital neurological impairment; disadvantaged youngsters and adults in vocational training, to elderly people at the beginning of a dementia process. Experience with cognitive rehabilitation of children and adults with acquired brain damage, has been relatively recent, first in the Feuerstein Institute's Brain Injury Unit in Jerusalem, later in other centers in different parts of the world; therefore scientific data are scarce. OBJECTIVES: The purpose of this paper is to examine how the Feuerstein-approach fits into the goals and proposed approaches of cognitive rehabilitation, and to explore its relevance for assessment and intervention in individuals with congenital or acquired brain damage. METHODS: The methodology of the Feuerstein approach consists of four pillars: dynamic assessment, cognitive activation, mediated learning and shaping a modifying environment. The criteria of mediated learning experience are explained with specific reference to people with acquired brain injury. The procedure of learning propensity assessment device uses visuospatial and verbal tasks known from neuropsychological assessment (such as Rey's complex figure drawing), as well as a in a pre-test - brief intervention - post-test format. Cognitive activation is done in various ways: a paper-and-pencil relatively content-free program called "instrumental enrichment", with transfer of learned principles into daily life situations, followed by metacognitive feedback. Four case histories of acquired brain damage are analyzed: a 19 year old man with extensive postastrocytoma frontotemporal brain lesions; a 19 year old man with bilateral frontal and right temporal and parieto-occipital parenchymatous destruction after a traumatic brain injury; a 24 year old man with hemispherectomy for intractable epilepsy because of Sturge-Weber syndrome; and a 30-year old man with left porencephalic cyst after cerebral hemorrhage. RESULTS: Structural cognitive improvement could be demonstrated in positive change scores in visuo-spatial memory, associative and verbal memory, abstract thinking, and organizing tasks, even more than 10 years post-TBI. In some cases a rise in IQ has been documented. Improvement in daily life functioning and academic skills (re)learning has also been seen. CONCLUSIONS: Though impossible to claim scientific evidence, the case histories nevertheless suggest the importance of interactive assessment in designing intervention programs which have sufficient intensity, frequency, duration and consistency of mediation; furthermore, an essential ingredient is the ecological approach which requires working with the patient and the whole network around; a firm "belief system" or that modifiability is possible even with severe brain damage and many years after the injury; a cognitive, metacognitive and executive approach, and a quality of interaction according to criteria of mediated learning. They suggest that Feuerstein approach may offer interesting perspectives to cognitive rehabilitation. More extensive research is needed to provide a broader scientific evidence base. [ABSTRACT FROM AUTHOR]

An isolated network with Wind Turbine implemented by Double Fed Induction Generator (WTDFIG) that maintaining different electrical variables in safe ranges following a severe transient condition is always a serious challenge. This paper proposes a control scheme based on Auto-Tuning Fuzzy PI (ATFPI) concept for the Rotor Side Converter (RSC) of the DFIG by online tuning of the output scaling factor of the ATFPI for enhancement the transient behavior of the system under various condition of operation. The scheme includes a coordinator unit which is elaborated to effectively regulate the frequency and active power of the system. The performance of the proposed ATFPI controller is evaluated in a typical network in Matlab Simulink environment. Obtained results show that the control can successfully meet all functions drawn in the scope and easily put behind the conventional competitors like conventional PI and/or fuzzy PI controllers. [ABSTRACT FROM AUTHOR]

In this paper, one imidazole macrocyclic divalent cation with flexible configuration was chosen with the aim of the immobilization of its conformation. One novel organic-inorganic hybrid supramolecule {[syn/anti-did] (CuS3C3N3) (C2H3N)} (1), [did2+ = (12z, 52z)–11H, 51H–1, 5(1, 3)-diimidazol-3-iuma-3, 7(1, 2)-dibenzenacyclooctaphane-13, 53-diium] has been synthesized through the self-assembly reaction of did2+ with CuSCN under solvothermal reaction. This compound has been unambiguously confirmed by PXRD, IR, TG and X-ray single crystal diffraction. Crystallographic analysis shows that the anion of compound 1 is binuclear structure. The stability of compound 1 is relatively good. [ABSTRACT FROM AUTHOR]

BACKGROUND: There is a trend in higher education towards the use of digital or electronic portfolios (ePortfolios) to collect evidence that demonstrates learning and skill development. There are very few papers that examine the key features and what to include in an occupational therapy ePortfolio. OBJECTIVE: This study presents an approach to developing a graduate entry ePortfolio to prepare occupational therapy students for transition to work. METHODS: An e-Delphi approach was used to gather the opinions of eight categories of experts. Three rounds of questionnaires were used to explore the purpose, terminology, content, structure, and development phases of a graduate entry ePortfolio. RESULTS: Key stakeholders indicated that the purpose of a professional portfolio is to create a professional profile, record experiences, skills and behaviours, and promote recording of lifelong learning and achievements. Delphi expert panel members emphasised the importance of personal choice in selecting evidence recorded in each collection, which must be guided by ethical decision-making. CONCLUSIONS: The findings of the study are important to students who wish to build an ePortfolio to organise and display evidence of competence prior to graduation in readiness to enter the workforce. The recommendations also will be of value to occupational therapy educators in curriculum development. [ABSTRACT FROM AUTHOR]

High influential users are playing an important role in promoting information propagation in social media. Thus, it has been a very interesting problem to identify influential users in social media, and attracted numerous researchers. A great deal of research work has been devoted to solving this problem. However, the existing methods mainly focus on the network topology, ignoring users' behaviors. In this paper, we propose an High Influential Users Detection (HIUD) algorithm by analyzing users' behaviors. To evaluate the performance of our algorithm, we carry out extensive experiments on Sina and Tencent microblogging data sets, and compare it with other methods. The experimental results have shown that the HIUD achieves the best performance. Furthermore, we also make a spatial analysis on those high influential users with thermodynamic map. [ABSTRACT FROM AUTHOR]

In this paper, we present some new results in the form of inequalities connecting transitivity of the given fuzzy preference relation with its consistent behaviour. The additive and multiplicative generators of t-norms and t-conorms play the key role in establishing these results. [ABSTRACT FROM AUTHOR]

The paper proposes a new set of improved Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by using the avoidance from the worst behaviour. The avoidance behaviour, which consist in introducing personal/local or global worst terms, are applied to the attractor's formula. The enhanced QPSO algorithms are targeted towards benchmark electromagnetic optimization problems, and their efficiency is tested and tuned on Loney's solenoid. [ABSTRACT FROM AUTHOR]

Background: The goal of this research is to use smart home technology to assist people who are recovering from injuries or coping with disabilities to live independently., Objective: We introduce an algorithm to model and forecast wake and sleep behaviors that are exhibited by the participant. Furthermore, we propose that sleep behavior is impacted by and can be modeled from wake behavior, and vice versa., Methods: This paper describes the Behavior Forecasting (BF) algorithm. BF consists of 1) defining numeric values that reflect sleep and wake behavior, 2) forecasting wake and sleep values from past behavior, 3) analyzing the effect of wake behavior on sleep and vice versa, and 4) improving prediction performance by using both wake and sleep scores., Results: The BF method was evaluated with data collected from 20 smart homes. We found that regardless of the forecasting method utilized, wake behavior and sleep behavior can be modeled with a minimum accuracy of 84%. Additionally, normalizing the wake and sleep scores drastically improves the accuracy to 99%., Conclusions: The results show that we can effectively model wake and sleep behaviors in a smart environment. Furthermore, wake behaviors can be predicted from sleep behaviors and vice versa.

Neuropsychiatric symptoms (NPS) are increasingly recognized as a core element of Alzheimer's disease (AD); however, clinicians still consider AD primarily as a cognitive disorder. We describe a case in which the underrecognition of NPS as part of AD resulted in substantial delay of an AD diagnosis, a wrong psychiatric diagnosis, and the organization of inappropriate care. The aim of this paper is to acknowledge NPS as an (early) manifestation of AD and to suggest features that may point toward underlying AD in older adults with late-life behavioral changes. [ABSTRACT FROM AUTHOR]

This paper explores individual's role in the innovation process of firms by determining staff members' openness to engage in cooperative behaviors with their peers in the Chinese context, and subsequently, the impact of those interactions on the perceptions of trust among colleagues, and how those links influence the innovation capability of firms. Also, we size the extent to which employees' years of experience can moderate those relations. Questionnaire responses derived from employees in Chinese technology-driven firms were used to perform the statistical analysis and test the hypotheses drawn in this study. The individual's engagement in cooperative behaviors was proven to be rather unbiased among colleagues. Likewise, those dynamics led to positive perceptions of trust among them and added to the innovation capability of firms while an individual's years of experience didn't influence those dynamics. The present article examined two knowledge-sharing approaches used to ease cooperative behaviors among staff members. However, it is also necessary to examine the influence of other approaches in order to generate more precise comparisons of the frameworks that can yield better results. It is suggested that Chinese firms adopt a more reflexive learning approach that would facilitate individual's engagement in cooperative behavior that lead to innovation. These results can be useful for managers in industries such as software-engineering and product-design which depend heavily on the effective transmission of knowledge among staff. [ABSTRACT FROM AUTHOR]

Much experimental evidence suggests that age-related brain pathologies are most often mediated by reactive oxygen species primarily originating from mitochondria (mROS). Two papers with such evidence have been recently published in the Journal of Alzheimer's Disease (Stefanova et al., J Alzheimers Dis 21, 476-491, 2010; Lloret et al., J Alzheimers Dis, doi: 10.3233/JAD-2011-110890). In the first paper, it was shown that appearance of a typical behavioral trait of aging in rats (that old animals do not enter an open arm in a maze) was completely reversed by ten weeks treatment of the old rats with the mitochondria-targeted antioxidant SkQ1. In the second article, the authors identified molecular mechanisms by which amyloid-β-induced mROS can mediate hyperphosphorylation of the tau protein, a key event in Alzheimer's disease. Conventional antioxidants prevented such hyperphosphorylation. In this article, I will summarize the present state of the art in this field. I conclude that mitochondria-targeted rechargeable antioxidants are promising as tools to treat brain pathologies developing in elderly humans. [ABSTRACT FROM AUTHOR]

This paper investigates the technological issues that affect the behavior of the users of an e-Learning system. In particular, it examines how user-based quality evaluation is influenced by the way the content of an e-Learning system is structured, as well as by aesthetic issues such as colors used. The paper presents a case study, in which a representative sample of users evaluated two different implementations of an e-learning system, developed particularly for the purposes of this survey. The users' evaluation focuses on three specific factors: the content structure, the navigation and the aesthetics of the system. The main aim of this paper is to determine these factors' effect on e-learning systems users' behavior by gathering and analyzing the opinions of the users who participated in this case study. [ABSTRACT FROM AUTHOR]

This paper introduces an approach to behavioral pattern identification as a part of a study of temporal patterns in complex dynamical systems. Rough set theory introduced by Zdzisław Pawlak during the early 1980s provides the foundation for the construction of classifiers relative to what are known as temporal pattern tables. It is quite remarkable that temporal patterns can be treated as features that make it possible to approximate complex concepts. This article introduces what are known as behavior graphs. Temporal concepts approximated by approximate reasoning schemes become nodes in behavioral graphs. In addition, we discuss some rough set tools for perception modeling that are developed for a system for modeling networks of classifiers. Such networks make it possible to recognize behavioral patterns of objects changing over time. They are constructed using an ontology of concepts delivered by experts that engage in approximate reasoning about concepts embedded in such an ontology. We also present a method that we call a method for on-line elimination of non-relevant parts (ENP). This method was developed for on-line elimination of complex object parts that are irrelevant for identifying a given behavioral pattern. The article includes results of experiments that have been performed on data from a vehicular traffic simulator useful in the identification of behavioral patterns by drivers. [ABSTRACT FROM AUTHOR]

In this paper we present an implementation-based comparison of Measurement-based Admission Control algorithms. Through the use of a special purpose environment, a performance and behaviour comparison is made. The results of this paper illustrate the independence of traffic from admission control behaviour in the homogeneous traffic environment. While illustrating the impact the admission decision will make upon heterogenous traffic systems. These results highlight the importance of estimators being robust to statistical variation in measurements and offering calibrated controls. While based upon a comparison of measurement-based admission control algorithms - these conclusions are applicable to any application of measurement-based estimation. [ABSTRACT FROM AUTHOR]

Background: It is well established that environmental factors can have impact upon an injured person's recovery and return-to-work outcomes. To date, there has been no cohesive model to provide theoretical understanding of the way in which these divergent factors combine to create disability behaviours., Object: Development of a conceptual model for understanding the development of disability behavior., Methods: Interpolation from existing neuroplasticity theory to observed behaviors and studies of behavior in the workers' compensation environment, including existing research concerning predictors for disability., Results: The paper describes a conceptual model for understanding instances of disability that are not necessarily attributable to physical harm. Preliminary testing provides support for the model., Conclusions: Factors that contribute to the formation of a neural network supporting the behavior of learned disability are described. From that description, intervention methods to prevent or resolve so-called "needless disability" are discussed.

Videomodeling is an effective technological tool for intervention and rehabilitation of children with autism spectrum disorders in different contexts. The purpose of this paper is to verify whether a videomodeling intervention is effective for the acquisition of social and emotional behaviour and skills of children with ASD using tablet PC. One 6-year-old child with ASD, who does not use verbal communication, was involved in a multiple baseline across behaviours design: the independent variable was the videomodeling intervention, instead the dependent variable was the participant behaviour. Intervention was preceded by a pre-session of Functional Communication training. An intersubject analysis was used to evaluate participant percentage of response through the sessions. The participant showed a marked improvement in his emotional skills. The intersubjective analysis carried out shows the sharp increase in response in many target behaviours from the baseline to the intervention phase. A limitation of the study was the lack of time: there were only two inversion sessions, no follow-up evaluation, no generalization and no double-blind study. It would be interesting for future research to extend it to everyday life scenarios, to enhance generalization in other contexts, involving a larger number of people with ASD. [ABSTRACT FROM AUTHOR]

This paper proposes Cognitive Chrono-Ethnography (CCE), a new study methodology for understanding people's in situ behavior selections in daily life. People select their next behavior to maximize their satisfaction for a given behavioral needs. They appropriately coordinate available cognitive resources to make the best decisions by using their knowledge of past experiences and by processing input from the environment and individual intrinsic state. When a study field is specified, CCE starts by defining critical parameters for understanding people's behavior by considering the nature of behavior selection processes in the field in question, and then designing ethnographical field observations by taking into account the fact that their results will be described in terms of the specified critical parameters. The participants' behavior is recorded, followed by a series of structured retrospective interviews for the purpose of describing their present behavior and obtaining their history of behavioral development. Analysis of the interview results aid in developing models of present behavior selections and their chronological changes in the past. These models serve as defining future needs of persons who follow the same developing paths with a certain amount of delay, e.g., a few years of delay. This paper describes a CCE study of spectators of professional baseball games at a ballpark who have become frequent visitors to a baseball stadium in 5 years.

The introduction of Adaptive Cruise Control (ACC) could be very helpful for making the longitudinal driving task more comfortable for the drivers and, as a consequence, it could have a global beneficial effect on road safety. However, before or during the usage of the device, due to several reasons, drivers might generate in their mind incomplete or flawed mental representations about the fundamental operation principles of ACC; hence, the resulting usage of the device might be improper, negatively affecting the human-machine interaction and cooperation and, in some cases, leading to negative behavioural adaptations to the system that might neutralise the desirable positive effects on road safety. Within this context, this paper will introduce the methodology which has been developed in order to analyse in detail the topic and foresee, in the future, adequate actions for the recovery of inaccurate mental representations of the system.

The advent and adoption of internet-based social networking has significantly altered our daily lives. The educational community has taken notice of the positive aspects of social networking such as creation of blogs and to support groups of system designers going through the same challenges and difficulties. This paper introduces a social networking framework for collaborative education, design and modeling of the next generation of smarter products and services. Human behaviour modeling in social networking application aims to ensure that human considerations for learners and designers have a prominent place in the integrated design and development of sustainable, smarter products throughout the total system lifecycle. Social networks blend self-directed learning and prescribed, existing information. The self-directed element creates interest within a learner and the ability to access existing information facilitates its transfer, and eventual retention of knowledge acquired.

Determination of the cognitive and behavioural demands of work is an important part of holistic workplace intervention. Attention to these factors is especially important when developing return-to-work programs for persons with reduced cognitive, behavioural or psycho-emotional capacity, and when designing risk management programs in organizations. Occupational therapists have the background knowledge and skills to assess these components of work, but often lack valid and reliable measurement tools. This paper reports on three field studies that assessed the reliability and validity of ratings made by novice users of the City of Toronto Job Demands Analysis, which includes a measure of cognitive and behavioural work demands. Numerous challenges to accuracy and reliability that are common to empirical measurement were disclosed, including the necessity for clear and strong definitions, and the importance of thorough rater training. Implications for therapist training and mentorship are discussed.

Several research fields have described phenomena that produce endless sequences of samples, referred to as data streams. These phenomena are studied using data clustering models continuously obtained throughout the endless data gathering process, whose set of dynamical properties, i.e., behavior, evolves over time. In order to cope with data streams characteristics, researchers have developed clustering techniques with low time-complexity requirements. However, pre-defined and static parameters (thresholds, number of clusters and learning rates) commonly found in current techniques still limit the application of clustering to data streams. These limitations to adapt clustering process to behavior changes motivated this paper to propose an on-line and adaptive approach to detect changes and modify parameters. The proposed approach is based on the traditional k-means algorithm to update cluster prototypes and the statistical model of Markov chains to represent behavior. Behavior changes are detected by testing the isomorphism of Markov chains over time under the grounds of Dynamical Systems Theory. The results have confirmed the advantages of the approach when compared with current techniques. [ABSTRACT FROM AUTHOR]

For agent-based modelling of collective phenomena, more and more agent models are employed that go beyond simple reactive behaviour. Such less trivial individual behaviours can be modelled either from an agent-internal perspective, in the form of direct (causal) temporal relations between internal states of the agent, or from an agent-external, behavioural perspective, in the form of more complex input-output relations for the agent. Illustrated by a case study on collective decision making, this paper addresses how the two types of agent models can be related to each other. First an internal agent model for collective decision making is presented, based on neurological principles. It is shown how by an automated systematic transformation from an internal agent model an abstracted behavioural model can be obtained, by abstracting from the internal states. The abstraction approach introduced includes specific methods for abstraction of internal loops, as often occur in neurologically inspired internal agent models, for example to be able to model mutual interaction between cognitive and affective states. As an example of a given behavioural agent model, an existing behavioural agent model for collective decision making incorporating principles on social diffusion is described. It is shown under which conditions and how by an interpretation mapping the obtained abstracted behavioural agent model can be related to this existing behavioural agent model for collective decision making. [ABSTRACT FROM AUTHOR]

New methods based on local spatio-temporal features have exhibited significant performance in action recognition. In these methods, feature selection plays an important role to achieve a superior performance. Actions are represented by local spatio-temporal features extracted from action videos. Action representations are then classified by applying a classifier (such as k-nearest neighbor or SVM). In this paper, we have proposed two feature weighting methods to better discriminate similar actions. We have proposed a definition of feature discrimination power to be used in the feature selection process. Our proposed weighting schemes have greatly improved the final categorization accuracy on the well-known KTH and Weizmann datasets. [ABSTRACT FROM AUTHOR]

Assistive software becomes more and more important part of our everyday life. As it is not straightforward to create such a system, the engineering of assistive systems is a topic of current research with different applications in healthcare, education and industry. In this paper we introduce three contributions to this field of research. Whereas most assistive systems use approaches for intention recognition based on training data applicable to specific environments and applications, we introduce a training-free approach. We do that by showing that it is possible to generate probabilistic inference systems from causal models for human behavior. Additionally, we collect a list of requirements for context aware assistive software and human behavior modeling for intention recognition and showed that our system satisfies them. We then introduce a software architecture for assistive systems that provides support for this kind of modeling. In addition to introducing the modeling approach and the architecture we show in an experimental way that our approach is suited for smart environments. The collected list of requirements could help a software engineer create a robust and easily adaptable to changes in the environment assistive software. [ABSTRACT FROM AUTHOR]

This paper presents the results of an ergonomic intervention conducted within a blast furnace plant. As part of its risk prevention program, the company decided to set up an action plan, in a participatory manner, by setting up working groups to solve health & safety issues. This field mission involved 230 employees, 80 of whom participated actively by being incorporated into working groups. After four months of intervention, a questionnaire survey has been conducted among employees to study the effects of participation on the safety climate. The results seem promising and show that the benefits of participation are numerous: a more positive safety climate associated to safer attitudes and behaviors. However, rather than just participation, it seems to be the employee involvement in the working groups and the satisfaction they derive from their participation that guarantee these positive results. Hence, participatory ergonomics seems to be an effective way to decrease the number of unsafe behaviors at work, provided that the type of participation has been previously well defined and organized according to the specific context of each organization. [ABSTRACT FROM AUTHOR]

To endow virtual agents with more believable affective behavior, it is important to provide them not only the capability to generate and regulate their own emotions, but also the ability to reason about the emotion regulation processes of the agents around them. As a step in this direction, this paper introduces a computational model for a Theory of Emotion Regulation (ToER), which combines the notions of Theory of Mind (ToM) and emotion regulation. This model has been implemented and tested using the modeling language LEADSTO, and found to show the expected behaviour. In addition, a virtual environment application has been developed, which is inhabited by agents that are equipped with the model for ToER. A first evaluation indicates that the model indeed enables the virtual agents to show more realistic affective behavior. [ABSTRACT FROM AUTHOR]

BACKGROUND: Vocational training centers improve outcomes for people with intellectual and developmental disabilities (IDD), but employment rates remain low. Additionally, vocational direct support professionals (DSPs) often lack the training to implement effective instruction and the administrative procedures needed for change. OBJECTIVE: This study examines the effect of behavioral skills training (BST) on the maintained procedural fidelity of evidence-based practice (EBP) instruction in direct support providers with clients who have moderate to extensive support needs (M-ESN). METHOD: Three instructor-identified EBPs for teaching individuals with IDD were selected for professional development (PD): most-to-least prompting, time delay procedures, and system of least prompts. Three single case multiple probe across behaviors experiments evaluated the efficacy of the researcher-delivered BST instruction model on trainee accuracy as assessed by trained center administrators. RESULTS: A functional relation between the BST instruction and generalized DSP implementation was indicated, despite challenges and limitations related to COVID-19. Social validity measures found the goals, procedures, and outcomes were socially significant, and the intervention was practical and effective. CONCLUSION: Practices in vocational training should reflect the high standards for systematic instruction in the K-12 setting. EPB BST training with administration progress monitoring can improve DSPs' instruction with clients, thus improving learning outcomes for adults with M-ESN. [ABSTRACT FROM AUTHOR]