Explore the vast range of titles available.

Visual search for a target is faster when the spatial layout of distractors is repeatedly encountered, illustrating that statistical learning of contextual invariances facilitates attentional guidance (contextual cueing; Chun & Jiang, 1998 , Cognitive Psychology, 36, 28–71). While contextual learning is usually relatively efficient, relocating the target to an unexpected location (within an otherwise unchanged search layout) typically abolishes contextual cueing and the benefits deriving from invariant contexts recover only slowly with extensive training (Zellin et al., 2014 , Psychonomic Bulletin & Review, 21 (4), 1073–1079). However, a recent study by Peterson et al. ( 2022 , Attention, Perception, & Psychophysics, 84 (2), 474–489) in fact reported rather strong adaptation of spatial contextual memories following target position changes, thus contrasting with prior work. Peterson et al. argued that previous studies may have been underpowered to detect a reliable recovery of contextual cueing after the change. However, their experiments also used a specific display design that frequently presented the targets at the same locations, which might reduce the predictability of the contextual cues thereby facilitating its flexible relearning (irrespective of statistical power). The current study was a (high-powered) replication of Peterson et al., taking into account both statistical power and target overlap in context-memory adaptation. We found reliable contextual cueing for the initial target location irrespective of whether the targets shared their location across multiple displays, or not. However, contextual adaptation following a target relocation event occurred only when target locations were shared. This suggests that cue predictability modulates contextual adaptation, over and above a possible (yet negligible) influence of statistical power.

In the last decade, deep learning has enjoyed its spotlight as the game-changing addition to smart farming and precision agriculture. Such development has been predominantly observed in developed countries, while on the other hand, in developing countries most farmers especially ones with smallholder farms have not enjoyed such wide and deep adoption of this new technologies. In this paper we attempt to improve the image classification part of smart farming and precision agriculture. Agricultural commodities tend to possess certain textural details on their surfaces which we attempt to exploit. In this work, we propose a deep learning based approach called Selective Context Adaptation Network (SCANet). SCANet performs feature enhancement strategy by leveraging level-wise information and employing context selection mechanism. In exploiting contextual correlation feature of the crop images our proposed approach demonstrates the effectiveness of the context selection mechanism. Our proposed scheme achieves 88.72% accuracy and outperforms the existing approaches. Our model is evaluated on the cocoa bean dataset constructed from the real cocoa bean industry scene in Indonesia.

The purpose of this paper is to explore context adaptation of change processes in the setting of public administration. This is done by adopting a contingency approach. Thus, the research question guiding this study is: How is context adaptation enacted in change processes and what are the behavioural implications? Drawing on empirical findings from the Swedish Transport Administration, the study illustrates the enactment of context adaptation and its behavioural implications through the phases of change. The analysis identifies key contextual factors and contingency variables, demonstrating that context adaptation is enacted by managers and employees. Notably, certain context adaptation led to contradictory behavioural patterns. This study hence contributes to public administration literature by exploring the enactment of context adaption in change processes which represents an understudied phenomenon in this field.

Background Junior doctors lack confidence and competence in handling the critically ill patient including diagnostic skills, decision-making and team working with other health care professionals. Simulation-based training on managing emergency situations can have substantial effects on satisfaction and learning. However, there are indications of problems when applying learned skills to practice. Our aim was to identify first-year doctors’ perceptions, reflections and experiences on transfer of skills to a clinical setting after simulation-based training in handling critically ill patients. Methods We used a qualitative approach and conducted semi-structured telephone interviews with a sample of twenty first-year doctors six months after a 4-day simulation-based training course in handling critically ill patients. Interviews were transcribed verbatim. A content-analysis approach was used to analyse the data. Results The following main themes were identified from the interviews: preparedness for clinical practice, organisational readiness, use of algorithms, communication, teamwork, situational awareness and decision making. The doctors gave several examples of simulation-based training increasing their preparedness for clinical practice and handling the critically ill patient. The usefulness of algorithms and the appreciation of non-technical skills were highlighted and found to be helpful in managing clinical difficulties. Concern was expressed related to staff willingness and preparedness in using these tools. Conclusions Overall, the simulation-based training seemed to facilitate the transition from being a medical student to become a junior doctor. The doctors experienced an ability to transfer the use of algorithms and non-technical skills trained in the simulated environment to the clinical environment. However, the application of these skills was more difficult if these skills were unfamiliar to the surrounding clinical staff. Trial registration Not applicable.

Over the past years, the impact of surveillance systems on public safety increases dramatically. One significant challenge in this domain is person re-identification, which aims to detect whether a person has already been captured by another camera in the surveillance network or not. Most of the work that has been conducted on person re-identification problem uses a single dataset, in which the training and test data are coming from the same source. However, as we have shown in this work, there is a strong bias among the person re-identification datasets, therefore, a method that has been trained and optimized on a specific person re-identification dataset may not generalize well and perform successfully on the other datasets. This is a problem for many real-world applications, since it is not feasible to collect and annotate sufficient amount of data from the target application to train or fine-tune a deep convolutional neural network model. Taking this issue into account, in this work, we have focused on cross-dataset person re-identification problem and first explored and analyzed in detail the use of the state-of-the-art deep convolutional neural network architectures, namely AlexNet, VGGNet, GoogLeNet, ResNet, and DenseNet that have been developed for generic image classification task. These deep CNN models have been adapted to the person re-identification domain by fine-tuning them for each human body part separately, as well as on the entire body, with the two relatively large person re-identification datasets: CUHK03 and Market-1501. Then, the performance of each adapted model has been evaluated on two different publicly available datasets: VIPeR and PRID2011. We have shown that, even just a domain adaptation leads comparable results to the state-of-the-art cross-dataset approaches. Another point that we have addressed in this paper is context adaptation. It has been known that person re-identification approaches implicitly utilizes background as context information. Therefore, to have a consistent background across different camera views, we have employed the cycle-consistent generative adversarial network. We have shown that this further improves the performance.

We investigate the impact of market-supporting institutions on business strategies by analyzing the entry strategies of foreign investors entering emerging economies. We apply and advance the institution-based view of strategy by integrating it with resource-based considerations. In particular, we show how resource-seeking strategies are pursued using different entry modes in different institutional contexts. Alternative modes of entry--greenfield, acquisition, and joint venture (JV)--allow firms to overcome different kinds of market inefficiencies related to both characteristics of the resources and to the institutional context. In a weaker institutional framework, JVs are used to access many resources, but in a stronger institutional framework, JVs become less important while acquisitions can play a more important role in accessing resources that are intangible and organizationally embedded. Combining survey and archival data from four emerging economies, India, Vietnam, South Africa, and Egypt, we provide empirical support for our hypotheses.

Context adaptation (CA) based on evolutionary algorithms is certainly a promising approach to the development of fuzzy rule-based systems (FRBSs). In CA, a context-free model is instantiated to a context-adapted FRBS so as to increase accuracy. A typical requirement in CA is that the context-adapted system maintains the same interpretability as the context-free model, a challenging constraint given that accuracy and interpretability are often conflicting objectives. Furthermore, interpretability is difficult to quantify because of its very nature of being a qualitative concept. In this paper, we first introduce a novel index based on fuzzy ordering relations in order to provide a measure of interpretability. Then, we use the proposed index and the mean square error as goals of a multi-objective evolutionary algorithm aimed at generating a set of Pareto-optimum context-adapted Mamdani-type FRBSs with different trade-offs between accuracy and interpretability. CA is obtained through the use of specifically designed operators that adjust the universe of the input and output variables, and modify the core, the support and the shape of fuzzy sets characterizing the partitions of these universes. Finally, we show results obtained by using our approach on synthetic and real data sets.

Elderly patients discharged from hospital experience fragmented care, repeated and lengthy emergency department (ED) visits, relapse into their earlier condition, and rapid cognitive and functional decline. The Acute Care for Elders (ACE) program at Mount Sinai Hospital in Toronto, Canada uses innovative strategies, such as transition coaches, to improve the care transition experiences of frail elderly patients. The ACE program reduced the lengths of hospital stay and readmission for elderly patients, increased patient satisfaction, and saved the health care system over Can $4.2 million (US $2.6 million) in 2014. In 2016, a context-adapted ACE program was implemented at one hospital in the Centre intégré de santé et de services sociaux de Chaudière-Appalaches (CISSS-CA) with a focus on improving transitions between hospitals and the community. The quality improvement project used an intervention strategy based on iterative user-centered design prototyping and a \"Wiki-suite\" (free web-based database containing evidence-based knowledge tools) to engage multiple stakeholders. The objectives of this study are to (1) implement a context-adapted CISSS-CA ACE program in four hospitals in the CISSS-CA and measure its impact on patient-, caregiver-, clinical-, and hospital-level outcomes; (2) identify underlying mechanisms by which our context-adapted CISSS-CA ACE program improves care transitions for the elderly; and (3) identify underlying mechanisms by which the Wiki-suite contributes to context-adaptation and local uptake of knowledge tools. Objective 1 will involve staggered implementation of the context-adapted CISSS-CA ACE program across the four CISSS-CA sites and interrupted time series to measure the impact on hospital-, patient-, and caregiver-level outcomes. Objectives 2 and 3 will involve a parallel mixed-methods process evaluation study to understand the mechanisms by which our context-adapted CISSS-CA ACE program improves care transitions for the elderly and by which our Wiki-suite contributes to adaptation, implementation, and scaling up of geriatric knowledge tools. Data collection started in January 2019. As of January 2020, we enrolled 1635 patients and 529 caregivers from the four participating hospitals. Data collection is projected to be completed in January 2022. Data analysis has not yet begun. Results are expected to be published in 2022. Expected results will be presented to different key internal stakeholders to better support the effort and resources deployed in the transition of seniors. Through key interventions focused on seniors, we are expecting to increase patient satisfaction and quality of care and reduce readmission and ED revisit. This study will provide evidence on effective knowledge translation strategies to adapt best practices to the local context in the transition of care for elderly people. The knowledge generated through this project will support future scale-up of the ACE program and our wiki methodology in other settings in Canada. ClinicalTrials.gov NCT04093245; https://clinicaltrials.gov/ct2/show/NCT04093245. DERR1-10.2196/17363.

When the traditional \"one size fits all\" approach is used in designing educational games, the game context is usually arranged in a fixed sequence. However, the designated content may not effectively support the diversity of players. The player's ability and characteristics should be considered and supported with an appropriate learning context embedded in the game to facilitate personalised experiences. Adapting game scenarios to a player's characteristics can boost motivation and ultimately improve learning outcomes. This research applies a context-aware design approach and the Learner-Centered Design approach to establish a personalised adaptation framework for designing educational serious games and enhancing personalised knowledge delivery. The proposed framework decouples the game logic implementation and adaptation mechanism. It dynamically adapts the designed game objects and activities to personal learning objectives, learning levels and learning progress to achieve a non-linear learning sequence. Through synchronous real-time xAPI message exchange mechanisms, system components and learning content adaptation are enabled. The adaptation aims to fit personal learning objectives and provide a non-linear learning sequence in a game environment. The framework provides students with personalised learning experiences. A game named GhostCoder is implemented and used to evaluate the framework. Based on the externalised adaptive mechanism, the game content is adapted to the player's performance by adjusting the difficulty of the learning content within the game. Testing of the game in the lab environment has been performed. At the next stage, an evaluation will be conducted with the target groups of students.