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WRAP: Warwick Research Archive Portal: No conditions. Results ordered -Date Deposited.

Model-based control system design is a well-established method for advanced engine control systems. These control systems maintain engine operation at levels that meet stringent environmental regulations on vehicular emissions. However, the models required for model-based design need to be accurate enough for design and pre-calibration and fast enough for optimization and implementation purposes. On the other hand, the variable valve timing (VVT) technology significantly affects the dynamic performance of internal combustion engines (ICEs). This study aims at developing a control-oriented extended mean-value model (EMVM) of a gasoline engine, taking into account the effects of VVT on the dynamic model. The developed model analyzes the engine performance characteristics in transient and steady-state regimes. The engine model incorporates four peripheral, nonlinear, dynamic subsystems: manifold, fuel injection, wall-film adhesion, and evaporation processes. Moreover, lying at the core of the developed model is a nonlinear, static, in-cylinder process (ICP) model which simulates gas exchange and combustion processes based on the cylinder boundary conditions. Based on the experimental data obtained from the engine test setup, an artificial neural network (ANN) has been trained to predict the ICPs as a single model. The ICP model was integrated into the dynamic peripheral models to form the final EMVM. The results of the developed model were compared to the engine experimental tests for two test scenarios: half-throttle and full-throttle cases. It was observed that the developed model could accurately simulate the engine speed, inlet air pressure, aspirated air mass, and exhaust temperature. Moreover, the EMVM could successfully predict the effects of VVT on the performance of ICEs.

Background: Multimorbidity or multiple long-term conditions (MLTCs), the coexistence of two or more chronic conditions within an individual, presents a growing concern for healthcare systems and individuals' well-being. However, we know little about the experiences of those living with MLTCs in low- and middle-income countries (LMICs) such as India. We explore how people living with MLTCs describe their illness, their engagements with healthcare services, and challenges they face within primary care settings in Kerala, India. Methods: We designed a qualitative descriptive study and conducted in-depth, semi-structured interviews with 31 people (16 males and 15 females) from family health centres (FHCs) in Kerala. Interview data were recorded, transcribed, and thematic analysis using the Framework Method was undertaken. Findings: Two main themes and three sub-themes each were identified; (1) Illness impacts on life (a)physical issues (b) psychological difficulties (c) challenges of self-management and (2) Care-coordination maze (a)fragmentation and poor continuity of care (b) medication management; an uphill battle and (c) primary care falling short. All participants reported physical and psychological challenges associated with their MLTCs. Younger participants reported difficulties in their professional lives, while older participants found household activities challenging. Emotional struggles encompassed feelings of hopelessness and fear rooted in concerns about chronic illness and physical limitations. Older participants, adhering to Kerala's familial support norms, often found themselves emotionally distressed by the notion of burdening their children. Challenges in self-management, such as dietary restrictions, medication adherence, and physical activity engagement, were common. The study highlighted difficulties in coordinating care, primarily related to traveling to multiple healthcare facilities, and patients' perceptions of FHCs as fit for diabetes and hypertension management rather than their multiple conditions. Additionally, participants struggled to manage the task of remembering and consistently taking multiple medications, which was compounded by confusion and memory-related issues. Conclusion: This study offers an in-depth view of the experiences of individuals living with MLTCs from Kerala, India. It emphasizes the need for tailored and patient-centred approaches that enhance continuity and coordination of care to manage complex MLTCs in India and similar LMICs.

Here, we view the mental lexicon as a semantic network where words are connected if they are semantically related. Steyvers and Tenenbaum (Cognitive Science, 29, 41–78, 2005) proposed that the growth of semantic networks follows preferential attachment, the observation that new nodes are more likely to connect to preexisting nodes that are more well connected (i.e., the rich get richer). If this is the case, well-connected known words should be better at acquiring new links than poorly connected words. We tested this prediction in three paired-associate learning (PAL) experiments in which participants memorized arbitrary cue–response word pairs. We manipulated the semantic connectivity of the cue words, indexed by the words' free associative degree centrality. Experiment 1 is a reanalysis of the PAL data from Qiu and Johns (Psychonomic Bulletin & Review, 27, 114–121, 2020), in which young adults remembered 40 cue–response word pairs (e.g., nature–chain) and completed a cued recall task. Experiment 2 is a preregistered replication of Qiu and Johns. Experiment 3 addressed some limitations in Qiu and Johns's design by using pseudowords as the response items (e.g., boot–arruity). The three experiments converged to show that cue words of higher degree centrality facilitated the recall/recognition of the response items, providing support for the notion that better-connected words have a greater ability to acquire new links (i.e., the rich do get richer). Importantly, while degree centrality consistently accounted for significant portions of variance in PAL accuracy, other psycholinguistic variables (e.g., concreteness, contextual diversity) did not, suggesting that degree centrality is a distinct variable that affects the ease of verbal associative learning.

Today, employing model based design approach in powertrain development is being paid more attention. Precise, meanwhile fast to run models are required for applying model based techniques in powertrain control design and engine calibration. In this paper, an in-cylinder process model of a CVVT gasoline engine is developed to be employed in extended mean valve control oriented model and also model based calibration procedure. In-cylinder models are static thermos-fluid models, which predict the performance and emission index of engines based on boundary conditions of cylinder. Due to computations burden of thermos-fluid models, they are not fast enough to be used in control models. In this paper a validated thermodynamic model of engine is developed using a commercial engine analyzing software. The developed model is employed for generation input-output data sets which are used for training an artificial multi-layer neural network. In order to increase the richness of data, the Sobol method is employed to generate input data to thermodynamic model. Based on output trend, the output data are divided to two clusters and two corresponding distinct neural networks are employed. In order to validate the modeling performance the neural network results are compared to experimental results in both full and part load conditions. Comparison of neural network results with experimental results shows that the developed model is able to predict the engine emission and performance indices with required accuracy and fast enough in both full-load and part-load conditions and might be employed in extended mean value models as well as model based engine calibration with required performance.

This chapter focuses on the theoretical basis for career development work. It sets out a case for an integrative cultural learning theory of career development. The distinctive basis of this theoretical perspective is explained, and the five facets of cultural learning theory are described, namely: learning relationships, learning contents, learning processes, learning contexts, and personal myth. In order to inform career development work, these facets are combined in the form of a cultural learning alliance. The formation and agreement of the alliance are described in detail in relation to the initial, middle, and end phases of interactions. Further practical innovations include seven techniques for supporting client learning, including a cultural influences collage, career management styles card sort, and golden threads activity. Implications for the training and development of practitioners are discussed in relation to reflexivity and assessment.



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