Kinetic theory approach to the study of a curved shock-wave

Kinetic theory approach to curved shock wave with low Reynolds numbe

Exploring the Invisible Pain of Workplace Ostracism: Its Outcomes & Coping Mechanism

Workplace ostracism is a form of silent mistreatment where an individual experiences deliberate denied social interaction with others. It is believed to be a universal phenomenon which silently injures the human heart. Ostracism is believed to be very destructive as the ostracized person finds it difficult to prove that he is being ostracized due to its covert nature. It is a proven fact that the nature of mistreatment under ostracism can be silent but its outcomes in the form of decreased job engagement, diminished work performance, increased intention to quit, etc. are very vocal. The relationship between workplace ostracism and its negative consequences is explained in the light of Conservation of Resources theory. According to conservation of resources theory, every individual is attached to various kinds of resources which they perceive as worthy. The theory claimed that when an individual encounters resource loss or is threatened of potential loss, such experiences disturbs his psychological well-being. This study also describes the coping mechanism which an individual can adopt in order to manage the negative aspects of workplace ostracism. The organizational administrators need to focus on this silent form of harassment, which is making the organization weak on a broader scope. Thus, a serious need of encouraging healthy communication, cooperation and coordination is required in every organization in order to avoid counter-productive work behaviors

Delirium in patients with hip fractures

This thesis is designed to improve understanding, recognition, prevention and management of delirium in hospitalised older patients with hip fracture. Delirium is a preventable neuropsychiatric disorder characterized with acute confusion, disorientation, global cognitive deficit and is multifactorial. Delirium is considered one of the most common post-operative complication in patients with hip fracture. Research suggests that one third of delirium episodes are preventable if the identified factors can be addressed. Early recognition is also considered a key aspect of delirium prevention and management. This thesis focuses on development and implementation of the intervention bundle to reduce incidence of delirium through improved delirium recognition. The systematic review of the literature was performed to investigate the effect of multicomponent interventions on incidence of delirium. Focus groups with multidisciplinary clinicians from orthopaedic were performed to understand their perceptions in relation to recognition, diagnosis and management of delirium. The barriers identified in the focus groups and the best practice evidence identified in the systematic review was used to develop the intervention bundle to improve delirium recognition and care. This thesis demonstrated that a well-considered intervention bundle only had a mixed impact on decreasing incidence of delirium. This project also highlighted the significance of aligning clinical service improvement goals with the wider goals of the organisation. We have formed international research collaboration (Australia, Europe and United States) based on this project. We are exploring the concept of machine learning for preoperative prediction of postoperative delirium in patients with hip fractures

Delirium in patients with hip fractures

This thesis is designed to improve understanding, recognition, prevention and management of delirium in hospitalised older patients with hip fracture. Delirium is a preventable neuropsychiatric disorder characterized with acute confusion, disorientation, global cognitive deficit and is multifactorial. Delirium is considered one of the most common post-operative complication in patients with hip fracture. Research suggests that one third of delirium episodes are preventable if the identified factors can be addressed. Early recognition is also considered a key aspect of delirium prevention and management. This thesis focuses on development and implementation of the intervention bundle to reduce incidence of delirium through improved delirium recognition. The systematic review of the literature was performed to investigate the effect of multicomponent interventions on incidence of delirium. Focus groups with multidisciplinary clinicians from orthopaedic were performed to understand their perceptions in relation to recognition, diagnosis and management of delirium. The barriers identified in the focus groups and the best practice evidence identified in the systematic review was used to develop the intervention bundle to improve delirium recognition and care. This thesis demonstrated that a well-considered intervention bundle only had a mixed impact on decreasing incidence of delirium. This project also highlighted the significance of aligning clinical service improvement goals with the wider goals of the organisation. We have formed international research collaboration (Australia, Europe and United States) based on this project. We are exploring the concept of machine learning for preoperative prediction of postoperative delirium in patients with hip fractures

Design Methodology of Very Large Scale Integration

Very Large Scale Integration (VLSI) deals with systems complexity rather than transistor size or circuit performance. VLSI design methodology is supported by Computer Aided Design (CAD) and Design Automation (DA) tools, which help VLSI designers to implement more complex and guaranteed designs. The increasing growth in VLSI complexity dictates a hierarchical design approach and the need for hardware DA tools. This paper discusses the generalized Design Procedure for CAD circuit design; the commercial CADs offered by CALMA and the Caesar System, supported by the Berkeley design tools. A complete design of a Content Addressable Memory (CAM) cell, using the Caesar system, supported by Berkeley CAD tools, is illustrated

Recovery of cellular traction in three-dimensional nonlinear hyperelastic matrices

The traction exerted by a cell on the extra-cellular matrix (ECM) is critical to understanding and manipulating important biological processes such as stem cell differentiation, cancer cell metastasis, and embryonic morphogenesis. This traction is typically quantified through traction force microscopy (TFM). In TFM, the displacement of select markers inside the ECM is tracked, and is used in conjunction with an elasticity problem to reconstruct the traction field. Most applications of this technique thus far have assumed that the matrix behaves as a linear elastic solid that undergoes small deformation and infinitesimal strains. In this manuscript, we develop and implement a robust and efficient TFM methodology that overcomes these limitations by accounting for geometric and material nonlinearities in the ECM. We pose the TFM problem as an inverse problem and develop efficient adjoint-based minimization techniques to solve it. We test the effect of measurement noise on the proposed method, and examine the error incurred by not including nonlinear effects when solving the TFM problem. We present these results for in-silico traction fields that are applied to realistic geometric models of microglial and neuronal cells

A few-shot graph Laplacian-based approach for improving the accuracy of low-fidelity data

Low-fidelity data is typically inexpensive to generate but inaccurate. On the other hand, high-fidelity data is accurate but expensive to obtain. Multi-fidelity methods use a small set of high-fidelity data to enhance the accuracy of a large set of low-fidelity data. In the approach described in this paper, this is accomplished by constructing a graph Laplacian using the low-fidelity data and computing its low-lying spectrum. This spectrum is then used to cluster the data and identify points that are closest to the centroids of the clusters. High-fidelity data is then acquired for these key points. Thereafter, a transformation that maps every low-fidelity data point to its bi-fidelity counterpart is determined by minimizing the discrepancy between the bi- and high-fidelity data at the key points, and to preserve the underlying structure of the low-fidelity data distribution. The latter objective is achieved by relying, once again, on the spectral properties of the graph Laplacian. This method is applied to a problem in solid mechanics and another in aerodynamics. In both cases, this methods uses a small fraction of high-fidelity data to significantly improve the accuracy of a large set of low-fidelity data