Measurement of Slip Velocity and Lift Coefficient for Laterally Focused Particles in an Inertial Flow through a Spiral Microfluidic Channel

Microfluidic channels with a spiral geometry are extensively researched for their use in particle focusing, separation and identification. Instead of using electrophoresis, magnetophoresis, etc., spiral channel takes advantage of the Inertial Lift Force along with the Viscous Drag to achieve size based separation of particles. Inertial microfluidic channel can have high throughput and are much safer to use for live cell separation and other physiological fluids processing. A particle flowing freely in a spiral microchannel at low Reynolds number inertial flow, attains lateral equilibrium due to balance of Inertial Lift force and the viscous Dean Drag. The inertial lift forces are primarily due to the wall effect and the shear gradient of the fluid flow profile. Much theoretical research has been done in this field to explain the lateral migration of a particle in an inertial fluid flow. Notable contributions were made by Saffman (1965), Ho and Leal (1974) and later Vasseur and Cox (1976) in explaining the lift force on a particle theoretically. All these and many other theoretical models developed in the last few decades discuss Lift force being dependent on the particle slip velocity. Additionally many models including the one developed by Saffman predicts a linear dependence of Lift force on the slip velocity of particle. But it seems that the microfluidic community has ignored this dependence with the result that several hypotheses and models exist in which the slip velocity is nonexistent. The measurement of slip velocities for particles has never been done in the field of microfluidics. The current study aims to do so and bridge the gap in understanding the Lift force responsible for the lateral migration of particles. The focused particle’s velocity when it passes through the outer arm of the spiral microfluidic device is measured experimentally followed by a computational study (using COMSOL Multiphysics) to obtain the undisturbed fluid flow velocity through the spiral arm. To calculate the slip velocity, identification of focusing positions in the horizontal and vertical plane of the channel is necessary. Identification in horizontal plane is easy by simply observing the channel under microscope. To identify the vertical focusing positions, a high speed camera (Photron SA-4) coupled with a Nikon microscope and a 50x objective lens (depth of focus = 0.9 um) is used. The narrow depth of focus of objective lens coupled with the precise movement of microfluidic device in the vertical plane is used to identify the height of focused particles from the channel bottom. A focus-measure of all the acquired images is calculated (using a Matlab script which calculates the global variance of an image as a focus-measure) followed by its statistical distribution to obtain the particle’s vertical location within an error of ±5 um. Velocity of the particles for all the focused positions is now calculated using a Matlab script which detects the particles from the acquired images and traces it across successive frames. At the focused position, particle is in equilibrium due to a balance of Dean Drag and the Inertial Lift force. Velocity components of Dean Flow are obtained from the computational study, followed by calculation of Dean Drag acting on the focused particles. The Lift force acting on the particle is now known and equating it with the slip velocity of particles, numerical values of Lift coefficient are obtained for the first time. These Lift coefficients are obtained for various focusing positions in the vertical plane of channel for two sets of Reynolds number

The Effect of Performance Variation on Rater Attributions and Ratings of Job Performance

In most organizations, employee performance is evaluated annually by their supervisors and these evaluations lead to important individual and organizational outcomes. Research has shown that properties of the performance distribution referred to as Gestalt characteristics (e.g., mean, variability) have a significant effect on performance ratings, as well as the attributions that raters make about ratee ability and motivation. This study extends previous research demonstrating the influence of Gestalt characteristics on performance ratings by examining the effect of two operationalizations of variability on performance ratings: tremors (short-term changes) and swells (longer-term changes). One hundred forty-eight participants participated in a 3 (mean: below average, above average, average) × 3 (swells: positive, negative, and none) × 2 (tremors: low, high) × 2 (rater locus of control: internal, external) mixed factorial experiment. Participants evaluated 18 hypothetical salespersons’ performance distributions and made attributions about the salesperson’s locus of causality, ability, and effort. Findings indicated that both tremors and swells had a significant effect on performance ratings, such that performance profiles with a high level of tremors were rated more favorably than profiles with a low level of tremors, and profiles with a positive/negative swell were rated significantly higher/lower than profiles without a swell. As predicted, tremors had a significant effect on rater attributions of effort such that raters attributed higher amounts of effort to performance profiles with a high level of tremors compared to profiles with a low level of tremors. Swells had a significant effect on rater attributions of ability as well as attributions of effort such that raters attributed positive swells to higher levels of ability and effort and negative swells to lower levels of ability and effort. Contrary to expectation, rater locus of control did not moderate the variability performance rating relationship. However, exploratory analyses revealed that rater locus of control moderated the relationship between swells and attributions of locus of causality, such that raters with an internal locus of control tended to attribute swells internally rather than externally. Implications of these findings for performance management are discussed

Data Driven Surrogate Frameworks for Computational Mechanics: Bayesian and Geometric Deep Learning Approaches

In modern applications, high-fidelity computational models are often impractical due to their slow performance and also lack information about the certainty of their predictions. Deep learning techniques have recently emerged as a powerful tool for accelerating such predictions. However, these techniques can be inefficient when confronted with larger and more complex problems. This thesis introduces innovative deep learning surrogate frameworks that are scalable, robust, require minimum hyper-parameter tuning, are fast at the inference stage, and are accurate in forecasting non-linear deformation responses of solid objects. These surrogate frameworks are constructed using various deep learning techniques under deterministic as well as Bayesian settings. Bayesian frameworks enable us to capture uncertainties and provide a means to trust the predictions of the neural network approaches. This thesis introduces a new geometric deep learning framework, called MAgNET (Multi-channel Aggregation Network). MAgNET is designed to handle large-dimensional graph-structured data using an encoder-decoder architecture. MAgNET is built upon the novel MAg (Multichannel Aggregation) operation, which generalises the concept of multi-channel local operations found in convolutional neural networks to arbitrary non-grid inputs. The MAg layers are combined with the novel graph pooling/unpooling operations to form a powerful graph U-Net architecture capable of efficiently performing supervised learning on large-dimensional graph-structured data, like complex meshes. Additionally, the thesis demonstrates the use of state-of-the-art attention-based networks, which have revolutionized various engineering fields but have remained unexplored for their uses in the field of computational mechanics. We demonstrate the efficiency and versatility of the proposed frameworks by applying them to surrogate modeling for non-linear finite element simulations. Our suggested methods, particularly the MAgNET architecture, possess broad applicability, enabling researchers and practitioners to explore novel modeling scenarios and applications. Through the open sharing of the source codes and datasets employed, this thesis not only makes a significant contribution to the field of surrogate modeling in mechanics but also paves the way for numerous research opportunities for their utilisation in various engineering and scientific applications.R-AGR-3325 - H2020-MSCA-ITN-2017-764644-RAINBOW (01/04/2018 - 31/03/2023) - BORDAS Stéphan

Sonographic appearance of testicular adrenal rest tumour in a patient with congenital adrenal hyperplasia

Background: Testicular adrenal rest tumours (TARTs) are benign testicular masses that are found in inadequately treated patients with congenital adrenal hyperplasia (CAH). Recognizing this association and identifying characteristic ultrasound features of TARTs is important so as to avoid misdiagnosing them as malignancies, which can lead to unnecessary interventions. Case Report: We describe a case of a 9-year-old boy, with a diagnosis of CAH and precocious puberty, who was referred to our department for an ultrasound evaluation of the abdomen and scrotum. On ultrasound, there were well-defined, heterogeneous, predominantly hypoechoic, round-to-oval masses in both testes. Taking into account the presence of CAH and a typical sonographic appearance of bilateral testicular masses, a diagnosis of testicular adrenal rest tumour was made; biopsy was deferred and hormonal treatment was modified. Conclusions: Prompt diagnosis of testicular adrenal rest tumours is essential, as it only indicates inadequate hormonal control. Moreover, it can prevent unnecessary biopsies and orchidectomies, and can maintain fertility. TARTs have a typical imaging appearance that every radiologist must be aware of

The Effect of Performance Variation on Rater Attributions and Ratings of Job Performance

In most organizations, employee performance is evaluated annually by their supervisors and these evaluations lead to important individual and organizational outcomes. Research has shown that properties of the performance distribution referred to as Gestalt characteristics (e.g., mean, variability) have a significant effect on performance ratings, as well as the attributions that raters make about ratee ability and motivation. This study extends previous research demonstrating the influence of Gestalt characteristics on performance ratings by examining the effect of two operationalizations of variability on performance ratings: tremors (short-term changes) and swells (longer-term changes). One hundred forty-eight participants participated in a 3 (mean: below average, above average, average) × 3 (swells: positive, negative, and none) × 2 (tremors: low, high) × 2 (rater locus of control: internal, external) mixed factorial experiment. Participants evaluated 18 hypothetical salespersons’ performance distributions and made attributions about the salesperson’s locus of causality, ability, and effort. Findings indicated that both tremors and swells had a significant effect on performance ratings, such that performance profiles with a high level of tremors were rated more favorably than profiles with a low level of tremors, and profiles with a positive/negative swell were rated significantly higher/lower than profiles without a swell. As predicted, tremors had a significant effect on rater attributions of effort such that raters attributed higher amounts of effort to performance profiles with a high level of tremors compared to profiles with a low level of tremors. Swells had a significant effect on rater attributions of ability as well as attributions of effort such that raters attributed positive swells to higher levels of ability and effort and negative swells to lower levels of ability and effort. Contrary to expectation, rater locus of control did not moderate the variability performance rating relationship. However, exploratory analyses revealed that rater locus of control moderated the relationship between swells and attributions of locus of causality, such that raters with an internal locus of control tended to attribute swells internally rather than externally. Implications of these findings for performance management are discussed

On the Role of Constraints in Optimization under Uncertainty

This thesis addresses the problem of industrial real-time process optimization that suffers from the presence of uncertainty. Since a process model is typically used to compute the optimal operating conditions, both plant-model mismatch and process disturbances can result in suboptimal or, worse, infeasible operation. Hence, for practical applications, methodologies that help avoid re-optimization during process operation, at the cost of an acceptable optimality loss, become important. The design and analysis of such approximate solution strategies in real-time optimization (RTO) demand a careful analysis of the components of the necessary conditions of optimality. This thesis analyzes the role of constraints in process optimality in the presence of uncertainty. This analysis is made in two steps. Firstly, a general analysis is developed to quantify the effect of input adaptation on process performance for static RTO problems. In the second part, the general features of input adaptation for dynamic RTO problems are analyzed with focus on the constraints. Accordingly, the thesis is organized in two parts: For static RTO, a joint analysis of the model optimal inputs, the plant optimal inputs and a class of adapted inputs, and For dynamic RTO, an analytical study of the effect of local adaptation of the model optimal inputs. The first part (Chapters 2 and 3) addresses the problem of adapting the inputs to optimize the plant. The investigation takes a constructive viewpoint, but it is limited to static RTO problems modeled as parametric nonlinear programming (pNLP) problems. In this approach, the inputs are not limited to being local adaptation of the model optimal inputs but, instead, they can change significantly to optimize the plant. Hence, one needs to consider the fact that the set of active constraints for the model and the plant can be different. It is proven that, for a wide class of systems, the detection of a change in the active set contributes only negligibly to optimality, as long as the adapted solution remains feasible. More precisely, if η denotes the magnitude of the parametric variations and if the linear independence constraint qualification (LICQ) and strong second-order sufficient condition (SSOSC) hold for the underlying pNLP, the optimality loss due to any feasible input that conserves only the strict nominal active set is of magnitude O(η2), irrespective of whether or not there is a change in the set of active constraints. The implication of this result for a static RTO algorithm is to prioritize the satisfaction of only a core set of constraints, as long as it is possible to meet the feasibility requirements. The second part (Chapters 4 and 5) of the thesis deals with a way of adapting the model optimal inputs in dynamic RTO problems. This adaptation is made along two sets of directions such that one type of adaptation does not affect the nominally active constraints, while the other does. These directions are termed the sensitivity-seeking (SS) and the constraint-seeking (CS) directions, respectively. The SS and CS directions are defined as elements of a fairly general function space of input variations. A mathematical criterion is derived to define SS directions for a general class of optimal control problems involving both path and terminal constraints. According to this criterion, the SS directions turn out to be solutions of linear integral equations that are completely defined by the model optimal solution. The CS directions are then chosen orthogonal to the subspace of SS directions, where orthogonality is defined with respect to a chosen inner product on the space of input variations. It follows that the corresponding subspaces are infinite-dimensional subspaces of the function space of input variations. It is proven that, when uncertainty is modeled in terms of small parametric variations, the aforementioned classification of input adaptation leads to clearly distinguishable cost variations. More precisely, if η denotes the magnitude of the parametric variations, adaptation of the model optimal inputs along SS directions causes a cost variation of magnitude O(η2). On the other hand, the cost variation due to input adaptation along CS directions is of magnitude O(η). Furthermore, a numerical procedure is proposed for computing the SS and CS components of a given input variation. These components are projections of the input variation on the infinite-dimensional subspaces of SS and CS directions. The numerical procedure consists of the following three steps: approximation of the optimal control problem by a pNLP problem, projection of the given direction on the finite-dimensional SS and CS subspaces of the pNLP and, finally, reconstruction of the SS and CS components of the original problem from those of the pNLP

MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations

Mesh-based approaches are fundamental to solving physics-based simulations, however, they require significant computational efforts, especially for highly non-linear problems. Deep learning techniques accelerate physics-based simulations, however, they fail to perform efficiently as the size and complexity of the problem increases. Hence in this work, we propose MAgNET: Multi-channel Aggregation Network, a novel geometric deep learning framework for performing supervised learning on mesh-based graph data. MAgNET is based on the proposed MAg (Multichannel Aggregation) operation which generalises the concept of multi-channel local operations in convolutional neural networks to arbitrary non-grid inputs. MAg can efficiently perform non-linear regression mapping for graph-structured data. MAg layers are interleaved with the proposed novel graph pooling operations to constitute a graph U-Net architecture that is robust, handles arbitrary complex meshes and scales efficiently with the size of the problem. Although not limited to the type of discretisation, we showcase the predictive capabilities of MAgNET for several non-linear finite element simulations

Mavericks at BLP-2023 Task 1: Ensemble-based Approach Using Language Models for Violence Inciting Text Detection

This paper presents our work for the Violence Inciting Text Detection shared task in the First Workshop on Bangla Language Processing. Social media has accelerated the propagation of hate and violence-inciting speech in society. It is essential to develop efficient mechanisms to detect and curb the propagation of such texts. The problem of detecting violence-inciting texts is further exacerbated in low-resource settings due to sparse research and less data. The data provided in the shared task consists of texts in the Bangla language, where each example is classified into one of the three categories defined based on the types of violence-inciting texts. We try and evaluate several BERT-based models, and then use an ensemble of the models as our final submission. Our submission is ranked 10th in the final leaderboard of the shared task with a macro F1 score of 0.737.Comment: 6 pages, 1 figure, accepted at the BLP Workshop, EMNLP 202

Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics

Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains a significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures for efficient learning of highly non-linear deformations of solid bodies. The first two architectures are based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown promising performance for learning on mesh-based data. The third architecture is Perceiver IO, a very recent architecture that belongs to the family of attention-based neural networks--a class that has revolutionised diverse engineering fields and is still unexplored in computational mechanics. We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies