Validation of a Computer Code for Use in the Mechanical Design of Spallation Neutron Targets

The present work concentrates on comparing a numerical code and a closed-form analytic solution for determining transient stress waves generated by an impinging, high-intensity proton pulse onto a perfectly elastic solid cylindrical target. The comparison of the two methods serves both to benchmark the physics and numerical methods of the codes, and to verify them against analytic expressions that can be established for calculating the response of the target for simple cases of loading and geometry. Additionally, the comparison elucidated the effects of approximations used in the computation of the analytic results. Two load cases have been investigated: (1) an instantaneously uniform thermal loading along the central core, and (2) a ramped and uniform thermal load applied along the central core. In addition, the influence of the approximations applied to the accurate analytic forms has been elucidated. By validating these analytical results, the closed-form solution may be confidently used to "bound" the solution prior to initiating more detailed and comprehensive numerical studies

Capturing the Benefits of Worker Specialization: Effectsof Managerial and Organizational Task Experience

Learning by doing is a fundamental driver of productivity among knowledge workers. As workers accumulate experience working on certain types of tasks (i.e., they become specialized), they also develop proficiency in executing these tasks. However, previous research suggests that organizations may struggle to leverage the knowledge workers accrue through specialization because specialized workers tend to lose interest and reduce effort during task execution. This study investigates how organizations can improve specialized workers’ performance by mitigating the dysfunctional effects of specialization. In particular, we study how other sources of task experiences from the worker’s immediate manager as well as the organization itself help manage the relationship between worker specialization and performance. We do so by analyzing a proprietary dataset that comprises of 39,162 software service tasks that 310 employees in a Fortune 100 organization executed under the supervision of 92 managers. Results suggest that the manager role experience (i.e., the manager’s experience supervising workers) is instrumental in mitigating the potential negative effect of worker specialization on performance, measured as task execution time. Such influence, however, is contingent on cases in which organizational task experience (i.e., the organization’s experience in executing tasks of the same substantive content as the focal task) is limited. Taken together, our research contributes to multiple streams of research and unearths important insights on how multiple sources of experience beyond the workers themselves can help capture the elusive benefits of worker specialization

Innate Immune Mechanism of Platelet-Neutrophil Aggregation Dependent Vaso-occlusion in Sickle Cell Disease

Sickle Cell Disease (SCD) is an autosomal-recessive–genetic-disorder that affects 100,000 in the U.S. and millions worldwide. Sickle cell anemia, the most common form of SCD results from a single nucleotide polymorphism in the β-globin gene that causes the hemoglobin to polymerize under deoxygenated conditions. Hemoglobin polymerization leads to sickling of erythrocytes, exposure of adhesion molecules on the erythrocyte membrane, and hemolysis. Hemolysis releases erythrocyte derived danger-associated molecular pattern molecules (DAMPs) that activate leukocytes, platelets and endothelium and enable interactions with sickle erythrocytes and promote vaso-occlusion (VOC). VOC is the predominant pathophysiology responsible for acute systemic vaso-occlusive crisis, the leading cause of emergency medical care among SCD patients. VOC is also believed to contribute to progression of other morbidities such as pulmonary hypertension, stroke, and acute chest syndrome, however, the cellular, molecular and biophysical mechanisms that enable VOC in SCD patients remain incompletely understood. To determine the mechanisms that promote VOC in SCD patients, we developed quantitative microfluidic fluorescence microscopy (qMFM), a novel fluorescence imaging approach that utilizes PDMS-based microfluidic devices to visualize single-cell interactions in SCD human blood. Using qMFM, neutrophils were observed to roll, arrest and capture freely flowing platelets leading to formation of large platelet-neutrophil aggregates that occluded microfluidic flow channels. Quantitative analysis revealed that platelet-neutrophil interactions in SCD patient blood were not only more numerous but also significantly longer in duration than those in control blood. These platelet-neutrophil interactions were enabled by platelet P-selectin and GPIbα binding to neutrophil PSGL-1 and Mac-1, respectively and were abolished following blockade of these interactions. qMFM revealed for the first time that platelets in SCD blood form P-selectin expressing “hair-like” membrane tethers that promote platelet-neutrophil interactions by shielding the bonds from the hydrodynamic shear forces of blood. Hair-like tethers act like a ‘lasso’ that allows circulating platelets to interact more efficiently with neutrophils within the vasculature. Inhibition of platelet TLR4 or NLRP3 inflammasome dependent signaling abolished “hair-like” platelet tethers and attenuated platelet-neutrophil aggregation in SCD human blood. This study highlights the potential of therapeutic inhibition of platelet P-selectin or NLRP3 inflammasome pathway in preventing VOC in SCD patients

Visualization of Endothelial Actin Cytoskeleton in the Mouse Retina

Angiogenesis requires coordinated changes in cell shape of endothelial cells (ECs), orchestrated by the actin cytoskeleton. The mechanisms that regulate this rearrangement in vivo are poorly understood - largely because of the difficulty to visualize filamentous actin (F-actin) structures with sufficient resolution. Here, we use transgenic mice expressing Lifeact-EGFP to visualize F-actin in ECs. We show that in the retina, Lifeact-EGFP expression is largely restricted to ECs allowing detailed visualization of F-actin in ECs in situ. Lifeact-EGFP labels actin associated with cell-cell junctions, apical and basal membranes and highlights actin-based structures such as filopodia and stress fiber-like cytoplasmic bundles. We also show that in the skin and the skeletal muscle, Lifeact-EGFP is highly expressed in vascular mural cells (vMCs), enabling vMC imaging. In summary, our results indicate that the Lifeact-EGFP transgenic mouse in combination with the postnatal retinal angiogenic model constitutes an excellent system for vascular cell biology research. Our approach is ideally suited to address structural and mechanistic details of angiogenic processes, such as endothelial tip cell migration and fusion, EC polarization or lumen formation

Improving Performance in Combinatorial Optimization Problems with Inequality Constraints: An Evaluation of the Unbalanced Penalization Method on D-Wave Advantage

Combinatorial optimization problems are one of the target applications of current quantum technology, mainly because of their industrial relevance, the difficulty of solving large instances of them classically, and their equivalence to Ising Hamiltonians using the quadratic unconstrained binary optimization (QUBO) formulation. Many of these applications have inequality constraints, usually encoded as penalization terms in the QUBO formulation using additional variables known as slack variables. The slack variables have two disadvantages: (i) these variables extend the search space of optimal and suboptimal solutions, and (ii) the variables add extra qubits and connections to the quantum algorithm. Recently, a new method known as unbalanced penalization has been presented to avoid using slack variables. This method offers a trade-off between additional slack variables to ensure that the optimal solution is given by the ground state of the Ising Hamiltonian, and using an unbalanced heuristic function to penalize the region where the inequality constraint is violated with the only certainty that the optimal solution will be in the vicinity of the ground state. This work tests the unbalanced penalization method using real quantum hardware on D-Wave Advantage for the traveling salesman problem (TSP). The results show that the unbalanced penalization method outperforms the solutions found using slack variables and sets a new record for the largest TSP solved with quantum technology.Comment: 8 pages, 7 figures, conferenc

Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

Accurate pressure drop estimation in forced boiling phenomena is important during the thermal analysis and the geometric design of cryogenic heat exchangers. However, current methods to predict the pressure drop have one of two problems: lack of accuracy or generalization to different situations. In this work, we present the correlated-informed neural networks (CoINN), a new paradigm in applying the artificial neural network (ANN) technique combined with a successful pressure drop correlation as a mapping tool to predict the pressure drop of zeotropic mixtures in micro-channels. The proposed approach is inspired by Transfer Learning, highly used in deep learning problems with reduced datasets. Our method improves the ANN performance by transferring the knowledge of the Sun & Mishima correlation for the pressure drop to the ANN. The correlation having physical and phenomenological implications for the pressure drop in micro-channels considerably improves the performance and generalization capabilities of the ANN. The final architecture consists of three inputs: the mixture vapor quality, the micro-channel inner diameter, and the available pressure drop correlation. The results show the benefits gained using the correlated-informed approach predicting experimental data used for training and a posterior test with a mean relative error (mre) of 6%, lower than the Sun & Mishima correlation of 13%. Additionally, this approach can be extended to other mixtures and experimental settings, a missing feature in other approaches for mapping correlations using ANNs for heat transfer applications

Functional evaluation and testing of a newly developed Teleost’s Fish Otolith derived biocomposite coating for healthcare

Polymers such as polycaprolactone (PCL) possess biodegradability, biocompatibility and affinity with other organic media that makes them suitable for biomedical applications. In this work, a novel biocomposite coating was synthesised by mixing PCL with layers of calcium phosphate (hydroxyapatite, brushite and monetite) from a biomineral called otolith extracted from Teleost fish (Plagioscion Squamosissimus) and multiwalled carbon nanotubes in different concentrations (0.5, 1.0 and 1.5 g/L). The biocomposite coating was deposited on an osteosynthesis material Ti6Al4V by spin coating and various tests such as Fourier transformation infrared spectroscopy (FTIR), Raman spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), scratch tests, MTT reduction cytotoxicity, HOS cell bioactivity (human osteosarcoma) by alkaline phosphatase (ALP) and fluorescence microscopy were performed to comprehensively evaluate the newly developed biocoating. It was found that an increase in the concentration of carbon nanotube induced microstructural phase changes of calcium phosphate (CP) leading to the formation of brushite, monetite and hydroxyapatite. While we discovered that an increase in the concentration of carbon nanotube generally improves the adhesion of the coating with the substrate, a certain threshold exists such that the best deposition surfaces were obtained as PCL/CP/CNT 0.0 g/L and PCL/CP/CNT 0.5 g/L

Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation

The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders. The strain this places on services means providing 24-hour monitoring is not sustainable. No solution exists to non-intrusively monitor the wellbeing of patients with dementia, resulting in delayed intervention. Using machine learning and signal processing, domestic energy supplies can be disaggregated to detect appliance usage. This enables Activities of Daily Living (ADLs) to be assessed. The aim is to facilitate early intervention and enable patients to stay in their homes for longer. A Support Vector Machine (SVM) and Random Decision Forest classifier are modelled using data from three test homes. The trained models are then used to monitor two patients with dementia during a six-month clinical trial undertaken in partnership with Mersey Care NHS Foundation Trust. In the case of load disaggregation, the SVM achieved (AUC=0.86074, Sen=0.756 and Spec=0.92838). While the Decision Forest achieved (AUC=0.9429, Sen=0.9634 and Spec=0.9634). ADLs are also analysed to identify the behavioural patterns of the occupant while detecting alterations in routine. The approach is sensitive in identifying behavioural routines and detecting anomalies in patient behaviour