Use of Energy Consumption during Milling to Fill a Measurement Gap in Hybrid Additive Manufacturing

Coupling additive manufacturing (AM) with interlayer peening introduces bulk anisotropic properties within a build across several centimeters. Current methods to map high resolution anisotropy and heterogeneity are either destructive or have a limited penetration depth using a nondestructive method. An alternative pseudo-nondestructive method to map high-resolution anisotropy and heterogeneity is through energy consumption during milling. Previous research has shown energy consumption during milling correlates with surface integrity. Since surface milling of additively manufactured parts is often required for post-processing to improve dimensional accuracy, an opportunity is available to use surface milling as an alternative method to measure mechanical properties and build quality. The variation of energy consumption during the machining of additive parts, as well as hybrid AM parts, is poorly understood. In this study, the use of net cutting specific energy was proposed as a suitable metric for measuring mechanical properties after interlayer ultrasonic peening of 316 stainless steel. Energy consumption was mapped throughout half of a cuboidal build volume. Results indicated the variation of net cutting specific energy increased farther away from the surface and was higher for hybrid AM compared to as-printed and wrought. The average lateral and layer variation of the net cutting specific energy for printed samples was 81% higher than the control, which indicated a significantly higher degree of heterogeneity. Further, it was found that energy consumption was an effective process signature exhibiting strong correlations with microhardness. Anisotropy based on residual strains were measured using net cutting specific energy and validated by hole drilling. The proposed technique contributes to filling part of the measure gap in hybrid additive manufacturing and capitalizes on the preexisting need for machining of AM parts to achieve both goals of surface finish and quality assessment in one milling operation

Comorbid Depression and Psychosis in Parkinson's Disease: A Report of 62,783 Hospitalizations in the United States

Background Depression and psychosis are common comorbidities that significantly affects the quality of life and disease outcomes in Parkinson's disease (PD) patients. Objective The aim of this study was to analyze and discern the differences in the hospitalization outcomes, comorbidities, and utilization of deep brain stimulation (DBS) in PD patients with comorbid depression and comorbid psychosis. Methods We used the Nationwide Inpatient Sample (2010-2014) and identified PD as a primary diagnosis (N = 62,783), and depression (N = 11,358) and psychosis (N = 2,475) as co-diagnosis using the International Classification of Diseases, Ninth Revision (ICD-9) codes. Pearson's chi-square test and independent-sample t-test were used for categorical data and continuous data, respectively. Results White male, older age, and comorbid psychosis were significantly associated with higher odds of having major severity of illness in PD inpatients. The mean length of stay (LOS) was higher in PD patients with psychosis compared to PD with depression (7.32 days vs. 4.23 days; P < 0.001), though the mean total charges of hospitalization were lower in psychosis ($31,240 vs.$38,581; P < 0.001). Utilization of DBS was lower in PD patients with psychosis versus with depression (3.9% vs. 24.3%; P < 0.001). Conclusion Psychiatric comorbidities are prevalent in PD patients and are associated with more disease severity, impaired quality of life, and increased use of healthcare resources (higher LOS and cost). They should be considered an integral part of the disease, and a multidisciplinary approach to managing this disease is crucial to improve the health-related quality of life of PD patients

Accelerated Sizing of a Power Split Electrified Powertrain

Component sizing generally represents a demanding and time-consuming task in the development process of electrified powertrains. A couple of processes are available in literature for sizing the hybrid electric vehicle (HEV) components. These processes employ either time-consuming global optimization techniques like dynamic programming (DP) or near-optimal techniques that require iterative and uncertain tuning of evaluation parameters like the Pontryagin's minimum principle (PMP). Recently, a novel near-optimal technique has been devised for rapidly predicting the optimal fuel economy benchmark of design options for electrified powertrains. This method, named slope-weighted energy-based rapid control analysis (SERCA), has been demonstrated producing results comparable to DP, while limiting the associated computational time by near two orders of magnitude. In this paper, sizing parameters for a power split electrified powertrain are considered that include the internal combustion engine size, the two electric motor/generator sizes, the transmission ratios, and the final drive ratio. The SERCA approach is adopted to rapidly evaluate the fuel economy capabilities of each sizing option in various driving missions considering both type-approval drive cycles and real-world driving profiles. While screening out for optimal sizing options, the implemented methodology includes drivability criteria along with fuel economy potential. Obtained results will demonstrate the agility of the developed sizing tool in identifying optimal sizing options compared to state-of-the-art sizing tools for electrified powertrains

Acute kidney injury in patients hospitalized with COVID-19

© 2020 International Society of Nephrology The rate of acute kidney injury (AKI) associated with patients hospitalized with Covid-19, and associated outcomes are not well understood. This study describes the presentation, risk factors and outcomes of AKI in patients hospitalized with Covid-19. We reviewed the health records for all patients hospitalized with Covid-19 between March 1, and April 5, 2020, at 13 academic and community hospitals in metropolitan New York. Patients younger than 18 years of age, with end stage kidney disease or with a kidney transplant were excluded. AKI was defined according to KDIGO criteria. Of 5,449 patients admitted with Covid-19, AKI developed in 1,993 (36.6%). The peak stages of AKI were stage 1 in 46.5%, stage 2 in 22.4% and stage 3 in 31.1%. Of these, 14.3% required renal replacement therapy (RRT). AKI was primarily seen in Covid-19 patients with respiratory failure, with 89.7% of patients on mechanical ventilation developing AKI compared to 21.7% of non-ventilated patients. 276/285 (96.8%) of patients requiring RRT were on ventilators. Of patients who required ventilation and developed AKI, 52.2% had the onset of AKI within 24 hours of intubation. Risk factors for AKI included older age, diabetes mellitus, cardiovascular disease, black race, hypertension and need for ventilation and vasopressor medications. Among patients with AKI, 694 died (35%), 519 (26%) were discharged and 780 (39%) were still hospitalized. AKI occurs frequently among patients with Covid-19 disease. It occurs early and in temporal association with respiratory failure and is associated with a poor prognosis

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.Comment: To appear in Neural Network