Aircraft Engine's Lock-On Envelope due to Internal and External Sources of Infrared Signature

The lock-on envelope of target aircraft is important in determining its susceptibility against a heat-seeking missile and depends on the aircraft's infrared (IR) signature level (IRSL). In this investigation, the lock-on envelope is estimated in 3-5 mu m and 8-12 mu m bands, considering internal and external sources of IR signature from surfaces of aircraft engine layout. This is achieved by a synthesis of the following four major tasks: 1) analytical estimation of solid angles subtended by aircraft surfaces, 2) prediction of aircraft surface temperatures from convective and radiative heat transfer model, 3) computation of atmospheric transmission and background radiance, and 4) estimation of earthshine, skyshine, and sunshine irradiances. Polar plots in 2-D for lock-on range, for vertical and horizontal planes are analyzed, to study the role of internal and external sources for different aspects

Effects of State Law Limiting Postoperative Opioid Prescription in Patients After Cesarean Delivery

The impact of the Florida State law House Bill 21 (HB 21) restricting the duration of opioid prescriptions for acute pain in patients after cesarean delivery is unknown. Our objective was to assess the association of the passage of Florida State law HB 21 with trends in discharge opioid prescription practices following cesarean delivery, necessity for additional opioid prescriptions, and emergency department visits at a large tertiary care center. This was a retrospective cohort study conducted at a large, public hospital. The 2 cohorts represented the period before and after implementation of the law. Using a confounder-adjusted segmented regression analysis of an interrupted time series, we evaluated the association between HB 21 and trends in the proportions of patients receiving opioids on discharge, duration of opioid prescriptions, total opioid dose prescribed, and daily opioid dose prescribed. We also compared the need for additional opioid prescriptions within 30 days of discharge and the prevalence of emergency department visits within 7 days after discharge. Eight months after implementation of HB 21, the mean duration of opioid prescriptions decreased by 2.9 days (95% confidence interval [CI], 5.2-0.5) and the mean total opioid dose decreased by 20.1 morphine milligram equivalents (MME; 95% CI, 4-36.3). However, there was no change in the proportion of patients receiving discharge opioids (95% CI of difference, -0.1 to 0.16) or in the mean daily opioid dose (mean difference, 5.3 MME; 95% CI, -13 to 2.4). After implementation of the law, there were no changes in the proportion of patients who required additional opioid prescriptions (2.1% vs 2.3%; 95% CI of difference, -1.2 to 1.5) or in the prevalence of emergency department visits (2.4% vs 2.2%; 95% CI of difference, -1.6 to 1.1). Implementation of Florida Law HB 21 was associated with a lower total prescribed opioid dose and a shorter duration of therapy at the time of hospital discharge following cesarean delivery. These reductions were not associated with the need for additional opioid prescriptions or emergency department visits

Tree-Based Machine Learning and Nelder–Mead Optimization for Optimized Cr(VI) Removal with Indian Gooseberry Seed Powder

Wastewater containing a mixture of heavy metals, a byproduct of chemical, petrochemical, and refinery activities driven by urbanization and industrial expansion, poses significant environmental threats. Analyzing such wastewater through adsorbate-adsorbent experiments yields extensive datasets. However, traditional methodologies like the Box–Behnken design (BBD) within the response surface methodology (RSM) struggle with managing large datasets and capturing the complex, nonlinear relationships inherent in such experimental data. To address these challenges, ML techniques have emerged as promising tools for accurately predicting the removal percentage of heavy metals from wastewater. In this study, we utilized tree-based regression models—specifically decision tree regression (DTR), random forest regression (RFR), and extra tree regression (ETR)—to forecast the efficiency of gooseberry seed powder in removing chromium (Cr(VI)) from wastewater. Additionally, we employed an ML-based Nelder–Mead optimization approach to identify the optimal values for key features (initial Cr(VI) concentration, pH, and Indian gooseberry powder dosage) which maximized the Cr(VI) removal percentage. Our experimental results reveal that the ETR model achieved an impressive R2 score of 0.99, demonstrating a low error rate in predicting the Cr(VI) removal percentage. Furthermore, we used DTR-Nelder–Mead, RFR-Nelder–Mead, and ETR-Nelder–Mead optimization approaches on a synthesized dataset of 2000 instances while varying the initial Cr(VI) concentration, pH, and Indian gooseberry powder dosage. The analysis determined that the DTR-Nelder–Mead and RFR-Nelder–Mead approaches yielded the highest Cr(VI) removal percentages of 78.21% and 78.107% at an initial concentration of 95.55 mg/L, respectively, a pH level of four, and an adsorbent dosage of 8 g/L of gooseberry seed powder. Furthermore, the ETR-Nelder–Mead approach obtained the maximum Cr(VI) removal percentage of 85.11% at an initial concentration of 99.25 mg/L, a pH level of 4.97, and an adsorbent dosage of 9.62 g/L of gooseberry seed powder. These results reported an increase in the Cr(VI) removal percentage ranging from 4.66% to 11.56% more than the Cr(VI) removal percentage obtained by experimentation. These findings underscore the efficacy of tree-based regression models and ML-based Nelder–Mead optimization in elucidating chromium removal processes from wastewater, offering valuable insights into effective treatment strategies

Derivative Free Square-root Cubature Kalman Filter for Nonlinear Brushless DC Motors

This paper presents a nonlinear square-root estimation scheme for brushless DC (BLDC) motors. The cubature Kalman filter (CKF) is the main estimation tool for the presented approach. The CKF is a recently proposed estimator for highly nonlinear systems and its efficacy has been verified on several applications. The square-root version of the CKF is preferred over the conventional CKF for real-time applications. Despite of having several advantages over other nonlinear filters, the CKF has not yet been explored for state estimation of electric drives in the electric drives community. In this paper, we present a square-root CKF for the speed and rotor position estimation of a highly nonlinear and high fidelity BLDC motor, these estimated speed and rotor position are then fed back to control the speed of the BLDC motor. The efficacy of the presented approach for low and high reference speeds, and in the presence of parametric uncertainties, is demonstrated by real-time experiments