M4 Engineering (United States)

This research paper discusses the creation and application of a fully automated robot that follows a line by means of reinforcement learning techniques, more specifically the Q-learning algorithm. The system proposed comprises a vision-based perception module that uses OpenCV for its development and also includes the simulation environment of CoppeliaSim thus making the navigation process adaptive as well as robust. By interaction with its surroundings at all times, the agent gets trained on the control policy that is optimal in terms of lateral deviation minimization and hence provides stable trajectory tracking. The research comprises a detailed assessment of the Q-table dimensions and hyper-parameters such as the learning rate, discount factor, and exploration rate, systematically determining their impact on learning performance, convergence, and accuracy. According to the experimental findings, a configuration of a 7×7 Q-table strikes the best balance between precision and convergence speed that in turn results in smooth and even path tracking. The method, though quite effective under controlled conditions, does have its drawbacks in terms of state discretization, generalization, and real-time processing, thereby providing directions for the applications of deep reinforcement learning and adaptive perception models in the future.

This article reveals the creation and execution of a digital twin (DT) for the robotic inspection system of mechanical parts, applying the machine vision method. The examination is carried out on the KUKA iiwa robot that has a camera for the purpose of identifying and sorting the parts that are positioned on the work area. A vision system based on a YOLO convolutional neural network (CNN) was utilized for the tasks of object detection and classification. The dataset was automatically created via the use of 3D CAD models along with domain randomization to mitigate the risk of overfitting. The simulation environment was set up in IsaacSim, and the vision system was interfaced through ROS Noetic with hand-eye calibration also included. The performance of the YOLO network evidenced an overall precision of 94.8% and recall of 87.7% in the test dataset indicating the parts' effective detection and classification. The research underlines the convergence of simulation, deep learning, robotics, and the potential for automated inspections in manufacturing sectors.

The development of high-fidelity multidisciplinary analysis and optimization (MDAO) capabilities is essential for advancing modern aerospace vehicle design. Traditional workflows often suffer from limited interoperability between structural solvers, aerodynamic tools, and optimization frameworks. To address these limitations, this project focuses on integrating NASTRAN, a structural solver, into the OpenMDAO framework. This effort was conducted under NASA Contract 80GRC023CA047 by M4 Engineering, Inc., as part of the broader initiative to enhance Model-Based Systems Analysis and Engineering (MBSA&E) capabilities. The primary objective of the project was to develop a robust, reusable, and modular Python-based interface that allows NASTRAN to interface within OpenMDAO workflows and improve structural optimization capabilities. This enables engineers to leverage NASTRAN's structural analysis and sensitivity capabilities while benefiting from OpenMDAO's flexibility and advanced optimization drivers. In support of this objective, multiple demonstration models were created across various NASTRAN solution sequences (SOLs), including static, aeroelastic, flutter, and buckling analyses. These models were used to evaluate the accuracy, robustness, and computational performance of the integration. Additionally, capabilities were developed to extract sensitivity data, aggregate constraint outputs, and streamline the generation of OpenMDAO components from existing NASTRAN input decks. The work described herein also included integration of third-party aerodynamic tools (e.g., transonic CFD solvers and GROM) with OpenMDAO, development of representative aircraft configurations such as the truss-braced wing (TTBW) and blended-wing body (BWB), and final deployment into NASA's Aviary conceptual design framework. The outcomes of this project provide a foundation for future multidisciplinary optimizations using high-fidelity structural models and demonstrates some of the challenges.

Understanding individual genetic factors has led to significant improvements in disease prediction, diagnosis and tailored treatment, and data well beyond what the human mind can comprehend can only be managed through machine learning (ML). It focuses on the use of ML techniques for analyzing and interpreting genomic data, particularly in their contributions to the identification of disease-associated genetic variants. ML uses one or a combination of supervised and unsupervised learning models, deep learning architectures, and ensemble methods to guide the extraction of meaningful information from complex genetic data. The feature selection techniques, including recursive feature elimination (RFE) and principal component analysis (PCA), improve model performance by minimizing dimensions and removing noise. Specific disease susceptibility can be predicted with high accuracy from the genetic markers using classification algorithms, such as random forests (RF), support vector machines (SVM), and neural networks. Clustering techniques (k-means, hierarchical clustering, etc.) are also used to identify hidden genetic subgroups associated with particular diseases. With the development of next-generation sequencing (NGS) and genome-wide association studies (GWAS), big data sets have emerged encouraging the application of ML-based approaches for instance to allow fast and accurate variant classification. CNNs (convolutional neural networks) are effective in modelling sequence-based data, while RNNs (recurrent neural networks) have demonstrated utility in recognizing pathogenic mutations. Naturallanguage processing (NLP) is another approach that further improves the variant annotation process by extracting valuable information about variants from biomedical publications. Future Work In this paper reinforcement learning in used for genetic diagnosis and treatment planning. Explainable AI methods make sure that the model is transparent and reliable and mitigate the black-box issue of deep learning models. However, challenges remain such as data heterogeneity, class imbalance, and explaining ML models. Some federated learning frameworks can aid genome privacy-preserving analysis by allowing collaborative model training without the aggregation of sensitive patient data. These approaches come with ethical questions including potential biases in genetic information resources and fair access to ML solutions in healthcare, that need to be constantly monitored. Further research could aim to improve model generalizability across different populations and develop hybrid ML methods that utilize both statistical and deep learning approaches to provide optimal predictive accuracy. This impressive potential for ML to revolutionize precision medicine lies in its ability to achieve disease prediction, targeted therapeutics development, and hence combat diseases at branch level.

(Abstracted from Ultrasound Obstet Gynecol 2024;63:15–23 Congenital anomalies can be diagnosed prenatally through genetic testing, including exome sequencing (ES), quantitative fluorescence polymerase chain reaction (QF-PCR), and chromosomal microarray analysis (CMA), in addition to whole-genome sequencing (WGS). Each method has anomalies it can better detect, with WGS having the greatest diagnostic capability.

In healthcare WSN applications, data loss due to congestion may trigger a "death alert" for a crucial patient. Because of this, a system must be designed to either prevent or reduce congestion. This study presents an energy-efficient and reliable multi-path data transmission protocol for healthcare Wireless Sensor Networks (WSN). Spare data and sensitive data packets are sent through a route with little transmission interference when the system is jammed. The recommended technique assesses the danger of congestion at intermediate nodes and adjusts their transmission rate to prevent congestion. Each node's buffer is partitioned to make data transport fair and efficient. The protocol's high reliability is maintained through hop-by-hop loss recovery and acknowledgement. Simulations are used to test the recommended method's functionality. In terms of energy economy, reliability, and end-to-end delivery ratio, it exceeds existing healthcare congestion management algorithms. This study evaluates and compares the routing techniques. They present a concept for developing an energy-efficient routing protocol. This approach designs quick, compact, more energy-efficient routes than existing ones. NS2 is used to run and test the proposed system. The proposed method beats the current protocol in terms of average delay, energy savings, and packet delivery ratio.

View Video Presentation: https://doi.org/10.2514/6.2023-1360.vid Successful conceptual aircraft design requires accurate structural layout and weight prediction. Empirical formulations exist for many archetypal aircraft, but are not available for novel aircraft concepts. We propose a methodology for creating physics-based weight equations that can be used in lieu of empirical weight equations. The methodology relies on M4 Structures Studio, a toolset that can rapidly estimate the structural weight of conceptual designs for unconventional aerospace vehicles. Hundreds of design variants are used in conjunction with the M4 Structures Studio parametric structural mode to create hundreds of shell-based finite element models. Structural weight is calculated for each model and an n-dimensional, 2nd-order polynomial is fitted to each weight dataset. The polynomials can be used to predict the weight of urban air mobility variants, and they can be used within a larger multi-disciplinary design optimization as weight-prediction surrogate models. The methodology was demonstrated with NASA's Lift+Cruise Configuration.

Accurate structural weight prediction for novel vehicle configurations is an important and often neglected aspect of conceptual design. Many unconventional vehicle concepts are not well represented by empirical structural weight models based on historical data. Traditional finite element modeling and sizing optimization is a time-consuming man-in-the-loop process. This paper describes the process of bringing the accuracy of finite element modeling and physics-based loads modeling to the conceptual design stage in a streamlined workflow for rapid structural weight prediction. M4 Structures Studio (M4SS), a tool to parametrically define the structural configuration for aircraft and quickly estimate structural weight, has been enhanced to support the modeling of rotorcraft structures. Preliminary sizing results are presented for a NASA Urban Air Mobility concept.

Various multimodal analgesic approaches have been proposed for spine surgery. The authors evaluated the effect of using a combination of four nonopioid analgesics versus placebo on Quality of Recovery, postoperative opioid consumption, and pain scores. Adults having multilevel spine surgery who were at high risk for postoperative pain were double-blind randomized to placebos or the combination of single preoperative oral doses of acetaminophen 1,000 mg and gabapentin 600 mg, an infusion of ketamine 5 µg/kg/min throughout surgery, and an infusion of lidocaine 1.5 mg/kg/h intraoperatively and during the initial hour of recovery. Postoperative analgesia included acetaminophen, gabapentin, and opioids. The primary outcome was the Quality of Recovery 15-questionnaire (0 to 150 points, with 15% considered to be a clinically important difference) assessed on the third postoperative day. Secondary outcomes were opioid use in morphine equivalents (with 20% considered to be a clinically important change) and verbal-response pain scores (0 to 10, with a 1-point change considered important) over the initial postoperative 48 h. The trial was stopped early for futility per a priori guidelines. The average duration ± SD of surgery was 5.4 ± 2.1 h. The mean ± SD Quality of Recovery score was 109 ± 25 in the pathway patients (n = 150) versus 109 ± 23 in the placebo group (n = 149); estimated difference in means was 0 (95% CI, -6 to 6, P = 0.920). Pain management within the initial 48 postoperative hours was not superior in analgesic pathway group: 48-h opioid consumption median (Q1, Q3) was 72 (48, 113) mg in the analgesic pathway group and 75 (50, 152) mg in the placebo group, with the difference in medians being -9 (97.5% CI, -23 to 5, P = 0.175) mg. Mean 48-h pain scores were 4.8 ± 1.8 in the analgesic pathway group versus 5.2 ± 1.9 in the placebo group, with the difference in means being -0.4 (97.5% CI; -0.8, 0.1, P = 0.094). An analgesic pathway based on preoperative acetaminophen and gabapentin, combined with intraoperative infusions of lidocaine and ketamine, did not improve recovery in patients who had multilevel spine surgery.