Brian P. Mann

Background/NeedLaparoscopic abdominal surgery requires navigating unique technical challenges with precision, dexterity, and a thorough understanding of anatomy. There is a need for higher-fidelity training models to assist in improving trainee competence. This manuscript introduces a novel modular abdominal simulation environment (MASE) with the ability to insufflate under standard parameters to accommodate laparoscopic and robotic surgery training and assessment.Methodology and Device DescriptionCT scans of a deidentified patient pelvis and spine are processed, reconstructed, and modified into 3D printable files, then printed using a high-fidelity resin printer. Silicone skin is developed to cover the MASE and mechanically fixed to create an air-tight seal. Insufflation capability is tested by measuring the pre- and post-insufflation height of the model, as well as internal pressure.Preliminary ResultsMASE meets the following criteria: anatomical accuracy, scale-to-life, and re-usability. Its ability to be insufflated via a Veress needle at Palmer's point recreates a pneumoperitoneum (increasing in height by 108%), allowing for effective port placement and clear visualization with a laparoscope. The platform successfully supports fundamentals of laparoscopic surgery (FLS) tasks including intracorporeal knot tying and peg transfer both with laparoscopic tools and robotic system.Current StatusCurrent work includes a more efficient locking mechanism, incorporation of the retroperitoneal space, and addition of synthetic/explant organs for high-fidelity abdominal simulation. MASE combines high anatomical fidelity, realistic tissue simulation, and procedural versatility with reproducibility. Future testing includes stiffness characterization of the silicone skin and validation for surgical resident training.

Abstract Dynamical systems theory has long provided a foundation for understanding evolving phenomena across scientific domains. Yet, the application of this theory to complex real-world systems remains challenging due to issues in mathematical modeling, nonlinearity, and high dimensionality. In this work, we introduce a data-driven computational framework to derive low-dimensional linear models for nonlinear dynamical systems directly from raw experimental data. This framework enables global stability analysis through interpretable linear models that capture the underlying system structure. Our approach employs time-delay embedding, physics-informed deep autoencoders, and annealing-based regularization to identify novel low-dimensional coordinate representations, unlocking insights across a variety of simulated and previously unstudied experimental dynamical systems. These new coordinate representations enable accurate long-horizon predictions and automatic identification of intricate invariant sets while providing empirical stability guarantees. Our method offers a promising pathway to analyze complex dynamical behaviors across fields such as physics, climate science, and engineering, with broad implications for understanding nonlinear systems in the real world.

Study aim : Oxygen Uptake (VO 2 ) is avaluable metric for the prescription of exercise intensity and the monitoring of training progress. However, VO 2 is difficult to assess in anon-laboratory setting. Recently, an artificial neural network (ANN) was used to predict VO 2 responses during aset walking protocol on the treadmill [9]. The purpose of the present study was to test the ability of an ANN to predict VO 2 responses during cycling at self-selected intensities using Heart Rate (HR), time derivative of HR, power output, cadence, and body mass data. Material and methods : 12 moderately-active adult males (age: 21.1 ± 2.5 years) performed a50-minute bout of cycling at a variety of exercise intensities. VO 2 , HR, power output, and cadence were recorded throughout the test. An ANN was trained, validated and tested using the following inputs: HR, time derivative of HR, power output, cadence, and body mass. A twelve-fold hold-out cross validation was conducted to determine the accuracy of the model. Results : The ANN accurately predicted the experimental VO 2 values throughout the test (R 2 = 0.91 ± 0.04, SEE = 3.34 ± 1.07 mL/kg/min). Discussion : This preliminary study demonstrates the potential for using an ANN to predict VO 2 responses during cycling at varied intensities using easily accessible inputs. The predictive accuracy is promising, especially considering the large range of intensities and long duration of exercise. Expansion of these methods could allow ageneral algorithm to be developed for a more diverse population, improving the feasibility of oxygen uptake assessment.

Detection and identification of nonlinearity is a task of high importance for structural dynamics.On the one hand, identifying nonlinearity in a structure would allow one to build more accurate models of the structure.On the other hand, detecting nonlinearity in a structure, which has been designed to operate in its linear region, might indicate the existence of damage within the structure.Common damage cases which cause nonlinear behaviour are breathing cracks and points where some material may have reached its plastic region.Therefore, it is important, even for safety reasons, to detect when a structure exhibits nonlinear behaviour.In the current work, a method to detect nonlinearity is proposed, based on the distribution of the gradients of a data-driven model, which is fitted on data acquired from the structure of interest.The data-driven model selected for the current application is a neural network.The selection of such a type of model was done in order to not allow the user to decide how linear or nonlinear the model shall be, but to let the training algorithm of the neural network shape the level of nonlinearity according to the training data.The neural network is trained to predict the accelerations of the structure for a time-instant using as input accelerations of previous time-instants, i.e. one-step-ahead predictions.Afterwards, the gradients of the output of the neural network with respect to its inputs are calculated.Given that the structure is linear, the distribution of the aforementioned gradients should be unimodal and quite peaked, while in the case of a structure with nonlinearities, the distribution of the gradients shall be more spread and, potentially, multimodal.To test the above assumption, data from an experimental structure are considered.The structure is tested under different scenarios, some of which are linear and some of which are nonlinear.More specifically, the nonlinearity is introduced as a column-bumper nonlinearity, aimed at simulating the effects of a breathing crack and at different levels, i.e. different values of the initial gap between the bumper and the column.Following the proposed method, the statistics of the distributions of the gradients for the different scenarios can indeed be used to identify cases where nonlinearity is present.Moreover, via the proposed method one is able to quantify the nonlinearity by observing higher values of standard deviation of the distribution of the gradients for lower values of the initial column-bumper gap, i.e. for "more nonlinear" scenarios.

Objective: We sought to measure the deformation of tibiofemoral cartilage immediately following a 3-mile treadmill run, as well as the recovery of cartilage thickness the following day. To enable these measurements, we developed and validated deep learning models to automate tibiofemoral cartilage and bone segmentation from double-echo steady-state magnetic resonance imaging (MRI) scans. Design: Eight asymptomatic male participants arrived at 7 a.m., rested supine for 45 ​min, underwent pre-exercise MRI, ran 3 miles on a treadmill, and finally underwent post-exercise MRI. To assess whether cartilage recovered to its baseline thickness, participants returned the following morning at 7 a.m., rested supine for 45 ​min, and underwent a final MRI session. These images were used to generate 3D models of the tibia, femur, and cartilage surfaces at each time point. Site-specific tibial and femoral cartilage thicknesses were measured from each 3D model. To aid in these measurements, deep learning segmentation models were developed. Results: All trained deep learning models demonstrated repeatability within 0.03 ​mm or approximately 1 ​% of cartilage thickness. The 3-mile run induced mean compressive strains of 5.4 ​% (95 ​% CI ​= ​4.1 to 6.7) and 2.3 ​% (95 ​% CI ​= ​0.6 to 4.0) for the tibial and femoral cartilage, respectively. Furthermore, both tibial and femoral cartilage thicknesses returned to within 1 ​% of baseline thickness the following day. Conclusions: The 3-mile treadmill run induced a significant decrease in both tibial and femoral cartilage thickness; however, this was largely ameliorated the following morning.

This paper outlines the performance of a precessing jet burner with no moving parts, which is efficient whilst at the same time generating only very low levels of NO x gases. Results from a range of different sized experimental burners ranging from 10kw to llOkw are discussed and compared with a full scale precessing jet burner ( ~20MW) firing a cement kiln. Two types of experimental burner were used-a naturally driven precessing jet and a mechanically driven analogue.

Dynamical systems theory has long provided a foundation for understanding evolving phenomena across scientific domains. Yet, the application of this theory to complex real-world systems remains challenging due to issues in mathematical modeling, nonlinearity, and high dimensionality. In this work, we introduce a data-driven computational framework to derive low-dimensional linear models for nonlinear dynamical systems directly from raw experimental data. This framework enables global stability analysis through interpretable linear models that capture the underlying system structure. Our approach employs time-delay embedding, physics-informed deep autoencoders, and annealing-based regularization to identify novel low-dimensional coordinate representations, unlocking insights across a variety of simulated and previously unstudied experimental dynamical systems. These new coordinate representations enable accurate long-horizon predictions and automatic identification of intricate invariant sets while providing empirical stability guarantees. Our method offers a promising pathway to analyze complex dynamical behaviors across fields such as physics, climate science, and engineering, with broad implications for understanding nonlinear systems in the real world.

Tissue identification is essential for surgeons to properly perform procedures and make informed decisions to minimize potential harm to patients. Minimally invasive surgery (MIS) offers enhanced patient safety and outcomes at the cost of lost information due to restricted vision and loss of touch, among other factors. This makes it more difficult to quickly and consistently identify tissues correctly. Bioimpedance spectroscopy (BIS) offers the potential to identify tissues using rapid measurements that leverage differences in electrical properties between tissues. However, using BIS to differentiate large sets of tissues in a singular anatomical area, such as the gastrointestinal (GI) tract, has remained a significant challenge because of the overlap of similar tissues' responses and variability between measurements. This work proposes the application of frequency response function (FRF) similarity metrics as a signal processing technique to extract new features from BIS measurements on porcine tissues. These features are then used as inputs to machine learning (ML) models that are trained on an ex vivo dataset for identification of eight different in vivo porcine abdominal tissues. The ML models using similarity metric inputs performed on par or better than models using raw measurement inputs, except for the support vector machine (SVM) models. A neural network (NN) model using a similarity metric input performed best by achieving a mean accuracy of 70.3% and F-measure of 0.716. More importantly, the similarity metrics enhanced the ability of the models to identify all tissues rather than considering tissues from similar anatomical areas as the same. Ultimately, the FRF similarity metrics are a novel approach for extracting features from BIS measurements that improved identification performance when considering both accuracy and capability of differentiating all tissues in the dataset.

Coherent Beam Combination (CBC) is used for Laser Directed Energy Weapons (LDEW) because of its power scalability and ability to produce high quality, low-divergence output beams capable of high-speed compensation for atmospheric turbulence. Traditional CBC optical arrays, comprised of many individual optics, suffer from mechanical and thermal stability issues as power levels and size increase. PowerPhotonic monolithic lens arrays offer a robust, scalable solution that simplifies system alignment and offers the mechanical and thermal stability required to succeed at current and future LDEW power levels. Unique manufacturing techniques allow PowerPhotonic to continue to increase the form factor of these monolithic arrays to keep up with power scaling requirements. Newly implemented tools have demonstrated a 4x increase in clear aperture capability with room for further improvement with more mechanical modifications to the manufacturing system. Large monolithic lens arrays have the power handling and dense packing capabilities to support LDEW systems aiming to achieve megawatts of coherently combined power and beyond.

By modeling a piezoelectric cantilever beam system in which mechanical bistability emerges from repulsion between a stationary magnet and magnetic tip mass, the size of the basin of attraction for interwell oscillations is increased, so that the higher-energy solutions to the system may be more readily acquired than in previously studied systems.The primary drawback of linear energy harvesters is their very narrow frequency range.Non-linear harvesters provide wider operating frequency regions, but can have coexisting solutions, with the desirable high energy solution usually the more difficult to obtain.Existing work on bistable piezoelectric harvesting systems consider dipole interactions between the magnetic tip mass and an external magnet that is also oscillating at the experimental frequency.This paper demonstrates increased energy generation in transitions between the potential wells of the system through replacing the oscillating independent magnet with a stationary one.Optimizing the system to produce frequent well escapes induced by minimal disturbances from dipole interactions, frequency alterations, or changes in excitation amplitude will aide in achieving the high amplitude solutions of the system.Analytical evaluations of the energy in this system along with simulations of the behavior of the cantilever beam are performed for proof of concept that may be utilized in producing these high amplitude solutions in future experiments.

Abstract Experimental analysis of a translating spring-mass system with a constant electrostatic force in the presence of viscous damping is presented. The challenge in this effort has been to develop an analytical model for the electrostatic system and experimentally extract numerical values for viscous damping and the very small electrostatic force. The system has dynamical equations of motion similar to that of a homogeneous spring-mass system with no external force in the presence of viscous damping. The electrostatic parallel plate system includes the addition of a conservative electrostatic potential energy term corresponding to an electrostatic restoring force. The electrostatic force is conservative and opposes the direction of motion when the position of the oscillating mass being acted upon is greater than or less than zero. Another, similar system comparison can be made between the electrostatic one and that of a translating system acted upon by friction alone. However, the frictional force, unlike the electrostatic one presented here, is non-conservative and, also unlike the electrostatic force, opposes the direction of velocity when the oscillating mass being acted upon is greater than or less than zero. From empirical data, parameters of the translating electrostatic system are determined and cross-verified by more than one method. A state-space model is developed using the Lagranian formalism which includes a dissipative viscous damping term. Results are simulated in Matlab numerically. These simulated results are compared to measured data. Applications of the electrostatic force in a translating set-up are discussed and concluded.