Prytime Medical (United States)

Large Language Models (LLMs) have demonstrated remarkable proficiency in generating code. However, the misuse of LLM-generated (synthetic) code has raised concerns in both educational and industrial contexts, underscoring the urgent need for synthetic code detectors. Existing methods for detecting synthetic content are primarily designed for general text and struggle with code due to the unique grammatical structure of programming languages and the presence of numerous ``low-entropy'' tokens. Building on this, our work proposes a novel zero-shot synthetic code detector based on the similarity between the original code and its LLM-rewritten variants. Our method is based on the observation that differences between LLM-rewritten and original code tend to be smaller when the original code is synthetic. We utilize self-supervised contrastive learning to train a code similarity model and evaluate our approach on two synthetic code detection benchmarks. Our results demonstrate a significant improvement over existing SOTA synthetic content detectors, delivering notable gains in both performance and robustness on the APPS and MBPP benchmarks.

Open accessand treatment method innovations to reduce preventable number of deaths and disability.Recent changes in military mission planning and investment are now further mortgaging the future of civilian trauma innovation and highlight a massive risk in assigning the majority of the responsibility for funding of research in this area to the military.While the military investment in this area is appropriately prioritized to its specific challenges, this leaves a growing resource vacuum that needs to be addressed by the appropriate civilian funding agencies (eg, NIH, Biomedical Advanced Research and Development Authority) to enhance health, lengthen life and reduce the burdens of illness and disability.A cohesive civilian approach to trauma research and development will benefit all Americans and allow US military leaders to focus their efforts on the future battlefield.Contributorship DB, RD, TM and RG each contributed substantially to the conception of this work.DB

Resuscitative endovascular balloon occlusion of the aorta (REBOA) allows for effective temporization of exsanguination from non-compressible hemorrhage (NCTH) below the diaphragm. However, the therapeutic window for aortic occlusion is time-limited given the ischemia-reperfusion injury generated. Significant effort has been put into translational research to develop new strategies to alleviate the ischemia-reperfusion injury and extend the application of endoaortic occlusion. Targeted regional optimization (TRO) is a partial REBOA strategy to augment proximal aortic and cerebral blood flow while targeting minimal threshold of distal perfusion beyond the zone of partial aortic occlusion. The objective of TRO is to reduce the degree of ischemia caused by complete aortic occlusion while providing control of distal hemorrhage. This review provides a synopsis of the concept of TRO, pre-clinical, translational experiences with TRO and early clinical outcomes. Early results from TRO strategies are promising; however, further studies are needed prior to large-scale implementation into clinical practice.

Sensor issues arise quite often in many fields such as data showing anomalous behavior or data being corrupt. This involves finding either faulty, noisy and malfunctioning sensors or anomalous behavior of the physical system deviating from the normal behavior indicating either new physics or the assumptions of the current model are being violated. This necessitates integration of domain-specific reduced form physics-based engineering models with data-driven modeling techniques to model effectively by covering wider data space. There could be sensors on the order of thousand but not every sensor is relevant and useful to the system modeling. Real-time drilling modeling is used as a prototype for demonstrating the new algorithm to deal with modeling efficiently virtual sensors. This paper provides a new real-time model with deep neural network (DNN) using a new hybrid physics/data driven algorithm that can intelligently pick the models to retrain and predict accurately for virtual sensing. This approach offers an improved and efficient methodology to arrive at the decision of whether the sensors are malfunctioning, or the physics models needs to be updated to model the new behavior. This method was applied to predict rate of penetration (ROP) with automatic sensor value predictions of hookload (HL), rotations per minute (RPM), pressure (P) and How rate (Q) for drilling. The physics model is obtained from the engineering models produced from domain insight. Thus, the modeling integrates reduced form physics-based engineering models into DNN framework. The generated data from the engineering model are needed to fill the void space in the surface not covered by the real-time measured data. The hybrid physics/data driven algorithm is fast, as the training is performed whenever the deviation occurs either between the model predictions and sensor values or ROP predictions deviate or both occur. The hybrid model uses the DNN framework to speed up the predictions and improve the accuracy of the ROP. The new hybrid modeling approach developed in this paper for virtual sensors can be applied to any real-time modeling system.

Abstract This paper describes a new and comprehensive 3D hydraulic fracturing simulation model, built within a single software system combining Finite-Difference reservoir flow simulation with Finite-Element displacement and stress/strain computations. This allows dynamic and explicit calculation of fracture width (i.e., opening, propagation, and closing) and modeling of surrounding dynamic stimulated reservoir volumes. This new fracture modeling formulation advances the model initially developed by Ji et al. (2009) and the workflows described in Min et al. (2018) and Sen et al. (2018) for optimization of completion design and well spacing in unconventional reservoirs. The new code relies on carefully considered algorithmic constructs (such as iterative coupling). It was designed for ensemble modeling over ranges of uncertainties in petrophysical and mechanical stratigraphy. Multiple options for combining node displacements on the fracture face along with new iterative algorithms and efficient use of elements of symmetry allow for representative calculations for a range of models with reasonable runtimes. The workflow uses structured grids, and the same block definitions are used for both the flow and geomechanical computations. Fracture extent and width, leakoff, and stress-dependent permeability enhancement in the stimulated reservoir volume (SRV) are all computed in a coupled fashion, based on reservoir flow characterization and mechanical stratigraphy (stiffness and stress) in a fully 3D heterogeneous sense. This tool has been used to model hydraulic fracturing (fluid injection), flow-back, and production within a single workflow. The evolution of the fractures can be tracked in usual time step fashion, along with leakoff, multiphase saturations, and pressures on a 3D grid. Simultaneously, the model computes the poroelastic changes in all stresses and their effect on fracture propagation or closure and on permeability and porosity of the media, i.e., the SRV development. Dynamic fracture height, width and length are determined without the constraints of explicit shape assumptions or simplifications; rather, they are computed based on propagation criteria using local, dynamic pressure, mechanical properties and stresses. We have also used this new simulator to model and match Diagnostic Fracture Injection Tests (DFITs). The complexity of the interactions of physical mechanisms in this model requires large computing times. A significant improvement in run time (an order of magnitude) was achieved by using new algorithms for the iterative solution of the flow/stress/fracture coupled problem. This provides possibilities for modeling fracturing in complex reservoirs with a new level of accuracy. For example, the model provided new insight in the importance of flow friction in the fracture for history matching the pumping pressure. In DFITs, the simulated details of fracture initiation and closure can be used to calibrate mechanical properties as well as permeability behavior with stress. Finally, the capability to use 3D stress and reservoir characterization is invaluable in modeling cases of unusual vertical fracture growth. Practicing engineers will appreciate the value of the integrated simulator presented herein. It helps in developing a clearer understanding of the causal effects of different factors that impact the success of a hydraulic fracturing/stimulation program. These factors include geological, tectonic, mechanical, and operator influences. Using this simulator as a kernel, powerful completion optimization workflows can be built by running a priori simulations of numerous permutations of influencing factors.