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In recent years, well-test research has witnessed several works to automate reservoir model identification and characterization using computer-assisted models. Since the reservoir model identification is a classification problem, while its characterization is a regression-based task, their simultaneous accomplishment is always challenging. This work combines genetic algorithm optimization and artificial neural networks to identify and characterize homogeneous reservoir systems from well-testing data automatically. A total of eight prediction models, including two classifiers and six regressors, have been trained. The simulated well-test pressure derivatives with varying noise percentages comprise the training samples. The feature selection and hyperparameter tuning have been performed carefully using the genetic algorithm to enhance the prediction accuracy. The models were validated using nine simulated and one real-field test case. The optimized classifier identifies all the reservoir models with a classification accuracy higher than 79%. In addition, the statistical analysis approves that the optimized regressors accurately perform the reservoir characterization with mean relative errors of lower than 4.5%. The minimized manual interference reduces human bias, and the models have significant noise tolerance for practical applications.

INTRODUCTION: Segregating hepatic tumors from the liver in computed tomography (CT) scans is vital in hepatic surgery planning. Extracting liver tumors in CT images is complex due to the low contrast between the malignant and healthy tissues and the hazy boundaries in CT images. Moreover, manually detecting hepatic tumors from CT images is complicated, time-consuming, and needs clinical expertise. OBJECTIVES: An automated liver and hepatic malignancies segmentation is essential to improve surgery planning, therapy, and follow-up evaluation. Therefore, this study demonstrates the creation of an intuitive approach for segmenting tumors from the liver in CT scans. METHODS: The proposed framework uses residual UNet (ResUNet) architecture and local region-based segmentation. The algorithm begins by segmenting the liver, followed by malignancies within the liver envelope. First, ResUNet trained on labeled CT images predicts the coarse liver pixels. Further, the region-level segmentation helps determine the tumor and improves the overall segmentation map. The model is tested on a public 3D-IRCADb dataset. RESULTS: Two metrics, namely dice coefficient and volumetric overlap error (VOE), were used to evaluate the performance of the proposed method. ResUNet model achieved dice of 0.97 and 0.96 in segmenting liver and tumor, respectively. The value of VOE is also reduced to 1.90 and 0.615 for liver and tumor segmentation. CONCLUSION: The proposed ResUNet model performs better than existing methods in the literature. Since the proposed model is built using U-Net, the model ensures quality and precise dimensions of the output.

Facial nerve paresis is a severe complication that arises post-head and neck surgery; This results in articulation problems, facial asymmetry, and severe problems in non-verbal communication. To overcome the side effects of post-surgery facial paralysis, rehabilitation requires which last for several weeks. This paper discusses an unsupervised approach to rehabilitating patients who have temporary facial paralysis due to damage in mimetic muscles. The work aims to make the rehabilitation process objective compared to the current subjective approach, such as House-Brackmann (HB) scale. Also, the approach will assist clinicians by reducing their workload in assessing the improvement during rehabilitation. This paper focuses on the clustering approach to monitor the rehabilitation process. We compare the results obtained from different clustering algorithms on various forms of the same data set, namely dynamic form, data expressed as functional data using B-spline basis expansion, and by finding the functional principal components of the functional data. The study contains data set of 85 distinct patients with 120 measurements obtained using a Kinect stereo-vision camera. The method distinguish effectively between patients with the least and greatest degree of facial paralysis, however patients with adjacent degrees of paralysis provide some challenges. In addition, we compared the cluster results to the HB scale outputs.

We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe. We present details of our algorithm and optimizations done related to data-loading and pre-processing on accelerated hardware. We train our model using binary black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has \\( 6\\) million trainable parameters with training times \\( 24\\) hours. Based on online deployment on a mock data stream of LIGO-Virgo data, Aframe + AMPLFI is able to pick up BBH candidates and infer parameters for real-time alerts from data acquisition with a net latency of \\( 6\\)s.

Background The impact of laparoscopic sleeve gastrectomy (LSG) on various co-morbidities including type II diabetes mellitus, hypertension, and sleep apnea is well established. However, its effect on hypothyroidism has not been given due attention evidenced by the scant literature on the subject. The purpose of this report is to assess the change in thyroxine (T 4 ) requirement in morbidly obese patients with clinical hypothyroidism after LSG. Methods We conducted a retrospective review of morbidly obese patients on T 4 replacement therapy for clinical hypothyroidism who underwent LSG from August 2009 to July 2012 at our institution. Results Of the 200 patients who underwent LSG during this period, 21 (10.5 %) were on T 4 replacement therapy preoperatively for clinical hypothyroidism. Two patients were lost to follow-up. The remaining 19 patients were categorized into two groups. Group 1 comprised 13 patients with decreased T 4 requirements after LSG. Group 2 comprised six patients in whom the T 4 dose remained unaltered. The mean change in T 4 requirement in group 1 was 42.07 % (12–100 %). Group 1 patients had a significantly higher mean preoperative body mass index (48.7 vs. 43.0 kg/m 2 ; p  = 0.03) than the group 2 patients. There was a significant correlation between the percentage excess weight loss and the percentage change in T 4 requirement in group 1 ( r  = 0.607, p  = 0.028). Conclusions Sleeve gastrectomy has a favorable impact on hypothyroid status as seen by a reduction in T 4 requirement in the majority of morbidly obese patients with overt hypothyroidism.