Over the last few years, a number of authors have suggested that, under certain circumstances, molecular oxygen (O2) or ozone (O3) generated by abiotic processes may accumulate to detectable ...concentrations in a habitable terrestrial planet's atmosphere, producing so-called "false positives" for life. But the models have occasionally disagreed with each other, with some predicting false positives, and some not, for the same apparent set of circumstances. We show here that photochemical false positives derive either from inconsistencies in the treatment of atmospheric and global redox balance or from the treatment (or lack thereof) of lightning. For habitable terrestrial planets with even trace amounts of atmospheric N2, NO produced by lightning catalyzes the recombination of CO and O derived from CO2 photolysis and should be sufficient to eliminate all reported false positives. Molecular oxygen thus remains a useful biosignature gas for Earth-like extrasolar planets, provided that the planet resides within the conventional liquid water habitable zone and has not experienced distinctly non-Earth-like, irrecoverable water loss.
This work aims at investigating the fracture evolution and energy characteristics of marble subjected to fatigue cyclic loading and confining pressure unloading (FC-CPU) conditions. Although rocks ...under separated fatigue cyclic loading and triaxial unloading conditions have been well studied, little is known about the dependence of the fatigue damage accumulation on the subsequent confining pressure unloading condition that influences the rock fracture behaviors. In this work, the servo-controlled GCTS 2000 rock mechanical system combined with the post-test X-ray computed tomography (CT) scanning technique were used to reveal the fracture behaviors of the marble samples. The samples were tested at three stages: the static loading stage, the fatigue cyclic loading stage, and the confining pressure unloading stage. Results show that the damage index-cycle number curve shows a different pattern—the damage increasing rate is different for the samples experiencing different fatigue damage. The damage accumulation at the fatigue cyclic stage influences the final failure mode and energy conversion. In addition, post-test CT scanning further reveals the effects of fatigue cycles on the crack pattern, as well as the stimulated crack scale and density after FC-CPU testing depending on the fatigue cycle. Furthermore, the stored elastic energy decreases and the dissipated energy increases with increasing fatigue cycle at the fatigue loading stage, and the energy conversion is consistent with the crack pattern analysis. By investigating the failure mechanism of marble under FC-CPU conditions, a theoretical basis for rock dynamic disaster prediction can be created.
•POD/ROM is introduced to LSTM for reduction of large training datasets.•A integrated LSTM-ROM framework for predictive and prescriptive analytics of floods.•A first LSTM-ROM for spatio-temporal ...flood prediction.•Using LSTM-ROM the CPU cost is reduced by several orders of magnitude.•Predictive results from the LSTM-ROM and full model are in a good agreement.
Recently accrued attention has been given to machine learning approaches for flooding prediction. However, most of these studies focused mainly on time-series flooding prediction at specified sensors, rarely on spatio-temporal prediction of inundations. In this work, an integrated long short-term memory (LSTM) and reduced order model (ROM) framework has been developed. This integrated LSTM-ROM has the capability of representing the spatio-temporal distribution of floods since it takes advantage of both ROM and LSTM. To reduce the dimensional size of large spatial datasets in LSTM, the proper orthogonal decomposition (POD) and singular value decomposition (SVD) approaches are introduced. The LSTM training and prediction processes are carried out over the reduced space. This leads to an improvement of computational efficiency while maintaining the accuracy. The performance of the LSTM-ROM developed here has been evaluated using Okushiri tsunami as test cases. The results obtained from the LSTM-ROM have been compared with those from the full model (Fluidity). In predictive analytics, it is shown that the results from both the full model and LSTM-ROM are in a good agreement whilst the CPU cost using the LSTM-ROM is decreased by three orders of magnitude compared to full model simulations. Additionally, prescriptive analytics has been undertaken to estimate the uncertainty in flood induced conditions. Given the time series of the free surface height at a specified detector, the corresponding induced wave conditions along the coastline have then been provided using the LSTM network. Promising results indicate that the use of LSTM-ROM can provide the flood prediction in seconds, enabling us to provide real-time predictions and inform the public in a timely manner, reducing injuries and fatalities.
Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging ...review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging.
This retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma (
= 111), ependymoma (
= 70), and pilocytic astrocytoma (
= 107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation.
For 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method χ
score and the Generalized Linear Model classifier achieved a test micro-averaged area under the curve of 0.92 with an accuracy of 0.74. Tree-Based Pipeline Optimization Tool models achieved significantly higher accuracy than average qualitative expert MR imaging review (0.83 versus 0.54,
< .001). For binary classification, Tree-Based Pipeline Optimization Tool models achieved an area under the curve of 0.94 with an accuracy of 0.85 for medulloblastoma versus nonmedulloblastoma, an area under the curve of 0.84 with an accuracy of 0.80 for ependymoma versus nonependymoma, and an area under the curve of 0.94 with an accuracy of 0.88 for pilocytic astrocytoma versus non-pilocytic astrocytoma.
Automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.
A central goal of computer graphics is to provide tools for designing and simulating real or imagined artifacts. An understanding of functionality is important in enabling such modeling tools. Given ...that the majority of man‐made artifacts are designed to serve a certain function, the functionality of objects is often reflected by their geometry, the way that they are organized in an environment, and their interaction with other objects or agents. Thus, in recent years, a variety of methods in shape analysis have been developed to extract functional information about objects and scenes from these different types of cues. In this report, we discuss recent developments that incorporate functionality aspects into the analysis of 3D shapes and scenes. We provide a summary of the state‐of‐the‐art in this area, including a discussion of key ideas and an organized review of the relevant literature. More specifically, the report is structured around a general definition of functionality from which we derive criteria for classifying the body of prior work. This definition also facilitates a comparative view of methods for functionality analysis. We focus on studying the inference of functionality from a geometric perspective, and pose functionality analysis as a process involving both the geometry and interactions of a functional entity. In addition, we discuss a variety of applications that benefit from an analysis of functionality, and conclude the report with a discussion of current challenges and potential future works.
A novel approach is proposed to demonstrate the two-photon Breit-Wheeler process by using collimated and wide-bandwidth γ-ray pulses driven by 10-PW lasers. Theoretical calculations suggest that more ...than 3.2×10^{8} electron-positron pairs with a divergence angle of 7° can be created per shot, and the signal-to-noise ratio is higher than 10^{3}. The positron signal, which is roughly 100 times higher than the detection limit, can be measured by using the existing spectrometers. This approach, which could demonstrate the e^{-}e^{+} pair creation process from two photons, would provide important tests for two-photon physics and other fundamental physical theories.
Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present "Next Day Wildfire Spread," a curated, large-scale, multivariate dataset of historical ...wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire datasets based on Earth observation satellites, our dataset combines 2-D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, and population density) aligned over 2-D regions, providing a feature-rich dataset for machine learning. To demonstrate the usefulness of this dataset, we implement a neural network that takes advantage of the spatial information of these data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This dataset can be used as a benchmark for developing wildfire propagation models based on remote-sensing data for a lead time of one day.
Body mass index (BMI) has been associated with the risk of oesophageal cancer. But the influence of BMI on postoperative complication and prognosis has always been controversial.
In total, 2031 ...consecutive patients who underwent oesophagectomy between 1998 and 2008 were classified according to Asian-specific BMI (kg m(-2)) cutoff values. The impact of BMI on overall survival (OS) was estimated using the Kaplan-Meier method and Cox proportional hazard models. We performed a meta-analysis to examine the association of BMI with OS and postoperative complication.
Patients with higher BMI had more postoperative complication (P=0.002), such as anastomotic leakage (P=0.016) and cardiovascular diseases (P<0.001), but less incidence of chylous leakage (P=0.010). Logistic regression analysis showed that BMI (P=0.005) was a confounding factor associated with postoperative complication. Multivariate analysis showed that overweight and obese patients had a more favourable survival than normal weight patients (HR (hazard ratio) = 0.80, 95% CI (confidence interval): 0.70-0.92, P=0.001). Subgroup analysis showed that the association with higher BMI and increased OS was observed in patients with oesophageal squamous cell carcinoma (ESCC) (P<0.001), oesophageal adenocarcinoma (EA) (P=0.034), never-smoking (P=0.035), ever-smoking (P=0.035), never alcohol consumption (P=0.005), weight loss (P=0.003) and advanced pathological stage (P<0.001). The meta-analysis further corroborated that higher BMI was associated with increased complication of anastomotic leakage (RR (risk ratio)=1.04, 95% CI: 1.02-1.06, P=0.001), wound infection (RR=1.03, 95% CI: 1.00-1.05, P=0.031) and cardiovascular diseases (RR=1.02, 95% CI: 1.00-1.05, P=0.039), but decreased incidence of chylous leakage (RR=0.98, 95% CI: 0.96-0.99, P<0.001). In addition, high BMI could significantly improved OS (HR=0.78, 95% CI: 0.71-0.85, P<0.001).
Preoperative BMI was an independent prognostic factor for survival, and strongly associated with postoperative complications in oesophageal cancer.
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