Statistical descriptions of earthquakes offer important probabilistic information, and newly emerging technologies of high-precision observations and machine learning collectively advance our ...knowledge regarding complex earthquake behaviors. Still, there remains a formidable knowledge gap for predicting individual large earthquakes' locations and magnitudes. Here, this study shows that the individual large earthquakes may have unique signatures that can be represented by new high-dimensional features-Gauss curvature-based coordinates. Particularly, the observed earthquake catalog data are transformed into a number of pseudo physics quantities (i.e., energy, power, vorticity, and Laplacian) which turn into smooth surface-like information via spatio-temporal convolution, giving rise to the new high-dimensional coordinates. Validations with 40-year earthquakes in the West U.S. region show that the new coordinates appear to hold uniqueness for individual large earthquakes (Formula: see text), and the pseudo physics quantities help identify a customized data-driven prediction model. A Bayesian evolutionary algorithm in conjunction with flexible bases can identify a data-driven model, demonstrating its promising reproduction of individual large earthquake's location and magnitude. Results imply that an individual large earthquake can be distinguished and remembered while its best-so-far model can be customized by machine learning. This study paves a new way to data-driven automated evolution of individual earthquake prediction.
Abstract
Predicting individual large earthquakes (EQs)’ locations, magnitudes, and timing remains unreachable. The author’s prior study shows that individual large EQs have unique signatures obtained ...from multi-layered data transformations. Via spatio-temporal convolutions, decades-long EQ catalog data are transformed into pseudo-physics quantities (e.g., energy, power, vorticity, and Laplacian), which turn into surface-like information via Gauss curvatures. Using these new features, a rule-learning machine learning approach unravels promising prediction rules. This paper suggests further data transformation via Fourier transformation (FT). Results show that FT-based new feature can help sharpen the prediction rules. Feasibility tests of large EQs (
$$M\ge$$
M
≥
6.5) over the past 40 years in the western U.S. show promise, shedding light on data-driven prediction of individual large EQs. The handshake among ML methods, Fourier, and Gauss may help answer the long-standing enigma of seismogenesis.
▶ Treadmill exercise ameliorates cognitive deficits in Tg mice. ▶ Treadmill exercise reduces Aβ-42 and tau deposition in Tg mice. ▶ Treadmill exercise reduces the number of TUNEL-positive cells in Tg ...mice. ▶ Treadmill exercise reduces TC, insulin, glucose, and corticosterone levels in Tg mice. ▶ Treadmill exercise may be beneficial in prevention or treatment in AD.
The present study was undertaken to further investigate the protective effect of treadmill exercise on the hippocampal proteins associated with neuronal cell death in an aged transgenic (Tg) mice with Alzheimer's disease (AD). To address this, Tg mouse model of AD, Tg-NSE/PS2m, which expresses human mutant PS2 in the brain, was chosen. Animals were subjected to treadmill exercise for 12 weeks from 24 months of age. The exercised mice were treadmill run at speed of 12
m/min, 60
min/day, 5 days/week on a 0% gradient for 3 months. Treadmill exercised mice improved cognitive function in water maze test. Treadmill exercised mice significantly reduced the expression of Aβ-42, Cox-2, and caspase-3 in the hippocampus. In parallel, treadmill exercised Tg mice decreased the phosphorylation levels of JNK, p38MAPK and tau (Ser404, Ser202, Thr231), and increased the phosphorylation levels of ERK, PI3K, Akt and GSK-3α/β. In addition, treadmill exercised Tg mice up-regulated the expressions of NGF, BDNF and phospho-CREB, and the expressions of SOD-1, SOD-2 and HSP-70. Treadmill exercised Tg mice up-regulated the expression of Bcl-2, and down-regulated the expressions of cytochrome c and Bax in the hippocampus. The number of TUNEL-positive cells in the hippocampus in mice was significantly decreased after treadmill exercise. Finally, serum TC, insulin, glucose, and corticosterone levels were significantly decreased in the Tg mice after treadmill exercise. As a consequence of such change, Aβ-dependent neuronal cell death in the hippocampus of Tg mice was markedly suppressed following treadmill exercise. These results strongly suggest that treadmill exercise provides a therapeutic potential to inhibit both Aβ-42 and neuronal death pathways. Therefore, treadmill exercise may be beneficial in prevention or treatment of AD.
Atherosclerosis is an inflammatory process, and inflammatory biomarkers have been identified as useful predictors of clinical outcomes. The prognostic value of leukocyte count in patients with ...ST-segment elevation myocardial infarctions who undergo primary percutaneous coronary intervention is not clearly defined. In 325 patients with STEMIs treated with primary percutaneous coronary intervention, total and differential leukocyte counts, once at admission and 24 hours thereafter, were measured. The neutrophil/lymphocyte ratio (NLR) was calculated as the ratio of neutrophil count to lymphocyte count. The primary end point was all-cause death. Twenty-five patients (7.7%) died during follow-up (median 1,092 days, interquartile range 632 to 1,464). The total leukocyte count decreased (from 11,853 ± 3,946/μl to 11,245 ± 3,979/μl, p = 0.004) from baseline to 24 hours after admission. Patients who died had higher neutrophil counts (9,887 ± 5,417/μl vs 8,399 ± 3,639/μl, p = 0.061), lower lymphocyte counts (1,566 ± 786/μl vs 1,899 ± 770/μl, p = 0.039), and higher NLRs (8.58 ± 7.41 vs 5.51 ± 4.20, p = 0.001) at 24 hours after admission. Baseline leukocyte profile was not associated with outcomes. The best cut-off value of 24-hour NLR to predict mortality was 5.44 (area under the curve 0.72, 95% confidence interval CI 0.52 to 0.82). In multivariate analysis, a 24-hour NLR ≥5.44 was an independent predictor of mortality (hazard ratio 3.12, 95% CI 1.14 to 8.55), along with chronic kidney disease (hazard ratio 4.23, 95% CI 1.62 to 11.1) and the left ventricular ejection fraction (hazard ratio 0.94 for a 3% increase, 95% CI 0.76 to 0.93). In conclusion, NLR at 24 hours after admission can be used for risk stratification in patients with STEMIs who undergo primary PCI. Patients with STEMIs with 24-hour NLRs ≥5.44 are at increased risk for mortality and should receive more intensive treatment.
Summary
There exists a deep chasm between machine learning (ML) and high‐fidelity computational material models in science and engineering. Due to the complex interaction of internal physics, ML ...methods hardly conquer or innovate them. To fill the chasm, this paper finds an answer from the central notions of deep learning (DL) and proposes information index and link functions, which are essential to infuse principles of physics into ML. Like the convolution process of DL, the proposed information index integrates adjacent information and quantifies the physical similarity between laboratory and reality, enabling ML to see through a complex target system with the perspective of scientists. Like the hidden layers' weights of DL, the proposed link functions unravel the hidden relations between information index and physics rules. Like the error backpropagation of DL, the proposed framework adopts fitness‐based spawning scheme of evolutionary algorithm. The proposed framework demonstrates that a fusion of information index, link functions, evolutionary algorithm, and Bayesian update scheme can engender self‐evolving computational material models and that the fusion will help rename ML as a partner of researchers in the broad science and engineering.
•A study on convergence of Blockchain-AI for sustainable smart city.•Presents the security issues and challenges based on various dimensions.•Discusses the blockchain security enhancement solutions, ...and summarizing key points.•Summarize the open issues and research direction: new security suggestions, future guidelines.
In the digital era, the smart city can become an intelligent society by utilizing advances in emerging technologies. Specifically, the rapid adoption of blockchain technology has led a paradigm shift to a new digital smart city ecosystem. A broad spectrum of blockchain applications promise solutions for problems in areas ranging from risk management and financial services to cryptocurrency, and from the Internet of Things (IoT) to public and social services. Furthermore, the convergence of Artificial Intelligence (AI) and blockchain technology is revolutionizing the smart city network architecture to build sustainable ecosystems. However, these advancements in technologies bring both opportunities and challenges when it comes to achieving the goals of creating a sustainable smart cities. This paper provides a comprehensive literature review of the security issues and problems that impact the deployment of blockchain systems in smart cities. This work presents a detailed discussion of several key factors for the convergence of Blockchain and AI technologies that will help form a sustainable smart society. We discuss blockchain security enhancement solutions, summarizing the key points that can be used for developing various blockchain-AI based intelligent transportation systems. Also, we discuss the issues that remain open and our future research direction, this includes new security suggestions and future guidelines for a sustainable smart city ecosystem.
Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed ...discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-based prediction algorithms. This study included 222,998 Korean adults aged 40-79 years, naïve to lipid-lowering therapy, had no history of cardiovascular disease. Pre-existing models showed moderate to good discrimination in predicting future cardiovascular events (C-statistics 0.70-0.80). Pooled cohort equation (PCE) specifically showed C-statistics of 0.738. Among other machine learning models such as logistic regression, treebag, random forest, and adaboost, the neural network model showed the greatest C-statistic (0.751), which was significantly higher than that for PCE. It also showed improved agreement between the predicted risk and observed outcomes (Hosmer-Lemeshow χ
= 86.1, P < 0.001) than PCE for whites did (Hosmer-Lemeshow χ
= 171.1, P < 0.001). Similar improvements were observed for Framingham risk score, systematic coronary risk evaluation, and QRISK3. This study demonstrated that machine learning-based algorithms could improve performance in cardiovascular risk prediction over contemporary cardiovascular risk models in statin-naïve healthy Korean adults without cardiovascular disease. The model can be easily adopted for risk assessment and clinical decision making.
The growing demand for human-independent comfortable lifestyle has emboldened the development of smart home. A typical keenly intellective home includes many Internet of things contrivances that ...engender processes and immensely colossal data to efficiently handle its users’ demands. This incrementing demand raises a plethora of concern cognate to a smart home system in terms of scalability, efficiency, and security. All these issues are tedious to manage, and the existing studies lack the granularity for surmounting them. Considering such a requisite of security and efficiency as a quandary at hand, this article presents a secure and efficient smart home architecture, which incorporates the blockchain and the cloud computing technologies for a cumulated solution. Because of the decentralized nature of blockchain technology, it can serve the processing services and make the transaction copy of the collected sensible user data from smart home. To ensure the security of smart home network, our proposed model utilizes the multivariate correlation analysis technique to analyze the network traffic and identify the correlation between traffic features. We have evaluated the performance of our proposed architecture using different parameters like throughput and discovered that blockchain is an efficient security solution for the future Internet of things network.
The 2017 American College of Cardiology/American Heart Association (ACC/AHA) hypertension guideline lowered the threshold defining hypertension and treatment target from 140/90 mmHg to 130/80 mmHg. ...We compared the 2017 ACC/AHA guideline and the Eighth Joint National Committee (JNC8) report with regard to the current status of hypertension using the Korean National Health and Nutrition Examination Survey. The association between blood pressure (BP) control and long-term major cardiovascular outcomes (MACEs) was analyzed using the Korea National Health Insurance Service cohort. In the cross-sectional study with 15,784 adults, the prevalence of hypertension was expected to be 49.2 ± 0.6% based on the definition suggested by the 2017 ACC/AHA guideline versus 30.4 ± 0.6% based on the JNC8 report. In a longitudinal analysis with 373,800 hypertensive adults for the median follow-up periods of 11.0 years, the adults meeting the target goal BP goal of 2017 ACC/AHA guideline were associated with 21% reduced risk of MACEs compared with adults, not meeting 2017 ACC/AHA BP goal but meeting JNC8 target goal. In conclusion, substantial increase of prevalence of hypertension is expected by the 2017 ACC/AHA guideline. This study also suggests endorsing the aggressive approach would lead to an improvement in cardiovascular care.
Metal oxides have attracted considerable interest due to their distinguished electrochemical properties and applications in multiple fields such as supercapacitors and solar cells. It is beneficial ...to exploit V2O5 electrode materials with desired structures as well as potential applications due to their low-cost, low toxicity, wide voltage windows and multiple oxidation states. A facile hydrothermal method for synthesizing V2O5 nanorods using ammonium metavanadate with an acidic reducing agent at 200 °C is reported. The surface morphology, crystallinity and functional group modifications of the nanorods are analyzed by scanning electron microscopy, transmission electron microscopy, and energy dispersive X-ray analysis. V2O5 nanorods on stainless steel (SS) plate architecture exhibit an outstanding electrochemical performance in supercapacitors with high areal specific capacitance of 417.4 mF cm−2 at a scan rate of 5 mV s−1, excellent rate capability, and good cycling stability for 5000 cycles in 0.5 M sodium sulfate in comparison to the observations in 0.5 M sulfuric acid and 0.5 M KCl electrolytes. Moreover, a three electrode setup is used to scrutinize the electrochemical performance of the V2O5-nanorod electrode; it shows superior performance in terms of high areal specific capacitance, which is the highest reported value so far, and it also shows long cycling stability. Our study demonstrates that the as-fabricated V2O5 nanorods can be applied in both high energy density fields and high power density applications such as flexible electronics, electric vehicles and energy storage devices.
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