An efficient and effective nonlinear solver in a parallel software for large scale petroleum reservoir simulation

Abstract. We study a parallel Newton-Krylov-Schwarz (NKS) based algorithm for solving large sparse systems resulting from a fully implicit discretization of partial differential equations arising from petroleum reservoir simulations. Our NKS algorithm is designed by combining an inexact Newton method with a rank-2 updated quasi-Newton method. In order to improve the computational efficiency, both DDM and SPMD parallelism strategies are adopted. The effectiveness of the overall algorithm depends heavily on the performance of the linear preconditioner, which is made of a combination of several preconditioning components including AMG, relaxed ILU, up scaling, additive Schwarz, CRPlike(constraint residual preconditioning), Watts correction, Shur complement

Molecular cancer classification using an meta-sample-based regularized robust coding method

Motivation Previous studies have demonstrated that machine learning based molecular cancer classification using gene expression profiling (GEP) data is promising for the clinic diagnosis and treatment of cancer. Novel classification methods with high efficiency and prediction accuracy are still needed to deal with high dimensionality and small sample size of typical GEP data. Recently the sparse representation (SR) method has been successfully applied to the cancer classification. Nevertheless, its efficiency needs to be improved when analyzing large-scale GEP data. Results In this paper we present the meta-sample-based regularized robust coding classification (MRRCC), a novel effective cancer classification technique that combines the idea of meta-sample-based cluster method with regularized robust coding (RRC) method. It assumes that the coding residual and the coding coefficient are respectively independent and identically distributed. Similar to meta-sample-based SR classification (MSRC), MRRCC extracts a set of meta-samples from the training samples, and then encodes a testing sample as the sparse linear combination of these meta-samples. The representation fidelity is measured by the l2-norm or l1-norm of the coding residual. Conclusions Extensive experiments on publicly available GEP datasets demonstrate that the proposed method is more efficient while its prediction accuracy is equivalent to existing MSRC-based methods and better than other state-of-the-art dimension reduction based methods.This article was funded by the National Science Foundation of China on finding tumor-related driver pathway with comprehensive analysis method based on next-generation sequencing data and the dimension reduction of gene expression data based on heuristic method (grant nos. 61474267, 60973153 and 61133010) and the National Institutes of Health (NIH) Grant P01 AG12993 (PI: E. Michaelis). This article has been published as part of BMC Bioinformatics Volume 15 Supplement 15, 2014: Proceedings of the 2013 International Conference on Intelligent Computing (ICIC 2013). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S15

Automated fault detection using deep belief networks for the quality inspection of electromotors

Vibration inspection of electro-mechanical components and systems is an important tool for automated reliable online as well as post-process production quality assurance. Considering that bad electromotor samples are very rare in the production line, we propose a novel automated fault detection method named "Tilear”, based on Deep Belief Networks (DBNs) training only with good electromotor samples. Tilear consctructs an auto-encoder with DBNs, aiming to reconstruct the inputs as closely as possible. Tilear is structured in two parts: training and decision-making. During training, Tilear is trained only with informative features extracted from preprocessed vibration signals of good electromotors, which enables the trained Tilear only to know how to reconstruct good electromotor vibration signal features. In the decision-making part, comparing the recorded signal from test electromotor and the Tilear reconstructed signal, allows to measure how well a recording from a test electromotor matches the Tilear model learned from good electromotors. A reliable decision can be mad

Continuous Multiagent Control using Collective Behavior Entropy for Large-Scale Home Energy Management

With the increasing popularity of electric vehicles, distributed energy generation and storage facilities in smart grid systems, an efficient Demand-Side Management (DSM) is urgent for energy savings and peak loads reduction. Traditional DSM works focusing on optimizing the energy activities for a single household can not scale up to large-scale home energy management problems. Multi-agent Deep Reinforcement Learning (MA-DRL) shows a potential way to solve the problem of scalability, where modern homes interact together to reduce energy consumers consumption while striking a balance between energy cost and peak loads reduction. However, it is difficult to solve such an environment with the non-stationarity, and existing MA-DRL approaches cannot effectively give incentives for expected group behavior. In this paper, we propose a collective MA-DRL algorithm with continuous action space to provide fine-grained control on a large scale microgrid. To mitigate the non-stationarity of the microgrid environment, a novel predictive model is proposed to measure the collective market behavior. Besides, a collective behavior entropy is introduced to reduce the high peak loads incurred by the collective behaviors of all householders in the smart grid. Empirical results show that our approach significantly outperforms the state-of-the-art methods regarding power cost reduction and daily peak loads optimization

Learning transferrable parameters for long-tailed sequential user behavior modeling

National Research Foundation (NRF) Singapore under its AI Singapore Programm

Topologically Optimized Electrodes for Electroosmotic Actuation

Electroosmosis is one of the most used actuation mechanisms for the microfluidics in the current active lab-on-chip devices. It is generated on the induced charged microchannel walls in contact with an electrolyte solution. Electrode distribution plays the key role on providing the external electric field for electroosmosis, and determines the performance of electroosmotic microfluidics. Therefore, this paper proposes a topology optimization approach for the electrodes of electroosmotic microfluidics, where the electrode layout on the microchannel wall can be determined to achieve designer desired microfluidic performance. This topology optimization is carried out by implementing the interpolation of electric insulation and electric potential on the specified walls of microchannels. To demonstrate the capability of this approach, one typical electroosmotic device, i.e., electroosmotic micropump, is modeled with several electrode layouts derived. And this approach permits potential applications in chemicals and biochemistry due to its outstanding capability on determining the performance of electrokinetic microfluidics

Predictive value of MRI-detected extramural vascular invasion in stage T3 rectal cancer patients before neoadjuvant chemoradiation

PURPOSE:We set out to explore the probability of MRI-detected extramural vascular invasion (mr-EMVI) before chemoradiation to predict responses to chemoradiation and survival in stage T3 rectal cancer patients. METHODS:A total of 100 patients with T3 rectal cancer who underwent MRI examination and received neoadjuvant chemoradiation and surgery were enrolled. The correlation between mr-EMVI and other clinical factors were analyzed by chi-square. Logistic regression model was performed to select the potential factors influencing tumor responses to neoadjuvant chemoradiation. A Cox proportional hazards regression model was performed to explore potential predictors of survival.RESULTS:The positive mr-EMVI result was more likely to be present in patients with a higher T3 subgroup (T3a+b = 7.1% vs. T3c+d = 90.1%, P < 0.001) and more likely in patients with mesorectal fascia involvement than in those without MRF (65% vs. 38.8%, P = 0.034). Compared with mr-EMVI (+) patients, more mr-EMVI (-) patients showed a good response (staged ≤ ypT2N0) (odds ratio [OR], 3.020; 95% confidence interval [CI], 1.071–8.517; P = 0.037). In univariate analysis, mr-EMVI (+) (hazard ratio [HR], 5.374; 95% CI, 1.210–23.872; P = 0.027) and lower rectal cancers (HR, 3.326; 95% CI, 1.135–9.743; P = 0.028) were significantly associated with decreased disease-free survival. A positive mr-EMVI status (HR, 5.727; 95% CI, 1.286–25.594; P = 0.022) and lower rectal cancers (HR, 3.137; 95% CI, 1.127–8.729; P = 0.029) also served as prognostic factors related to decreased disease-free survival in multivariate analysis.CONCLUSION:The mr-EMVI status before chemoradiation is a significant prognostic factor and could be used for identifying T3 rectal cancer patients who might benefit from neoadjuvant chemoradiation

Clinical outcome and prognostic factors of patients with non-traumatic angiography-negative subarachnoid hemorrhage

PurposeThe cause of spontaneous subarachnoid hemorrhage (SAH) is unknown in 10% of cases. The aim of this study was to demonstrate the characteristics of patients with angiography-negative subarachnoid hemorrhage (anSAH) and to analyze factors influencing the clinical outcome in patients suffering from anSAH.MethodsA retrospective cohort of 75 patients with anSAH [26 perimesencephalic (pmSAH) and 49 non-perimesencephalic SAH (npmSAH)] admitted between January 2016 and June 2022 was included. We analyzed demographic, clinical data and 6-month functional outcomes. Enter regression analysis was performed to identify factors associated with outcomes.ResultsUnfavorable outcome was achieved in 10 of 75 patients (13.3%). Unfavorable outcome was associated with senior adults (p = 0.008), Hijdra cistern score (HCS) elevation (p = 0.015), long-time lumbar cistern continuous drainage (LCFD; p = 0.029) and hydrocephalus (p = 0.046). The only significant risk factor for unfavorable outcome after npmSAH was the HCS (OR 1.213 (95%CI 1.007–1.462), p = 0.042).ConclusionOur study provides valuable information on both SAH patterns and functional outcome in patients suffering from anSAH and should be taken into consideration during management of these patients

Two case reports: EML4-ALK rearrangement large cell neuroendocrine carcinoma and literature review

Anaplastic lymphoma kinase gene (ALK) rearrangement is present in only approximately 5% of non-small cell lung cancers (NSCLCs) and is scarce in LCNEC patients. The conventional first-line treatment options are chemotherapy combined with immunotherapy or chemotherapy followed by palliative radiotherapy. In this report, we present two cases of metastatic LCNEC with EML4-ALK fusion that were treated with ALK-TKI inhibitors and demonstrated a rapid therapeutic response. Both patients were nonsmoking women who declined cytotoxic chemotherapy, underwent Next-Generation Sequencing (NGS), and confirmed EML4-ALK fusion. They were treated with alectinib as first-line therapy, and the tumors showed significant shrinkage after two months, achieving a PR (defined as a more than 30% decrease in the sum of maximal dimensions). The PFS was 22 months and 32 months, respectively, until the last follow-up. A systematic review of all previously reported cases of LCNEC with ALK mutations identified only 21 cases. These cases were characterized by being female (71.4%), nonsmoking (85.7%), diagnosed at a relatively young age (median age 51.1), and stage IV (89.5%), with an overall response rate (ORR) of 90.5%. PFS and OS were significantly longer than those treated with conventional chemotherapy/immunotherapy. Based on the clinical characteristics and the effective therapeutic outcomes with ALK inhibitors in LCNEC patients with ALK fusion, we recommend routine ALK IHC (economical, affordable, and convenient, but with higher false positives) as a screening method in advanced LCNEC patients, particularly nonsmoking females or those who are not candidates for or unwilling to undergo cytotoxic chemotherapy. Further molecular profiling is necessary to confirm these potential beneficiaries. We suggest TKI inhibitors as the first-line treatment for metastatic LCNEC with ALK fusion. Additional studies on larger cohorts are required to assess the prevalence of ALK gene fusions and their sensitivity to various ALK inhibitors

Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem