Composite fermion theory of rapidly rotating two-dimensional bosons

Ultracold neutral bosons in a rapidly rotating atomic trap have been predicted to exhibit fractional quantum Hall-like states. We describe how the composite fermion theory, used in the description of the fractional quantum Hall effect for electrons, can be applied to interacting bosons. Numerical evidence supporting the formation of composite fermions, each being the bound state of a boson and one flux quantum, is shown for filling fractions of the type nu=p/(p+1), both by spectral analysis and by direct comparison with trial wave functions. The rapidly rotating system of two-dimensional bosons thus constitutes an interesting example of "statistical transmutation," with bosons behaving like composite fermions. We also describe the difference between the electronic and the bosonic cases when p approaches infinity. Residual interactions between composite fermions are attractive in this limit, resulting in a paired composite-fermion state described by the Moore-Read wave function.Comment: 12 pages, 9 figures. Conference proceeding. BEC 2005 Ital

The Gould-Hopper Polynomials in the Novikov-Veselov equation

We use the Gould-Hopper (GH) polynomials to investigate the Novikov-Veselov (NV) equation. The root dynamics of the $\sigma$-flow in the NV equation is studied using the GH polynomials and then the Lax pair is found. In particulr, when $N=3,4,5$, one can get the Gold-fish model. The smooth rational solutions of the NV equation are also constructed via the extended Moutard transformation and the GH polynomials. The asymptotic behavior is discussed and then the smooth rational solution of the Liouville equation is obtained.Comment: 22 pages, no figur

Ensemble Machine Learning for Predicting 90-Day Outcomes and Analyzing Risk Factors in Acute Kidney Injury Requiring Dialysis

Tzu-Hao Wang,1,2 Chih-Chin Kao,3– 5,* Tzu-Hao Chang2,6,* 1Division of General Medicine, Department of Medical Education, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan, Republic of China; 2Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan, Republic of China; 3Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, Republic of China; 4Division of Nephrology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan, Republic of China; 5Taipei Medical University-Research Center of Urology and Kidney (TMU-RCUK), Taipei Medical University, Taipei, Taiwan, Republic of China; 6Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei City, Taiwan, Republic of China*These authors contributed equally to this workCorrespondence: Tzu-Hao Chang, Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, International Center for Health Information Technology, Taipei Medical University, 9F, Education & Research Building, Shuang-Ho Campus, No. 301, Yuantong Road, Zhonghe District, New Taipei City, 235603, Taiwan, Republic of China, Tel +886-66202589 ext.10922, Email [email protected]: Our objectives were to (1) employ ensemble machine learning algorithms utilizing real-world clinical data to predict 90-day prognosis, including dialysis dependence and mortality, following the first hospitalized dialysis and (2) identify the significant factors associated with overall outcomes.Patients and Methods: We identified hospitalized patients with Acute kidney injury requiring dialysis (AKI-D) from a dataset of the Taipei Medical University Clinical Research Database (TMUCRD) from January 2008 to December 2020. The extracted data comprise demographics, comorbidities, medications, and laboratory parameters. Ensemble machine learning models were developed utilizing real-world clinical data through the Google Cloud Platform.Results: The Study Analyzed 1080 Patients in the Dialysis-Dependent Module, Out of Which 616 Received Regular Dialysis After 90 Days. Our Ensemble Model, Consisting of 25 Feedforward Neural Network Models, Demonstrated the Best Performance with an Auroc of 0.846. We Identified the Baseline Creatinine Value, Assessed at Least 90 Days Before the Initial Dialysis, as the Most Crucial Factor. We selected 2358 patients, 984 of whom were deceased after 90 days, for the survival module. The ensemble model, comprising 15 feedforward neural network models and 10 gradient-boosted decision tree models, achieved superior performance with an AUROC of 0.865. The pre-dialysis creatinine value, tested within 90 days prior to the initial dialysis, was identified as the most significant factor.Conclusion: Ensemble machine learning models outperform logistic regression models in predicting outcomes of AKI-D, compared to existing literature. Our study, which includes a large sample size from three different hospitals, supports the significance of the creatinine value tested before the first hospitalized dialysis in determining overall prognosis. Healthcare providers could benefit from utilizing our validated prediction model to improve clinical decision-making and enhance patient care for the high-risk population.Keywords: AKI-D, dialysis prognosis, ensemble machine learning, prediction models, risk factor

Computational identification of riboswitches based on RNA conserved functional sequences and conformations

[[abstract]]Riboswitches are cis-acting genetic regulatory elements within a specific mRNA that can regulate both transcription and translation by interacting with their corresponding metabolites. Recently, an increasing number of riboswitches have been identified in different species and investigated for their roles in regulatory functions. Both the sequence contexts and structural conformations are important characteristics of riboswitches. None of the previously developed tools, such as covariance models (CMs), Riboswitch finder, and RibEx, provide a web server for efficiently searching homologous instances of known riboswitches or considers two crucial characteristics of each riboswitch, such as the structural conformations and sequence contexts of functional regions. Therefore, we developed a systematic method for identifying 12 kinds of riboswitches. The method is implemented and provided as a web server, RiboSW, to efficiently and conveniently identify riboswitches within messenger RNA sequences. The predictive accuracy of the proposed method is comparable with other previous tools. The efficiency of the proposed method for identifying riboswitches was improved in order to achieve a reasonable computational time required for the prediction, which makes it possible to have an accurate and convenient web server for biologists to obtain the results of their analysis of a given mRNA sequence. RiboSW is now available on the web at http://RiboSW.mbc.nctu.edu.tw/

Stepwise frequency tuning of a gyrotron backward-wave oscillator

[[abstract]]The gyrotron backward-wave oscillator (gyro-BWO) features broadband tunability, but ragged tuning curves are frequently observed experimentally. Accordingly, a Ka-band gyro-BWO experiment with external circuit mismatch was conducted to examine its tuning properties at two reflected strengths: one is slightly mismatched (15 dB reflection) and the other can be categorized as matched (30 dB reflection). Stepwise frequency tunings by varying the magnetic field, the beam voltage, and the beam current were observed under mismatched conditions. A self-locking model was introduced using the concept of injection-locking, where the output and reinjected signals tend to form a stable phase relation, favoring certain discrete oscillation frequencies. The observed frequencies agree closely with the calculated frequencies. Smooth tuning curves were also obtained, revealing a remedy for the stepwise tuning of a gyro-BWO.[[fileno]]2010132010017[[department]]物理

Transition of absolute instability from global to local modes in a gyrotron traveling-wave amplifier

[[abstract]]The gyrotron traveling-wave amplifier employing the distributed-loss scheme is capable of very high gain and effective in suppressing the global absolute instabilities. This study systematically characterizes the local absolute instabilities and their transitional behavior. The local absolute instabilities are analyzed using a model that incorporates the penetration of the field from the copper section into the lossy section. The axial modes were characterized from the perspective of beam-wave interaction and were found to share many characteristics with the global modes. The transition from global modes to local modes as the distributed loss increases was demonstrated. The electron transit angle in the copper section, which determines the feedback criterion, governs the survivability of an oscillation. In addition, the oscillation thresholds predicted using this model are more accurate than those obtained using a simplified model[[fileno]]2010132010022[[department]]物理

Causes of dural sinus thrombosis in a Chinese community

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Entropy Projection Curved Gabor with Random Forest and SVM for Face Recognition

In this work, we propose a workflow for face recognition under occlusion using the entropy projection from the curved Gabor filter, and create a representative and compact features vector that describes a face. Despite the reduced vector obtained by the entropy projection, it still presents opportunity for further dimensionality reduction. Therefore, we use a Random Forest classifier as an attribute selector, providing a 97% reduction of the original vector while keeping suitable accuracy. A set of experiments using three public image databases: AR Face, Extended Yale B with occlusion and FERET illustrates the proposed methodology, evaluated using the SVM classifier. The results obtained in the experiments show promising results when compared to the available approaches in the literature, obtaining 98.05% accuracy for the complete AR Face, 97.26% for FERET and 81.66% with Yale with 50% occlusion

The electronic structure of amorphous silica: A numerical study

We present a computational study of the electronic properties of amorphous SiO2. The ionic configurations used are the ones generated by an earlier molecular dynamics simulations in which the system was cooled with different cooling rates from the liquid state to a glass, thus giving access to glass-like configurations with different degrees of disorder [Phys. Rev. B 54, 15808 (1996)]. The electronic structure is described by a tight-binding Hamiltonian. We study the influence of the degree of disorder on the density of states, the localization properties, the optical absorption, the nature of defects within the mobility gap, and on the fluctuations of the Madelung potential, where the disorder manifests itself most prominently. The experimentally observed mismatch between a photoconductivity threshold of 9 eV and the onset of the optical absorption around 7 eV is interpreted by the picture of eigenstates localized by potential energy fluctuations in a mobility gap of approximately 9 eV and a density of states that exhibits valence and conduction band tails which are, even in the absence of defects, deeply located within the former band gap.Comment: 21 pages of Latex, 5 eps figure

Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions

Identifying, and eventually eliminating throughput bottlenecks, is a key means to increase throughput and productivity in production systems. In the real world, however, eliminating throughput bottlenecks is a challenge. This is due to the landscape of complex factory dynamics, with several hundred machines operating at any given time. Academic researchers have tried to develop tools to help identify and eliminate throughput bottlenecks. Historically, research efforts have focused on developing analytical and discrete event simulation modelling approaches to identify throughput bottlenecks in production systems. However, with the rise of industrial digitalisation and artificial intelligence (AI), academic researchers explored different ways in which AI might be used to eliminate throughput bottlenecks, based on the vast amounts of digital shop floor data. By conducting a systematic literature review, this paper aims to present state-of-the-art research efforts into the use of AI for throughput bottleneck analysis. To make the work of the academic AI solutions more accessible to practitioners, the research efforts are classified into four categories: (1) identify, (2) diagnose, (3) predict and (4) prescribe. This was inspired by real-world throughput bottleneck management practice. The categories, identify and diagnose focus on analysing historical throughput bottlenecks, whereas predict and prescribe focus on analysing future throughput bottlenecks. This paper also provides future research topics and practical recommendations which may help to further push the boundaries of the theoretical and practical use of AI in throughput bottleneck analysis