Clustering Algorithms for Time Series Gene Expression in Microarray Data

Clustering techniques are important for gene expression data analysis. However, efficient computational algorithms for clustering time-series data are still lacking. This work documents two improvements on an existing profile-based greedy algorithm for short time-series data; the first one is implementation of a scaling method on the pre-processing of the raw data to handle some extreme cases; the second improvement is modifying the strategy to generate better clusters. Simulation data and real microarray data were used to evaluate these improvements; this approach could efficiently generate more accurate clusters. A new feature-based algorithm was also developed in which steady state value; overshoot, rise time, settling time and peak time are generated by the 2nd order control system for the clustering purpose. This feature-based approach is much faster and more accurate than the existing profile-based algorithm for long time-series data

CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems

In large-scale recommender systems, retrieving top N relevant candidates accurately with resource constrain is crucial. To evaluate the performance of such retrieval models, Recall@N, the frequency of positive samples being retrieved in the top N ranking, is widely used. However, most of the conventional loss functions for retrieval models such as softmax cross-entropy and pairwise comparison methods do not directly optimize Recall@N. Moreover, those conventional loss functions cannot be customized for the specific retrieval size N required by each application and thus may lead to sub-optimal performance. In this paper, we proposed the Customizable Recall@N Optimization Loss (CROLoss), a loss function that can directly optimize the Recall@N metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases. Furthermore, we develop the Lambda method, a gradient-based method that invites more flexibility and can further boost the system performance. We evaluate the proposed CROLoss on two public benchmark datasets. The results show that CROLoss achieves SOTA results over conventional loss functions for both datasets with various choices of retrieval size N. CROLoss has been deployed onto our online E-commerce advertising platform, where a fourteen-day online A/B test demonstrated that CROLoss contributes to a significant business revenue growth of 4.75%.Comment: 9 pages, 5 figures. Accepted by by CIKM 202

Iterative Learning Control of Hysteresis in Piezoelectric Actuators

We develop convergence criteria of an iterative learning control on the whole desired trajectory to obtain the hysteresis-compensating feedforward input in hysteretic systems. In the analysis, the Prandtl-Ishlinskii model is utilized to capture the nonlinear behavior in piezoelectric actuators. Finally, we apply the control algorithm to an experimental piezoelectric actuator and conclude that the tracking error is reduced to 0.15% of the total displacement, which is approximately the noise level of the sensor measurement

CFD modeling and simulation of PEM fuel cell using OpenFOAM

A proton exchange membrane (PEM) fuel cell is an electrolytic cell that converts chemical energy of hydrogen reacting with oxygen into electrical energy. To meet increasingly stringent application needs, improved performance and increased efficiency are paramount. Computational fluid dynamics (CFD) is an ideal means for achieving these improvements. In this paper, a comprehensive CFD-based tool that can accurately simulate the major transport phenomena which take place within a PEM fuel cell is presented. The tool is developed using OpenFOAM and it can be used to rapidly gain insights into the cell working processes. The base case results are compared with previous model results and experimental data. The present I-V curve shows better agreement with the experimental trend at low current densities. The simulation data also indicate that the chosen concentration constant has very significant impact on the concentration overpotential

Three-dimensional multiphase flow computational fluid dynamics models for proton exchange membrane fuel cell: a theoretical development

A review of published three-dimensional, computational fluid dynamics models for proton exchange membrane fuel cells that accounts for multiphase flow is presented. The models can be categorized as models for transport phenomena, geometry or operating condition effects, and thermal effects. The influences of heat and water management on the fuel cell performance have been repeatedly addressed, and these still remain two central issues in proton exchange membrane fuel cell technology. The strengths and weaknesses of the models, the modelling assumptions, and the model validation are discussed. The salient numerical features of the models are examined, and an overview of the most commonly used computational fluid dynamic codes for the numerical modelling of proton exchange membrane fuel cells is given. Comprehensive three-dimensional multiphase flow computational fluid dynamic models accounting for the major transport phenomena inside a complete cell have been developed. However, it has been noted that more research is required to develop models that include among other things, the detailed composition and structure of the catalyst layers, the effects of water droplets movement in the gas flow channels, the consideration of phase change in both the anode and the cathode sides of the fuel cell, and dissolved water transport

Isolated angiitis of the central nervous system with tumor-like lesion, mimicking brain malignant glioma: a case report and review of the literature

<p>Abstract</p> <p>Background</p> <p>Isolated angiitis of the central nervous system (IACNS) is a rare but severe vascular disease, which could present like an isolated inflammatory lesion on magnetic resonance imaging (MRI). To date, only a few such cases with tumor-like IACNS have been reported.</p> <p>Case Presentation</p> <p>A 35-year-old woman presented with headache and left-sided weakness. MRI scans initially mislead us to a diagnosis of glioblastoma (GBM). Surgery was performed. The mass was sub-totally resected. Pathological examination confirmed a cerebral vasculitis. Radiological features, such as disproportionate mass effect, striped hemorrhage and abnormal enhancement of adjacent vessels, could be helpful to distinguish a tumor-like IACNS from a GBM. Single therapy with high doses of steroid did not improve the patient's condition. Combined therapy with prednisolone and cyclophosphamide showed great benefit to the patient. No relapse occurred during the period of 18 months follow-up.</p> <p>Conclusions</p> <p>Although a tumor-like IACNS has no established imaging features, a diagnosis of tumor-like IACNS should be suspected when MRI shows inappropriate presentations of a tumor. Greater awareness of this potential manifestation of IACNS may facilitate more prompt diagnosis and treatment.</p

Hydrogeochemical characteristics and genesis of Hongshuilantang Hot Spring and its water temperature anomalies during the Rushan earthquake swarm in Eastern China

Water temperatures of hot springs close to tectonic fault zones often show some variations before earthquakes, and analyses of earthquake precursors in hot springs have significant referential meaning for earthquake monitoring and forecasting. This study measured the concentration of major ions in water from the Hongshuilantang Hot Spring in 2017 and 2020. The ion composition was classified by hydrochemistry into the HCO3·SO4-Na chemical type. The composition of hydrogen and oxygen isotopes in the Hongshuilantang Hot Spring were located near the global meteoric water line (GMWL), indicating that the recharge source of the hot spring was meteoric water. The δD and δ18O values were not plotted on the Glogal Meteroric Water Line (GMWL), and there were some deviations, which suggested that hot spring water underwent water–rock interactions. Deep circulation water played an important role during the evolution process of thermal water. Water temperature showed a decreasing trend from October 2013 to June 2015 during the Rushan earthquake swarm in eastern China. Because of the occurrence of the earthquake swarm, we inferred that regional stress in this area began to be released, allowing continuous rebalancing. Free surface water appeared in some aquifers, and the seepage of low-temperature underground water into the upper aquifer led to a drop in water temperature in the hot spring. The Hongshuilantang Hot Spring and the epicenter of the Rushan earthquake swarm were located on the Muping–Jimo seismological fault zone, with the same seismotectonic system and some genesis relationships