Multifractional Brownian Motion and Quantum-Behaved Partial Swarm Optimization for Bearing Degradation Forecasting

Gradual degradation of the bearing vibration signal is usually studied as a nonstationary stochastic time series. Roller bearings are working at high speed in a heavy load environment so that the combination of bearing faults gradually degraded during the rotation might lead to unpredicted catastrophic accidents. The degradation process has the property of long-range dependence (LRD), so that the fractional Brownian motion (fBm) is taken into account for a prediction model. Because of the dramatic changes in the bearing degradation process, the Hurst exponent that describes the fBm will change during the degradation process. A priori Hurst value of the conventional fBm in the prediction is fixed, thus inducing a minor accuracy of the prediction. To avoid this problem, we propose an improved prediction method. Based on the following steps, at the initial data processing, a skip-over factor is selected as the characteristics parameter of the bearing degradation process. A multifractional Brownian motion (mfBm) replaces the fBm for the degradation modeling. We will show that also our mfBm has the same property of long-range dependence as the fBm. Moreover, a time-varying Hurst exponent H(t) is taken to replace the constant H in fBm. Finally, we apply the quantum-behaved partial swarm optimization (QPSO) to optimize H(t) for a finite interval. Some tests and corresponding experimental results will show that our model QPSO + mfBm have a much better performance on the prediction effect than fBm

SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation

As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target dataset, is gaining increasing popularity. While extensive studies have been devoted to improving the model accuracy on target domain, an important issue of model robustness is neglected. To make things worse, conventional adversarial training (AT) methods for improving model robustness are inapplicable under UDA scenario since they train models on adversarial examples that are generated by supervised loss function. In this paper, we present a new meta self-training pipeline, named SRoUDA, for improving adversarial robustness of UDA models. Based on self-training paradigm, SRoUDA starts with pre-training a source model by applying UDA baseline on source labeled data and taraget unlabeled data with a developed random masked augmentation (RMA), and then alternates between adversarial target model training on pseudo-labeled target data and finetuning source model by a meta step. While self-training allows the direct incorporation of AT in UDA, the meta step in SRoUDA further helps in mitigating error propagation from noisy pseudo labels. Extensive experiments on various benchmark datasets demonstrate the state-of-the-art performance of SRoUDA where it achieves significant model robustness improvement without harming clean accuracy. Code is available at https://github.com/Vision.Comment: This paper has been accepted for presentation at the AAAI202

Cancer burden in China: a Bayesian approach

BACKGROUND Cancer is a serious health issue in China, but accurate national counts for cancer incidence are not currently available. Knowledge of the cancer burden is necessary for national cancer control planning. In this study, national death survey data and cancer registration data were used to calculate the cancer burden in China using a Bayesian approach. METHODS Cancer mortality and incidence rates for 2004-2005 were obtained from the National Cancer Registration database. The third National Death Survey (NDS), 2004-2005 database provided nationally representative cancer mortality rates. Bayesian modeling methods were used to estimate mortality to incidence (MI) ratios from the registry data and national incidence from the NDS for specific cancer types by age, sex and urban or rural location. RESULTS The total estimated incident cancer cases in 2005 were 2,956,300 (1,762,000 males, 1,194,300 females). World age standardized incidence rates were 236.2 per 100,000 in males and 168.9 per 100,000 in females in urban areas and 203.7 per 100,000 and 121.8 per 100,000 in rural areas. CONCLUSIONS MI ratios are useful for estimating national cancer incidence in the absence of representative incidence or survival data. Bayesian methods provide a flexible framework for smoothing rates and representing statistical uncertainty in the MI ratios. Expansion of China's cancer registration network to be more representative of the country would improve the accuracy of cancer burden estimates.This study used the data from National Central Cancer Registry database. The authors acknowledge the contributions of local cancer registries providing registration data and working group of the Third National Death Survey

Molecular characterization and phylogenetic analysis of one omega-gliadin gene from aegilops speltoides l.

Gliadins, as the major components of wheat storage proteins, determine the extensibility properties of dough and have important effects on flour processing quality. Wheat related species carries potential storage protein gene resources for quality improvement. In this study, we isolated and characterized the first complete omega-gliadin gene Omega-AS from Aegilops speltoides L. (2n = 2x = 14, SS) by allelic-specific PCR and investigated its phylogenetic relationships among Triticum and Aegilops species. Molecular structure showed that Omega-AS gene consisted of 1122 bp encoding 373 amino acid residues with deduced molecular mass 41379.21 Da. Omega-AS gene was exceptionally rich in prolines and glutamines with fewer methionine and no cysteine. Sequence characterization and epitope analysis showed that three epitopes QQPIPVQPQQ, TQPQQPTPIQ and IQPQQPFPQQ were absent in Omega-AS gene encoded protein, indicating its potential value for wheat quality improvement with less toxic, or no toxic peptides. Phylogenetic analysis revealed that Omega-AS was closely related to gliadin genes of wheat and related species and its divergence from bread wheat was more recently (less than 1.243 MYA). Heterologous expression showed that Omega-AS gene could successfully express with a high level in E. coli under the control of T-7 promoter. The transcription expression pattern of Omega AS gene during grain development detected by qRT-PCR revealed that the highest expression level occurred at 17 days post anthesis

Data-Centric Financial Large Language Models

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. LLMs have difficulty reasoning about and integrating all relevant information. We propose a data-centric approach to enable LLMs to better handle financial tasks. Our key insight is that rather than overloading the LLM with everything at once, it is more effective to preprocess and pre-understand the data. We create a financial LLM (FLLM) using multitask prompt-based finetuning to achieve data pre-processing and pre-understanding. However, labeled data is scarce for each task. To overcome manual annotation costs, we employ abductive augmentation reasoning (AAR) to automatically generate training data by modifying the pseudo labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR substantially outperforms baseline financial LLMs designed for raw text, achieving state-of-the-art on financial analysis and interpretation tasks. We also open source a new benchmark for financial analysis and interpretation. Our methodology provides a promising path to unlock LLMs' potential for complex real-world domains

Altered serum human cytomegalovirus microRNA levels are common and closely associated with the inflammatory status in patients with fever

BackgroundFever has a complicated etiology, and diagnosing its causative factor is clinically challenging. Human cytomegalovirus (HCMV) infection causes various diseases. However, the clinical relevance, prevalence, and significance of HCMV microRNAs (miRNA) in association with fever remain unclear. In the present study, we analyzed the HCMV miRNA expression pattern in the serum of patients with fever and evaluate its clinical associations with occult HCMV infection status in immune disorders.MethodsWe included serum samples from 138 patients with fever and 151 age-gender-matched controls in this study. First, the serum levels of 24 HCMV miRNAs were determined using a hydrolysis probe-based stem-loop quantitative reverse transcription polymerase chain reaction (RT-qPCR) assay in the training set. The markedly altered miRNAs were verified in the validation and testing sets. The serum HCMV IgG/IgM and DNA titers in the testing cohort were also assessed using enzyme-linked immunosorbent assay (ELISA) and RT-qPCR, respectively.ResultsThe majority of HCMV miRNAs were markedly upregulated in the serum of fever patients. We selected the five most significantly altered HCMV miRNAs: hcmv-miR-US4-3p, hcmv-miR-US29-3p, hcmv-miR-US5-2-3p, hcmv-miR-UL112-3p, and hcmv-miR-US33-3p for validation. These miRNAs were also significantly elevated in the serum of fever patients in the validation and testing sets compared with the controls. Logistic regression analysis revealed that the five miRNAs were novel potential risk factors for fever. Notably, the serum levels of four of the five confirmed HCMV miRNAs were significantly associated with blood C-reaction protein concentrations. Moreover, the five HCMV miRNA levels were closely correlated with the HCMV DNA titers in the testing cohort.ConclusionHCMV infection and activation are common in fever patients and could be novel risk factors for fever. These differentially expressed HCMV miRNAs could enable HCMV activation status monitoring in immune disorders

ATPT: Automate Typhoon Contingency Plan Generation from Text

Artificial intelligence (AI) planning models play an important role in decision support systems for disaster management e.g. typhoon contingency plan development. However, constructing an AI planning model always requires significant amount of manual effort, which becomes a bottleneck to emergency response in a time-critical situation. In this demonstration, we present a framework of automating a domain model of planning domain definition language from natural language input through deep learning techniques. We implement this framework in a typhoon response system and demonstrate automatic generation of typhoon contingency plan from official typhoon plan documents

Effects of perioperative blood transfusion in gastric cancer patients undergoing gastrectomy: A systematic review and meta-analysis

BackgroundThe short-term and long-term effects of perioperative blood transfusion (PBT) on patients with gastric cancer are still intriguing. This systematic review and meta-analysis aimed to investigate the effects of blood transfusion on clinical outcomes in patients with gastric cancer undergoing gastrectomy.MethodsWe searched PubMed, Web of Science, Embase, and The Cochrane Library on December 31th 2021. The main outcomes were overall survival (OS), disease-free survival (DFS), disease-specific survival (DFS), and postoperative complications. A fixed or random-effects model was used to calculate the hazard ratio (HR) with 95% confidence intervals (CIs).ResultsFifty-one studies with a total of 41,864 patients were included for this review and meta-analysis. Compared with patients who did not receive blood transfusions (NPBT), PBT was associated with worse 5-year OS (HR = 2.39 [95%CI: 2.00, 2.84]; p < 0.001; Multivariate HR = 1.43 [95%CI: 1.24, 1.63]; p < 0. 001), worse 5-year DFS (HR = 2.26 [95%CI: 1.68, 3.05]; p < 0.001; Multivariate HR = 1.45 [95%CI: 1.16, 1.82]; p < 0. 001), and worse 5-year DSS (HR = 2. 23 [95%CI: 1.35, 3.70]; p < 0.001; Multivariate HR = 1.24 [95%CI: 0.96, 1.60]; p < 0.001). Moreover, The PBT group showed a higher incidence of postoperative complications [OR = 2.30 (95%CI:1.78, 2. 97); p < 0.001] than that in the NPBT group, especially grade III-V complications, according to the Clavien-Dindo classification. [OR = 2.50 (95%CI:1.71, 3.63); p < 0.001].ConclusionIn patients who underwent gastrectomy, PBT was associated with negative survival effects (OS, DFS, DSS) and a higher incidence of perioperative complications. However, more research was expected to further explore the impact of PBT. Meanwhile, strict blood transfusion management should be implemented to minimize the use of PBT