Monetary policy, cash holding and corporate investment: evidence from China

This paper uses 13,766 firm-year observations between 2003 and 2013 from China to investigate the effects of monetary policy on corporate investment and the mitigating effects of cash holding. We find that tightening monetary policy reduces corporate investment while cash holdings mitigate such adverse effects. The cash mitigating role is especially significant for financially constrained firms, non-state-owned enterprises (non-SOEs) and those firms located in a less developed financial market. Cash holding also improves investment efficiency when monetary policy is tightening and tightening monetary policy enhances the ‘cash-cash flow’ sensitivity. Our empirical evidence calls for a critical evaluation on the monetary policies implemented in China which are less effective for state-owned enterprises. It also calls for a necessity for local government to further develop regional financial markets to protect vulnerable businesses, such as non-SOEs and financially constrained firms, from external shocks in order to maintain their sustainable growth and competitive advantages

Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm

Landslide is a natural disaster that can easily threaten local ecology, people's lives and property. In this paper, we conduct modelling research on real unidirectional surface displacement data of recent landslides in the research area and propose a time series prediction framework named VMD-SegSigmoid-XGBoost-ClusterLSTM (VSXC-LSTM) based on variational mode decomposition, which can predict the landslide surface displacement more accurately. The model performs well on the test set. Except for the random item subsequence that is hard to fit, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the trend item subsequence and the periodic item subsequence are both less than 0.1, and the RMSE is as low as 0.006 for the periodic item prediction module based on XGBoost\footnote{Accepted in ICANN2023}

A Worksite Occupational Health Clinic-Based Diabetes Mellitus Management Program

This study is an analysis of a workplace diabetes management program offered to employees of a Fortune 100 financial services corporation located in the United States. The 12-month worksite-based educational program was for employees who were at risk for diabetes, had prediabetes, or were diagnosed with diabetes. This employed population, with health benefits, generally had acceptable control of their diabetes at the start of the program. They statistically improved most self-efficacy measures, but improvement in biometric tests at 6 and 12 months were not significantly different from baseline. Mean hemoglobin A1c at baseline, 6 months, and 12 months was 7.2%, 7.2%, and 7.3%, respectively. At 12 months, about 40% of preprogram survey participants completed all screenings and the post-program questionnaire. Disease management programs at the workplace can be an important component in helping employees enhance their knowledge of diabetes and maintain and improve their health. (Population Health Management 2015;18:429?436)Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140190/1/pop.2014.0141.pd

Tuning the detection wavelength of quantum-well infrared photodetectors by single high-energy implantation

Single high-energy (0.9 MeV) proton implantation and rapid thermal annealing was used to tune the spectral response of the quantum-well infrared photodetectors (QWIPs). In addition to the large redshift of the QWIPs’ response wavelength after implantation, either narrowed or broadened spectrum was obtained at different interdiffusion extent. In general, the overall device performance for the low-dose implantation was not significantly degraded. In comparison with the other implantation schemes, this single high-energy implantation is the most effective and simple technique in tuning the wavelength of QWIPs, thus, to achieve the fabrication of multicolor detectors.Partial financial support from Australian Research Council, Hong Kong Research Grants Council, and the Australian Agency for International Development ~AusAID! through IDP Education Australia under Australia–China Institutional Links Program (ACILP) is acknowledged

FaFCNN: A General Disease Classification Framework Based on Feature Fusion Neural Networks

There are two fundamental problems in applying deep learning/machine learning methods to disease classification tasks, one is the insufficient number and poor quality of training samples; another one is how to effectively fuse multiple source features and thus train robust classification models. To address these problems, inspired by the process of human learning knowledge, we propose the Feature-aware Fusion Correlation Neural Network (FaFCNN), which introduces a feature-aware interaction module and a feature alignment module based on domain adversarial learning. This is a general framework for disease classification, and FaFCNN improves the way existing methods obtain sample correlation features. The experimental results show that training using augmented features obtained by pre-training gradient boosting decision tree yields more performance gains than random-forest based methods. On the low-quality dataset with a large amount of missing data in our setup, FaFCNN obtains a consistently optimal performance compared to competitive baselines. In addition, extensive experiments demonstrate the robustness of the proposed method and the effectiveness of each component of the model\footnote{Accepted in IEEE SMC2023}

Socioeconomic Status and the Quality of Acute Stroke Care

Background and Purpose—The association of socioeconomic status (SES) with quality of stroke care is not well understood, and few studies have examined the association with different indicators of SES simultaneously. We assessed the impacts of low levels of education, occupation, and income on the quality of stroke care. Methods—We examined data from the China National Stroke Registry recording consecutive stroke patients between September 2007 and August 2008. Baseline low SES was measured using educational level <6 years, occupation as manual workers or no job, and average family income per capita at ≤¥1000 per month. Compliance with 11 performances was summarized in a composite score defined as the proportion of all needed care given. Poor quality of care was defined as having a composite score of 0.71 or less. Results—Among 12 270 patients with ischemic stroke, 38.6% had <6 educational years, 37.6% had manual workers/no job, and 34.7% had income ≤¥1000 per month. There was an increased chance of receiving poor quality of care in patients with low education (adjusted odds ratio 1.15, 95% confidence interval 1.03–1.28), low occupation (adjusted odds ratio 1.16, 95% confidence interval 1.01–1.32), and low income (adjusted odds ratio 1.18, 95% confidence interval 1.06–1.30), respectively. People with low SES had poor performances on some aspects of care quality. Combined effects existed among these SES indicators; those with low SES from all 3 indicators had the poorest quality of care. Conclusions—There was a social gradient in the quality of stroke care. Continuous efforts of socioeconomic improvement will increase the quality of acute stroke care.The Ministry of Science and Technology of the People’s Republic of China (2006BAI01A11, 2011BAI08B01, 2011BAI08B02, 2012ZX09303-005-001, and 2013BAI09B03), The Beijing Biobank of Cerebral Vascular Disease (D131100005313003), Beijing Institute for Brain Disorders (BIBD-PXM2013_014226_07_000084

Research on deep hole drilling vibration suppression based on magnetorheological fluid damper

Based on the working principle of magnetorheological fluid damping, in this paper, a set of squeezing mode Magneto-rheological Fluid (MRF) dampers is designed for drilling vibration suppression in deep hole machines. Elaborate analysis of the correlativity between the dynamic morphology trajectory of the machined hole surface, the vibration of the drilling tool-shaft, and the theoretical derivation of the damping force, is put forward in accordance with the Bingham model and Euler-Bernoulli beam Equation. Simultaneously, the contrast analysis of the vibration suppression effect is carried out through the drilling experiments with and without an MRF damper. In addition, a series of measurements on the vibration characteristics of the drilling shaft, the drilling tool and the guide surface wear patterns, and the machine hole surface are analyzed, respectively. Both the drilling experiments and theory studies have revealed that the strength of the magnetic field changed with the drill shaft at different levels of vibration. The MRF damper could suppress the vibration with nonlinear characteristics initiatively and instantaneously, by variable damping, which can eventually improve the surface roughness. In addition, according to the phenomenon of tool tipping, the breakage of the guide bars and the machine hole surface deduces the condition of the vibration effect objectively

OPR-Miner: Order-preserving rule mining for time series

Discovering frequent trends in time series is a critical task in data mining. Recently, order-preserving matching was proposed to find all occurrences of a pattern in a time series, where the pattern is a relative order (regarded as a trend) and an occurrence is a sub-time series whose relative order coincides with the pattern. Inspired by the order-preserving matching, the existing order-preserving pattern (OPP) mining algorithm employs order-preserving matching to calculate the support, which leads to low efficiency. To address this deficiency, this paper proposes an algorithm called efficient frequent OPP miner (EFO-Miner) to find all frequent OPPs. EFO-Miner is composed of four parts: a pattern fusion strategy to generate candidate patterns, a matching process for the results of sub-patterns to calculate the support of super-patterns, a screening strategy to dynamically reduce the size of prefix and suffix arrays, and a pruning strategy to further dynamically prune candidate patterns. Moreover, this paper explores the order-preserving rule (OPR) mining and proposes an algorithm called OPR-Miner to discover strong rules from all frequent OPPs using EFO-Miner. Experimental results verify that OPR-Miner gives better performance than other competitive algorithms. More importantly, clustering and classification experiments further validate that OPR-Miner achieves good performance