An Empirical Study on Identification of Corporate Life Cycle Phases

This paper classifies corporate life cycle in four stages, from start-up, through growth, maturity and deline. 762 listed manufactory corporations have been used as sample in empirical analysis methodology. Profit margin is chosen as the fundamental index first, then the index of development trend that based on comparing with average similar companies in this industry is constructed. According to those indexes that are chosen from half time of the whole corporate life cycle, we can demonstrate that what kind of life cycle phases are corporations at

A General Neural Causal Model for Interactive Recommendation

Survivor bias in observational data leads the optimization of recommender systems towards local optima. Currently most solutions re-mines existing human-system collaboration patterns to maximize longer-term satisfaction by reinforcement learning. However, from the causal perspective, mitigating survivor effects requires answering a counterfactual problem, which is generally unidentifiable and inestimable. In this work, we propose a neural causal model to achieve counterfactual inference. Specifically, we first build a learnable structural causal model based on its available graphical representations which qualitatively characterizes the preference transitions. Mitigation of the survivor bias is achieved though counterfactual consistency. To identify the consistency, we use the Gumbel-max function as structural constrains. To estimate the consistency, we apply reinforcement optimizations, and use Gumbel-Softmax as a trade-off to get a differentiable function. Both theoretical and empirical studies demonstrate the effectiveness of our solution

Application of interpolation methodology with dynamical constraint to the suspended particulate matter in the Liaodong Bay

IntroductionSuspended Particulate Matter (SPM) influences the primary production and the distributions of pollutants in the ocean. Besides, the regulation mechanisms of SPM in the Liaodong Bay were complicated.MethodTo analyze the distributions and influencing factors of SPM, based on the adjoint assimilation method, an interpolation method with dynamical constraint was established in the Liaodong Bay.ResultIn two ideal experiments, the cost function, Mean Absolute Error (MAE) and Normalized Mean Error (NME) all had reduced by more than 90%, which proved the accuracy of the interpolation method. Based on conventional observations of SPM, the distributions of dynamically constrained, Kriging and radial basis function (RBF) interpolations in March, May, August and October of 2015 were obtained.DiscussionThe cross-validation was carried out to compare the dynamically constrained interpolation and the unconstrained interpolations. Among seven unconstrained interpolation methods, the averaged MAE of RBF interpolation was the lowest, which was 10.976 mg/L. The averaged MAE of dynamically constrained interpolation was 7.703 mg/L, reduced by 29.8% compared with the RBF interpolation. It was indicated that RBF interpolation was the most accurate among the seven unconstrained interpolations and dynamically constrained interpolation was more accurate than unconstrained interpolations at the observation stations. The distributions of dynamically constrained and RBF interpolations were compared with Korean Geostationary Ocean Color Imager (GOCI) satellite-derived distributions of SPM concentrations in the Liaodong Bay. Fully considering the influences of the hydrodynamic processes, the dynamically constrained interpolation provided distributions more consistent with the satellite-derived distributions. However, due to the lack of observations in some areas and ignoring the influences of currents, some high values of SPM concentration were not captured by the distributions of RBF interpolation. Moreover, in accordance with the results of dynamically constrained interpolation, it was found that the SPM concentrations in the bay were affected by the SPM discharge from the Liao River Basin

A Transgenic Mouse Model for DNA/RNA Gene Therapy of Human β Thalassemia

TheâIVS-2-654 C→T mutation accounts for approximately 20% of â thalassemia mutation in southern  China; it causes aberrant RNA splicing and leads to â thalassemia. To provide an animal model for testing  therapies for correcting splicing defects, we have produced two lines of transgenic mice with the human â  thalassemia mutant gene. The transgenic mice carrying this mutant gene show the same aberrant splicing  as their human counterparts and provide an animal model for testing therapies to correct splicing defects  at either the RNA or DNA level.

Application of Scatter Diagram in Prehospital Screening for Arrhythmia Using Single Lead，Wearable Remote ECG Monitoring System

BackgroundArrhythmia is a common cardiovascular disease, which has a range of transient or paroxysmal conditions. Arrhythmia easily occurs outside of the hospital, but signals of its onset often could not be captured by traditional ECG devices since they can not be worn at any time.ObjectiveTo assess the effect of applying scatter diagram in prehospital screening for arrhythmia via analyzing patients' data monitored by the single lead, wearable remote ECG monitoring system.MethodsParticipants (n=1 076) were primary care patients who were selected from Yinchuan from September 2018 to September 2019. All of them used single lead, wearable remote ECG monitoring system to monitor cardiac rhythms prehospitally when they had palpitation, dizziness, chest tightness, shortness of breath and other symptoms, and real-timely uploaded 24-hour ambulatory ECG data to be used for screen for arrhythmia by different approaches: approach A (diagnosis made using scatter diagram analysis by primary care physicians) , approach B (diagnosis made using scatter diagram analysis by physicians from Remote ECG Center, the First People's Hospital of Yinchuan) , and approach C (diagnosis made using scatter diagram analysis and ECG analysis by physicians from Remote ECG Center, the First People's Hospital of Yinchuan) . Prevalence and types of arrhythmia detected by these approaches and diagnostic coincidence rate of these approaches were analyzed. The sensitivity, specificity, positive and negative predictive values of approaches A and B were assessed with those of approach C as the gold standard.Results(1) The frequencies of arrhythmias detected by approaches A, B and C were 1 301, 1 323, and 1 647, respectively. The types of arrhythmias detected by approaches A, B and C were 14, 14, and 15, respectively. And the prevalence of arrhythmias detected by approaches A, B and C were 80.9%, 81.2% and 87.5%, respectively. (2) The diagnoses made by approaches A and B were highly consistent〔Kappa=0.891, 95%CI (0. 711, 1.071) , P=0.617〕, and the diagnostic coincidence rate was 96.7%. The diagnoses made by approaches B and C were highly consistent〔Kappa=0.759, 95%CI (0.489, 1.029) , P<0.001〕, and the diagnostic coincidence rate was 93.6%. The diagnoses made by approach A were relatively consistent with those by approach C〔Kappa=0.692, 95%CI (0.392, 0.992) , P<0.001〕, and the diagnostic coincidence rate was 91.7%. (3) The sensitivity, specificity, positive and negative predictive values of approach A in diagnosing arrhythmia were 91.5%, 93.3%, 99.0% and 61.2%, and those of approach B were 92.8%, 99.3%, 99.9% and 66.3%.ConclusionUsing scatter diagram in prehospital screening for arrhythmia through analyzing the monitoring results of single lead, wearable remote ECG monitoring system will contribute to the development of arrhythmia diagnosis and treatment in primary care, and the establishment of an arrhythmia prevention and treatment network with the participation of residents, primary care physicians and remote ECG center physicians

pH effects on the electrochemical reduction of CO₍₂₎ towards C₂ products on stepped copper

CO2 conversion to reduced products provides a use for greenhouse gases, but reaction complexity stymies mechanistic studies. Here, authors present a microkinetic model for CO2 and CO reduction on copper, based on ab initio simulations, to elucidate pH’s impact on competitive reaction pathways

Isolation, Identification and Growth Characteristics of Spoilage Fungi from Stropharia rugosoannulata

Objective: To avoid browning and spoilage caused by microbial growth, there is an urgent need to carry out researches related to the spoilage species of Stropharia rugosoannulata, and to provide a theoretical basis for exploring storage and preservation techniques. Methods: The spoilage fungi of S. rugosoannulata were isolated and purified, and the dominant spoilage fungi were selected by corrosivity analysis and identified by morphology and molecular biology methods. Results: The spoilage fungi from S. rugosoannulata were carried out to 16 strains of Fusarium, 14 strains of Aspergillus and 4 strains of Rhizopus among them. Four decay-causing fungi were selected to have a rot-causing effect, and identified by morphology and molecular biology as Fusarium pseudoanthophilum, Aspergillus niger, Rhizopus azygosporus and Talaromyces purpureogenus, respectively. The growth characteristics showed that the optimal temperature was 25~28 ℃. The spoilage fungi grew slowly with the temperature of 4~10 ℃ and 35~60 ℃, and the lethal temperature was 60 ℃. The optimal pH was about 7.0 and the spoilage fungi could not grow when the pH was 13.0. Conclusion: Post-harvest storage at low temperature and suitable alkaline environment could inhibit the growth and reproduction of spoilage fungi, which was conducive to the storage and preservation of S. rugosoannulata