Neutrosophic soft sets forecasting model for multi-attribute time series

Traditional time series forecasting models mainly assume a clear and definite functional relationship between historical values and current/future values of a dataset. In this paper, we extended current model by generating multi-attribute forecasting rules based on consideration of combining multiple related variables. In this model, neutrosophic soft sets (NSSs) are employed to represent historical statues of several closely related attributes in stock market such as volumes, stock market index and daily amplitudes

Research on the evolution of innovation behavior of new generation entrepreneurs in different scenarios

Innovation of new generation entrepreneurs is crucial to the development of a country. Empirical research method can analyze the history and current situation, but it is difficult to reflect the dynamic process and evolution trend under different scenarios. In this paper, we adopt computational experiment method to model the decision-making process of new generation entrepreneurs. Multi-agent evolution model is constructed to simulate individual behavior of different types of new generation entrepreneurs under different scenarios. By the comparison of different results, it analyses the evolutionary rules of innovation behaviors and explores guidance policies to promote entrepreneurs’ innovation behavior and achieve better innovation performance. The experimental results show that although internal elements such as individual’s innovative spirit, innovative ability and cognition of social capital determine the innovation intention, the capital, technology and talent conditions are also very important for innovation implementation. New generation entrepreneurs with different risk preferences should objectively evaluate and treat innovation risks according to their own characteristics. This helps to reduce the negative impact of innovation risk on continuous innovation. Meanwhile, government should pay attention to establishing risk guarantee mechanism such as innovation insurance fund to promote the innovation of new generation entrepreneurs. First published online 17 April 202

Performance of a Ton-scale Water-based Liquid Scintillator Detector

This study reports the performance and light yield of 1% concentration water-based liquid scintillator (WbLS) deployed in a 1000-liter detector. A light yield of 99$\pm$15 photons per MeV is determined by comparing data with simulation. This result aligns with our previous light yield determination using smaller detectors, thus establishing a solid foundation for the ongoing development and deployment of WbLS in larger-scale detectors. The feasibility of in situ preparation and the stability of light yield in WbLS are demonstrated, reinforcing its suitability for long-term experimental endeavors.Comment: Accepted for publication in JINS

Identification and treatment of intestinal malrotation with midgut volvulus in childhood: a multicenter retrospective study

BackgroundIntestinal malrotation is a rare condition, and its delayed diagnosis can lead to fatal consequences. This study aimed to investigate the identification and treatment of malrotation in children.MethodsClinical data, imaging, operative findings, and early postoperative outcomes of 75 children with malrotation were retrospectively analyzed.ResultsThe mean age was 6.18 ± 4.93 days and 51.26 ± 70.13 months in the neonatal group (56 patients) and non-neonatal group (19 patients), respectively. Sixty-seven patients were under the age of 1 year at the time of diagnosis. The occurrence of bilious vomiting and jaundice was significantly higher in the neonatal group (89.29%) than that in the non-neonatal group (37.5%), p < 0.05 and p < 0.01, respectively. The incidence of abnormal ultrasound (US) findings was 97.30% and 100%, respectively, and the sensitivities of the upper gastrointestinal series were 84.21% and 87.5%, respectively. Sixty-six (88%) patients had midgut volvulus, including in utero volvulus (two patients) and irreversible intestinal ischemia (four patients). Most neonates (89.29%) underwent open Ladd's procedure with a shorter operative time (p < 0.01). Reoperation was performed for postoperative complications (four patients) or missed comorbidities (two patients).ConclusionsNon-bilious vomiting was the initial symptom in >10% of neonates and nearly 40% of non-neonates. This highlights the importance for emergency physicians and surgeons to be cautious about ruling out malrotation in patients with non-bilious vomiting. Utilizing US can obviate the need for contrast examinations owing to its higher diagnostic accuracy and rapid diagnosis and can be recommended as a first-line imaging technique. Additionally, open surgery is still an option for neonatal patients

Tumor Biology and Immune Infiltration Define Primary Liver Cancer Subsets Linked to Overall Survival After Immunotherapy

Primary liver cancer is a rising cause of cancer deaths in the US. Although immunotherapy with immune checkpoint inhibitors induces a potent response in a subset of patients, response rates vary among individuals. Predicting which patients will respond to immune checkpoint inhibitors is of great interest in the field. In a retrospective arm of the National Cancer Institute Cancers of the Liver: Accelerating Research of Immunotherapy by a Transdisciplinary Network (NCI-CLARITY) study, we use archived formalin-fixed, paraffin-embedded samples to profile the transcriptome and genomic alterations among 86 hepatocellular carcinoma and cholangiocarcinoma patients prior to and following immune checkpoint inhibitor treatment. Using supervised and unsupervised approaches, we identify stable molecular subtypes linked to overall survival and distinguished by two axes of aggressive tumor biology and microenvironmental features. Moreover, molecular responses to immune checkpoint inhibitor treatment differ between subtypes. Thus, patients with heterogeneous liver cancer may be stratified by molecular status indicative of treatment response to immune checkpoint inhibitors

A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships

Many of the existing autoregressive moving average (ARMA) forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS) for two factors by calculating the difference of each data point with its previous day, then finding the absolute means of the two FTSs. We then constructed a fuzzy fluctuation time series (FFTS) according to the defined linguistic sets. The next step was establishing fuzzy fluctuation logical relation groups (FFLRGs) for a two-factor first-order autoregressive (AR(1)) model and forecasting the training data with the AR(1) model. Then we built FFLRGs for a two-factor first-order autoregressive moving average (ARMA(1,m)) model. Lastly, we forecasted test data with the ARMA(1,m) model. To illustrate the performance of our model, we used real Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Dow Jones datasets as a secondary factor to forecast TAIEX. The experiment results indicate that the proposed two-factor fluctuation ARMA method outperformed the one-factor method based on real historic data. The secondary factor may have some effects on the main factor and thereby impact the forecasting results. Using fuzzified fluctuations rather than fuzzified real data could avoid the influence of extreme values in historic data, which performs negatively while forecasting. To verify the accuracy and effectiveness of the model, we also employed our method to forecast the Shanghai Stock Exchange Composite Index (SHSECI) from 2001 to 2015 and the international gold price from 2000 to 2010