Unique Continuation for Stochastic Heat Equations

We establish a unique continuation property for stochastic heat equations evolving in a bounded domain $G$. Our result shows that the value of the solution can be determined uniquely by means of its value on an arbitrary open subdomain of $G$ at any given positive time constant. Further, when $G$ is convex and bounded, we also give a quantitative version of the unique continuation property. As applications, we get an observability estimate for stochastic heat equations, an approximate result and a null controllability result for a backward stochastic heat equation

Numerical Simulation for Exploring the Effect of Viscosity on Single-screw Extrusion Process of Propellant

AbstractSingle-screw extrusion process of propellant has the characteristics of multiple accidents, complicated rheological parameters and difficult measurement of real-time conditions, however, the process details can be reproduced by simulation conveniently and intuitively. In this paper, the POLYFLOW simulation platform was used to model and analyze the single-screw extrusion process of propellant through the application of Finite Element Analysis on extrusion of plastic. The distributions and changes of viscosity in extrusion process, which were taken as the starting point to study the threshold and distribution of pressure, temperature and other sensitive parameters, were obtained. The simulation shows that the risk at the screw edge is higher because of severe mixing and plasticizing process, and the viscous heating is up to 1.4×105 W · m-3. Parameters under different speed conditions were studied as well, which provide guidance for the coordination of security and economy in production

Knowledge and innovation in emerging market multinationals: the expansion paradox

This article examines the innovation and knowledge strategies that allow emerging-market companies to become major international players. By adopting a qualitative approach, this study identifies a significant paradox between the desire of some leading Chinese car companies to expand internationally and the current relationship of such companies with leading global car companies, which significantly inhibits Chinese international expansion. This study unpacks that paradox using innovation theory and the resource-based view and develops a matrix of strategic options that can assist emerging market multinational companies to expand internationally

Local state observation for stochastic hyperbolic equations

In this paper, we solve a local state observation problem for stochastic hyperbolic equations without boundary conditions, which is reduced to a local unique continuation property for these equations. This result is proved by a global Carleman estimate. As far as we know, this is the first result in this topic

Pervasive and Mobile Computing

We present in this paper our winning solution to Dedicated Task 1 in Nokia Mobile Data Challenge (MDC). MDC Task 1 is to infer the semantic category of a place based on the smartphone sensing data obtained at that place. We approach this task in a standard supervised learning setting: we extract discriminative features from the sensor data and use state-of-the-art classifiers (SVM, Logistic Regression and Decision Tree Family) to build classification models. We have found that feature engineering, or in other words, constructing features using human heuristics, is very effective for this task. In particular, we have proposed a novel feature engineering technique, Conditional Feature (CF), a general framework for domain-specific feature construction. In total, we have generated 2,796,200 features and in our final five submissions we use feature selection to select 100 to 2000 features. One of our key findings is that features conditioned on fine-granularity time intervals, e.g. every 30 min, are most effective. Our best 10-fold CV accuracy on training set is 75.1% by Gradient Boosted Trees, and the second best accuracy is 74.6% by L1-regularized Logistic Regression. Besides the good performance, we also report briefly our experience of using F# language for large-scale (∼70 GB raw text data) conditional feature construction

Source Free Transfer Learning for Text Classification

Transfer learning uses relevant auxiliary data to help the learning task in a target domain where labeled data is usually insufficient to train an accurate model. Given appropriate auxiliary data, researchers have proposed many transfer learning models. How to find such auxiliary data, however, is of little research so far. In this paper, we focus on the problem of auxiliary data retrieval, and propose a transfer learning framework that effectively selects helpful auxiliary data from an open knowledge space (e.g. the World Wide Web). Because there is no need of manually selecting auxiliary data for different target domain tasks, we call our framework Source Free Transfer Learning (SFTL). For each target domain task, SFTL framework iteratively queries for the helpful auxiliary data based on the learned model and then updates the model using the retrieved auxiliary data. We highlight the automatic constructions of queries and the robustness of the SFTL framework. Our experiments on 20NewsGroup dataset and a Google search snippets dataset suggest that the framework is capable of achieving comparable performance to those state-of-the-art methods with dedicated selections of auxiliary data

Effectiveness of cyclic treatment of municipal wastewater by Tetradesmus obliquus – Loofah biofilm, its internal community changes and potential for resource utilization

Microalgae biofilm has garnered significant attention from researchers in the field of sewage treatment due to its advantages such as ease of collection and stable sewage treatment capabilities. Using agricultural waste as biofilm carriers has become a hotspot in reducing costs for this method. This study first combined Tetradesmus obliquus with loofah to form a microalgae biofilm for the study of periodic nitrogen and phosphorus removal from municipal wastewater. The biofilm could stably treat 7 batches of wastewater within one month. The removal rate of TP almost reached 100 %, while the removal rates of NH4+ and TN both reached or exceeded 80 %. The average biomass yield over 25 days was 102.04 mg/L/day. The polysaccharide content increased from 8.61 % to 16.98 % during the cyclic cultivation. The lipid content gradually decreased from 40.91 to 26.1 %. The protein content increased from 32.93 % in the initial stage to 41.18 % and then decreased to 36.31 % in the later stage. During the mid-stage of culturing, the richness of anaerobic bacteria decreased, while the richness of aerobic and facultative bacteria increased, which was conducive to the construction of the microalgae-bacteria symbiotic system and steadily improved the effect of nitrogen and phosphorus removal. As the culturing progressed, the Rotifers that emerged during the mid-stage gradually damaged the biofilm over time, leading to a decline in the effectiveness of sewage treatment in the later stages. This study offers technical support for carrier selection in microalgae biofilm methods and for the periodic removal of nitrogen and phosphorus from wastewater

Preoperative contributing factors and the remission of diabetes after metabolic surgery: the mediating role of preoperative triglyceride

Abstract Background and objective Limited understanding exists regarding the factors affecting the prognosis of surgical treatment for type 2 diabetes mellitus (T2DM), particularly in Chinese patients. In this study, we examined a cohort of early and intermediate obese T2DM patients to explore the potential impact of preoperative lipid metabolism in metabolic surgery on the postoperative remission of T2DM. Methods Participants with T2DM and obesity underwent metabolic surgery. Clinical data, including baseline body mass index, percentage of excess weight loss, and preoperative biochemical indicators, were collected and analyzed. A multidisciplinary team conducted patient follow-up. Remission was defined as sub-diabetic hyperglycemia (HbA1c < 48 mmol/mol, fasting glucose 100–125 mg/dl) without pharmacological intervention for at least 12 months. Results Over a median follow-up of 27 months, 96 T2DM patients with metabolic surgery were studied, with no laparotomies required. Among these patients, 61 (63.5%) achieved complete remission, and 85 (88.5%) experienced remission. In multivariable analysis models, preoperative fasting blood glucose (FBG) significantly correlated with all postoperative outcomes. Furthermore, mediation analysis indicated that preoperative triglycerides (TG) mediated 26.31% of the association between preoperative FBG and postoperative remission. Both preoperative FBG and TG were negatively associated with the postoperative remission of T2DM. Conclusion In summary, our findings suggest that lower preoperative fasting glucose levels enhance the likelihood of postoperative T2DM remission. Moreover, preoperative TG could potentially play a mediating role in the postoperative remission of T2DM. Therefore, evaluating and managing fasting glucose and lipids before the procedure may aid in assessing the prognosis of metabolic surgery. Level of evidence Level III, designed cohort