The aim of this work was to investigate
γ-irradiation treatment of pentachlorophenol (PCP), 2,4-dichlorophenol (DCP) and 2-chlorophenol (2-CP) in water. The dechlorination of PCP, DCP and 2-CP has ...been studied by using
γ-radiation as well as ozonization. G(Cl
−) is always much higher than the corresponding G(-PCP), this means that some primary radiolytic products are unstable during post-irradiation. The COD (Mn) values of irradiated PCP were determined. PCP is much more easily decomposed by irradiation than either DCP or
o-CP. Some stable radiolytic intermediates were detected. Preliminary analysis revealed that some chloroquinones were among the final products. Where ozone was used, the results suggest that it enhanced the efficiency of radiation-induced decomposition.
The optimal control of epidemic-like stochastic processes is important both historically and for emerging applications today, where it can be especially important to include time-varying parameters ...that impact viral epidemic-like propagation. We connect the control of such stochastic processes with time-varying behavior to the stochastic shortest path problem and obtain solutions for various cost functions. Then, under a mean-field scaling, this general class of stochastic processes is shown to converge to a corresponding dynamical system. We analogously establish that the optimal control of this class of processes converges to the optimal control of the limiting dynamical system. Consequently, we study the optimal control of the dynamical system where the comparison of both controlled systems renders various important mathematical properties of interest.
In the big data era, many organizations face the dilemma of data sharing. Regular data sharing is often necessary for human-centered discussion and communication, especially in medical scenarios. ...However, unprotected data sharing may also lead to data leakage. Inspired by adversarial attack, we propose a method for data encryption, so that for human beings the encrypted data look identical to the original version, but for machine learning methods they are misleading. To show the effectiveness of our method, we collaborate with the Beijing Tiantan Hospital, which has a world leading neurological center. We invite \(3\) doctors to manually inspect our encryption method based on real world medical images. The results show that the encrypted images can be used for diagnosis by the doctors, but not by machine learning methods.
Motivated by few delay-optimal scheduling results, in comparison to results on throughput optimality, we investigate a canonical input-queued switch scheduling problem in which the objective is to ...minimize the discounted delay cost over an infinite time horizon. We derive an optimal scheduling policy and establish corresponding theoretical properties, which are expected to be of interest more broadly than input-queued switches. Computational experiments demonstrate and quantify the benefits of our optimal scheduling policy over alternative policies such as variants of MaxWeight scheduling, well-known to be throughput optimal and more recently shown to be delay optimal in the heavy-traffic regime limit.
We consider the optimal allocation of generic resources among multiple generic entities of interest over a finite planning horizon, where each entity generates stochastic returns as a function of its ...resource allocation during each period. The main objective is to maximize the expected return while at the same time managing risk to an acceptable level for each period. We devise a general solution framework and establish how to obtain the optimal dynamic resource allocation.
Motivated by few delay-optimal scheduling results, in comparison to results
on throughput optimality, we investigate a canonical input-queued switch
scheduling problem in which the objective is to ...minimize the discounted delay
cost over an infinite time horizon. We derive an optimal scheduling policy and
establish corresponding theoretical properties, which are expected to be of
interest more broadly than input-queued switches. Computational experiments
demonstrate and quantify the benefits of our optimal scheduling policy over
alternative policies such as variants of MaxWeight scheduling, well-known to be
throughput optimal and more recently shown to be delay optimal in the
heavy-traffic regime limit.
We consider the optimal allocation of generic resources among multiple generic entities of interest over a finite planning horizon, where each entity generates stochastic returns as a function of its ...resource allocation during each period. The main objective is to maximize the expected return while at the same time managing risk to an acceptable level for each period. We devise a general solution framework and establish how to obtain the optimal dynamic resource allocation.
First of all, the paper introduces great importance and significant advantages of electrified railway for our nation. And then negative effects to the grid's power quality brought by electrified ...railway are emphasized, followed by typical solutions such as the dynamic compensation strategy, among which compensator based on STATCOM technology shows the best general performance. Finally, the paper presents a two-phase STATCOM device- Railway Unified Power Quality Controller (RUPQC)'s topology, and its excellent performance is shown and verified through simulation.
Personality assessments are at present nearly entirely dependent on self-reports, and machine learning methods have been rarely applied to this field. This study used machine learning to predict ...people's self-reported proactive personalities. Based on a sample of 901 participants that used Weibo text and short answer text, the authors used five machine learning algorithms for classification: Support Vector Machine (SVM), XGboost, k-nearest neighbor (KNN), naïve Bayes, and logistic regression. Seven different indicators - Accuracy (ACC), F1-score(F1), Sensitivity(SEN), Specificity (SPE), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Area under Curve (AUC) - combined with hierarchical cross-validation were also used to make the comprehensive evaluation of models. Based on this, we proposed a method to classify people's proactive personalities based on text mining technology. The results showed that the SVM and naïve Bayes outperformed the other methods on short texts (i.e., short answer texts) and mixed long texts (i.e., short answer & Weibo text). In the context of the texts used, mixed long text (i.e., short answer & Weibo text) improved and stabilized the indices, and this combination was the best choice of text for predicting proactive personality. In addition, the SVM was the most stable classifier in most situations, even on Weibo text that was not suitable for analysis as long text, and it also recorded good results in terms of accuracy, the F1-score, and the AUC.
Eosinophilic gastroenteritis is not only easy to ignore in clinical practice, but also easy to miss in the process of pathological diagnosis. There is a need to consider it in the differential ...diagnosis of alimentary disease.
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