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The schematic diagram of the Cppy-O evaporator for all-day steam generation.

Amplifying free radical production by chemical dynamic catalysis to cause oxidative damage to cancer cells has received extensive interest for cancer-specific therapy. The major challenge is inevitable negative modulation on the tumor microenvironment (TME) by these species, hindering durable effectiveness. Here we show for the first time an oxygen vacancy-rich Bi-based regulator that allows environment-adaptive free radical catalysis. Specifically, the regulator catalyzes production of highly toxic O2 .- and . OH in cancer cells via logic enzymatic reactions yet scavenges accumulation of free radicals and immunosuppressive mediators in TME-associated noncancerous cells. Atomic-level mechanistic studies reveal that such dual-modal regulating behavior is dominated by oxygen vacancies that well fit for free radical catalytic kinetics, along with distinguished cellular fates of this regulator. With this smart regulator, a "two birds with one shot" cancer dynamic therapy can be expected.

With the rapid growth of online product reviews, many users refer to others’ opinions before deciding to purchase any product. However, unfortunately, this fact has promoted the constant use of fake reviews, resulting in many wrong purchase decisions. The effective identification of deceptive reviews becomes a crucial yet challenging task in this research field. The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods are inefficient because they depend on the features of reviewers to detect each fake review. Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. Firstly, a time series model of all the reviews of a product is constructed, and then the suspected time intervals are captured based on the burst review increases in these intervals. Secondly, a co-training two-view semisupervised learning algorithm was performed in each captured interval, in which linguistic cues, metadata, and user purchase behaviors were synthetically employed to classify the reviews and check whether they are spam ones or not. A series of numerical experiments on a real dataset acquired from Taobao.com have confirmed the effectiveness of the proposed model, not only reaping benefits in terms of time efficiency and high accuracy but also overcoming the shortcomings of supervised learning methods, which depend on large amounts of labeled examples. And a trade-off balance was obtained between accuracy and efficiency.

Online deceptive reviews widely exist in the online shopping environment. Numerous studies have investigated the impact of online product reviews on customer behaviour and sales. However, the existing literature is mainly based on real product reviews; only a few studies have investigated deceptive reviews. Based on the results of deceptive reviews, this article explores the factors that affect customer purchase decision in online review systems, which is flooded by deceptive reviews. Therefore, a deceptive review influence model is proposed based on three influential factors of online review system, sentiment characteristics, review length, and online seller characteristics. Based on them, text mining is used to quantify the indicators of the three influential factors. Through principal component analysis and linear regression, the experimental results of electronic appliances on Tmall show that the three influential factors are positively related to customers' purchase intention and decision making.

DNA-based molecular communications (DMC) are critical for regulating biological networks to maintain stable organismic functions. However, the complicated, time-consuming information transmission process involved in genome-coded DMC and the limited, vulnerable decoding activity generally lead to communication impairment or failure, in response to external stimuli. Herein, we present a conceptually innovative DMC strategy mediated by the DNA framework-based artificial DNA encoder. With the free-radical cascade as a proof-of-concept study, the artificial DNA encoder shows active sensing and real-time actuation, in situ and broad free radical-decoding efficacy, as well as robust resistance to environmental noises. It can also block undesirable short to medium-range communications between free radicals and inflammatory networks, leading to a synergistic antiobesity effect. The artificial DNA encoder-based DMC may be generalized to other communication systems for a variety of applications.

In Pseudo relevance feedback, It is often crucial to identify those good feedback documents from which useful expansion terms can be added to the query. For Extensible Markup Language (XML) data, this paper proposes an approach for identifying good feedback fragments by a complete framework in which two phrases are included. (1) The first phase is about XML search results clustering. We performed a k-medoid clustering algorithm to XML fragments based on an extended latent semantic indexing model. (2) The second phase is a two-stage ranking. Cluster ranking is performed in the first stage to select relevant clusters on the basis of cluster labelling, which is determined by extracted fragment keywords based on a combination of weight and context; fragment ranking is performed during the second stage where multiple features are used to identify high quality fragments from the previously obtained relevant clusters. Experimental results on standard INEX test data show that the proposed approach achieves statistically significant improvements over a strong original query results mechanism, ensuring high quality fragments for feedback.

The polycrystalline Eu2+ and Dy3+ co-doped strontium aluminates SrAl2O4: Eu2+, Dy3+ with different compositions were prepared by solid state reactions. The UV-excited photoluminescence, persistent luminescence and thermo-luminescence were studied and compared. Results show that the doped Eu2+ ion in SrAl2O4: Eu2+, Dy3+ phosphors works as not only the UV-excited luminescent center but also the persistent luminescent center. The doped Dy3+ ion can hardly yield any luminescence under UV-excitation, but effectively enhance the persistent luminescence and thermo-luminescence of SrAl2O4: Eu2+. Dy3+ co-doping can help form electron traps with appropriate depth due to its suitable electro-negativity, and increase the density and depth of electron traps. Based on above observations, a persistent luminescence mechanism, electron transfer model, is proposed and illustrated.

Alumina encapsulation on SrAl2O4: Eu2+, Dy3+ phosphors by a new type of chemical precipitation process was reported for the first time to the best of our knowledge. X-ray fluorescent measurements revealed that using glycol instead of the distilled water as the disperse medium is helpful to alumina encapsulation. Scanning electron micrographs and BET measurements showed that a dense and homogeneous off-white alumina layer was formed on the phosphor surface after encapsulation. Water resistance and heat resistance measurements showed that when the encapsulation amount reached to 5 wt.% (fed value), the encapsulated phosphors began to show good water resistance and heat resistance with little loss of persistent phosphorescence.

In this paper, we study how to perform XML query expansion effectively from the high quality pseudo-relevance documents. A solution for selecting good expansion information is presented, in which various features impacting weight, such as term element frequency, term inverse element frequency, semantic weight of tag and level information, are analyzed and those term with high weigh value are selected as expansion term. Experiment results show that proposed expansion method is feasible. Compared to original query and traditional expansion method with no structure features considered, our method achieves better retrieval performance.

This paper presents a multi-objective evolutionary algorithm based on Bayes with decision graphs, namely BSPEA, which replaces crossover and mutation operators in traditional EAs by building and learning Bayesian Networks, and avoids setting a lot of parameters manually and destroying some important building blocks. The simulation results demonstrate that the proposed algorithm is capable of converging on the better Pareto front quickly, and has a strong robustness.

With the rapid development of Internet technology and the great capacities of online documents, information retrieval has become a major research topic. In this paper, a new information retrieval algorithm is proposed, which can reduce time complexity and improve accuracy using classification and key phrase extraction. Then a new criterion named ranking error is contributed to solve the problem that the traditional performance evaluation methodology can't evaluate the ranking results of retrieved documents effectively. The experimental results indicate that the proposed algorithm outperforms vector space model and interactive retrieval based on classification in speed, accuracy and ranking error.