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Abstract Eu2+-activated phosphors are widely applied in lighting and display areas because of their good optical performance. In this paper, an excellent green-emitting zeolite-3A: 1.3 wt% Eu phosphor is prepared by a green and eco-friendly high-thermal reaction method without any reducing atmosphere or agents. Meanwhile, the reducing mechanism from Eu3+ ions to Eu2+ ions is investigated. The experiment results show that the morphology, crystal structure, and luminescent property are affected by sintering temperature. The resulting sample shows the broad excitation band is in the range of 310–450 nm and the peak of the broad emission band is located at 523 nm. Furthermore, zeolite-3A: 1.3 wt% Eu phosphor is encapsulated on a commercial UV-emitting chip to fabricate a purity green light-emitting diode (LED) with the Commission Internationale de L’Eclairage (CIE) color coordinates at (0.295, 0.537).

Abstract Composite hollow nanostructure composed by transition metal oxides are promising materials in electrochemistry, catalyst chemistry and material science. In this contribution, necklace-like NiO-CuO heterogeneous composite hollow nanostructures were synthesized by annealing Ni/Cu superlattice nanowires in air. Two kinds of morphologies including CuO nanotube linked core-shell structures and CuO nanotube linked hollow structures were obtained. The structure can be tuned easily by adjusting the relative length of Cu segments in Ni/Cu superlattice nanowires and the annealing temperature. The relative diffusion amount of Cu to Ni segments was proved to be the key factor to influence the annealed sample morphology. The formation mechanism was discussed in detail based on Kirkendal effect and high temperature oxidation of alloy. We demonstrated that hollow structure or core-shell structure is related to whether the oxidation exists only in external sites or co-exists in external and internal sites during annealing.

Introduction: Dysregulation in lipid metabolism contributes to the occurrence and development of various cancers. The connection between changes in lipid metabolism and the development of intrahepatic cholangiocarcinoma remains uncertain. Our objective was to investigate the significance of blood lipid levels in patients with intrahepatic cholangiocarcinoma who have undergone surgery. Methods: Ninety-seven ICC patients who underwent surgery were retrospectively enrolled. After 92.2 months of follow-up, the Kaplan-Meier analysis and Cox proportional hazard model were used to calculate overall survival and recurrence-free survival. Results: The median age of this cohort was 56 years, and 79 (81.4 %) of them were male. Eighty-eight (90.7 %) patients presented with tumor recurrence and 73 (75.3 %) died. In multivariate analyses, high-density lipoprotein cholesterol level (

Differential evolution (DE) has shown remarkable performance in solving continuous optimization problems. However, its optimization performance still encounters limitations when confronted with complex optimization problems with lots of local regions. To address this issue, this paper proposes a dual elite groups-guided mutation strategy called “DE/current-to-duelite/1” for DE. As a result, a novel DE variant called DEGGDE is developed. Instead of only using the elites in the current population to direct the evolution of all individuals, DEGGDE additionally maintains an archive to store the obsolete parent individuals and then assembles the elites in both the current population and the archive to guide the mutation of all individuals. In this way, the diversity of the guiding exemplars in the mutation is expectedly promoted. With the guidance of these diverse elites, a good balance between exploration of the complex search space and exploitation of the found promising regions is hopefully maintained in DEGGDE. As a result, DEGGDE expectedly achieves good optimization performance in solving complex optimization problems. A large number of experiments are conducted on the CEC’2017 benchmark set with three different dimension sizes to demonstrate the effectiveness of DEGGDE. Experimental results have confirmed that DEGGDE performs competitively with or even significantly better than eleven state-of-the-art and representative DE variants.

Although particle swarm optimization (PSO) has been successfully applied to solve optimization problems, its optimization performance still encounters challenges when dealing with complicated optimization problems, especially those with many interacting variables and many wide and flat local basins. To alleviate this issue, this paper proposes a differential elite learning particle swarm optimization (DELPSO) by differentiating the two guiding exemplars as much as possible to direct the update of each particle. Specifically, in this optimizer, particles in the current swarm are divided into two groups, namely the elite group and non-elite group, based on their fitness. Then, particles in the non-elite group are updated by learning from those in the elite group, while particles in the elite group are not updated and directly enter the next generation. To comprise fast convergence and high diversity at the particle level, we let each particle in the non-elite group learn from two differential elites in the elite group. In this way, the learning effectiveness and the learning diversity of particles is expectedly improved to a large extent. To alleviate the sensitivity of the proposed DELPSO to the newly introduced parameters, dynamic adjustment strategies for parameters were further designed. With the above two main components, the proposed DELPSO is expected to compromise the search intensification and diversification well to explore and exploit the solution space properly to obtain promising performance. Extensive experiments conducted on the widely used CEC 2017 benchmark set with three different dimension sizes demonstrated that the proposed DELPSO achieves highly competitive or even much better performance than state-of-the-art PSO variants.

Particle swarm optimization (PSO) has exhibited well-known feasibility in problem optimization. However, its optimization performance still encounters challenges when confronted with complicated optimization problems with many local areas. In PSO, the interaction among particles and utilization of the communication information play crucial roles in improving the learning effectiveness and learning diversity of particles. To promote the communication effectiveness among particles, this paper proposes a stochastic triad topology to allow each particle to communicate with two random ones in the swarm via their personal best positions. Then, unlike existing studies that employ the personal best positions of the updated particle and the neighboring best position of the topology to direct its update, this paper adopts the best one and the mean position of the three personal best positions in the associated triad topology as the two guiding exemplars to direct the update of each particle. To further promote the interaction diversity among particles, an archive is maintained to store the obsolete personal best positions of particles and is then used to interact with particles in the triad topology. To enhance the chance of escaping from local regions, a random restart strategy is probabilistically triggered to introduce initialized solutions to the archive. To alleviate sensitivity to parameters, dynamic adjustment strategies are designed to dynamically adjust the associated parameter settings during the evolution. Integrating the above mechanism, a stochastic triad topology-based PSO (STTPSO) is developed to effectively search complex solution space. With the above techniques, the learning diversity and learning effectiveness of particles are largely promoted and thus the developed STTPSO is expected to explore and exploit the solution space appropriately to find high-quality solutions. Extensive experiments conducted on the commonly used CEC 2017 benchmark problem set with different dimension sizes substantiate that the proposed STTPSO achieves highly competitive or even much better performance than state-of-the-art and representative PSO variants.

High-dimensional optimization problems are more and more common in the era of big data and the Internet of things (IoT), which seriously challenge the optimization performance of existing optimizers. To solve these kinds of problems effectively, this paper devises a dimension group-based comprehensive elite learning swarm optimizer (DGCELSO) by integrating valuable evolutionary information in different elite particles in the swarm to guide the updating of inferior ones. Specifically, the swarm is first separated into two exclusive sets, namely the elite set (ES) containing the top best individuals, and the non-elite set (NES), consisting of the remaining individuals. Then, the dimensions of each particle in NES are randomly divided into several groups with equal sizes. Subsequently, each dimension group of each non-elite particle is guided by two different elites randomly selected from ES. In this way, each non-elite particle in NES is comprehensively guided by multiple elite particles in ES. Therefore, not only could high diversity be maintained, but fast convergence is also likely guaranteed. To alleviate the sensitivity of DGCELSO to the associated parameters, we further devise dynamic adjustment strategies to change the parameter settings during the evolution. With the above mechanisms, DGCELSO is expected to explore and exploit the solution space properly to find the optimum solutions for optimization problems. Extensive experiments conducted on two commonly used large-scale benchmark problem sets demonstrate that DGCELSO achieves highly competitive or even much better performance than several state-of-the-art large-scale optimizers.

Optimization problems are ubiquitous in every field, and they are becoming more and more complex, which greatly challenges the effectiveness of existing optimization methods. To solve the increasingly complicated optimization problems with high effectiveness, this paper proposes an adaptive covariance scaling estimation of distribution algorithm (ACSEDA) based on the Gaussian distribution model. Unlike traditional EDAs, which estimate the covariance and the mean vector, based on the same selected promising individuals, ACSEDA calculates the covariance according to an enlarged number of promising individuals (compared with those for the mean vector). To alleviate the sensitivity of the parameters in promising individual selections, this paper further devises an adaptive promising individual selection strategy for the estimation of the mean vector and an adaptive covariance scaling strategy for the covariance estimation. These two adaptive strategies dynamically adjust the associated numbers of promising individuals as the evolution continues. In addition, we further devise a cross-generation individual selection strategy for the parent population, used to estimate the probability distribution by combing the sampled offspring in the last generation and the one in the current generation. With the above mechanisms, ACSEDA is expected to compromise intensification and diversification of the search process to explore and exploit the solution space and thus could achieve promising performance. To verify the effectiveness of ACSEDA, extensive experiments are conducted on 30 widely used benchmark optimization problems with different dimension sizes. Experimental results demonstrate that the proposed ACSEDA presents significant superiority to several state-of-the-art EDA variants, and it preserves good scalability in solving optimization problems.

Background: Clinical and laboratory data were collected and analysed from patients with Ebola virus disease (EVD) in Jui Government Hospital in Freetown, Sierra Leone, where patients with EVD were received and/or treated from October 1, 2014 to March 21, 2015 during the West Africa EVD outbreak. Methods: The study admitted 285 patients with confirmed EVD and followed them up till the endpoint (recovery or death). EVD was confirmed by quantitative RT-PCR assays detecting blood Ebola virus (EBOV). Results: Among the 285 lab-confirmed EVD cases in Jui Government Hospital, 146 recovered and 139 died, with an overall survival rate of 51.23 %. Patients under the age of 6 years had a lower survival rate (37.50 %). Most non-survivors (79.86 %) died within 7 days after admission and the mean hospitalization time for non-survivors was 5.56 ± 6.11 days. More than half survivors (63.69 %) turned blood EBOV negative within 3 weeks after admission and the mean hospitalization time for survivors was 20.38 ± 7.58 days. High blood viral load (≥106 copies/ml) was found to be predictive of the non-survival outcome as indicated by the Receiver Operating Characteristic (ROC) curve analysis. The probability of patients' survival was less than 15 % when blood viral load was greater than 106 copies/ml. Multivariate analyses showed that blood viral load (P = 0.005), confusion (P = 0.010), abdominal pain (P = 0.003), conjunctivitis (P = 0.035), and vomiting (P = 0.004) were factors independently associated with the outcomes of EVD patients. Conclusions: Most death occurred within 1 week after admission, and patients at the age of 6 or younger had a lower survival rate. Most surviving patients turned blood EBOV negative within 1-4 weeks after admission. Factors such as high blood viral load, confusion, abdominal pain, vomiting and conjunctivitis were associated with poor prognosis for EVD patients. [ABSTRACT FROM AUTHOR]

We explain some phenomena existing generally in the timing residuals: amplitude and sign of the second derivative of a pulsar's spin-frequency (?), some sophisticated residual patterns, which also change with the time span of data segments. The sample is taken from Hobbs et al., in which the pulsar's spin-frequency and its first derivative have been subtracted from the timing solution fitting. We first classify the timing residual patterns into different types based on the sign of ? . Then we use the magnetic field oscillation model developed in our group to fit successfully the different kinds of timing residuals with the Markov Chain Monte Carlo method. Finally, we simulate the spin evolution over 20 years for a pulsar with typical parameters and analyse the data with the conventional timing solution fitting. By choosing different segments of the simulated data, we find that most of the observed residual patterns can be reproduced successfully. This is the first time that the observed residual patterns are fitted by a model and reproduced by simulations with very few parameters. From the distribution of the different residual patterns in the P-?P diagram, we argue that (1) a single magnetic field oscillation mode exists commonly in all pulsars throughout their lifetimes; (2) there may be a transition period over the lifetimes of pulsars, in which multiple magnetic field oscillation modes exist. [ABSTRACT FROM AUTHOR]

XTE J1810-189 underwent an outburst in 2008, and was observed over ~100 d by RXTE. Performing a time-resolved spectral analysis on the photospheric radius expansion burst detected on 2008 May 4, we obtain the source distance in the range 3.5-8.7 kpc for the first time. During its outburst, XTE J1810-189 did not enter into the high/soft state, and both the soft and hard colours decreased with decreasing flux. The fractional rms remained at high values (~30 per cent). The RXTE/PCA spectra for 3-25 keV can be described by an absorbed power-law component with an additional Gaussian component, and the derived photon index V increased from 1.84 ± 0.01 to 2.25 ± 0.04 when the unabsorbed X-ray luminosity at 3-25 keV dropped from 4 x 1036 to 6 x 1035 erg s-1. The relatively high flux, dense observations and broad-band spectra are strong evidence that the softening behaviour detected in the outburst of XTE J1810-189 originates from the evolution of a non-thermal component rather than the thermal component (i.e. neutron star surface emission). [ABSTRACT FROM AUTHOR]

Mutation takes a vital part in assisting differential evolution (DE) to achieve satisfactory performance. The most crucial factor for a good mutation scheme is to mutate individuals dispersedly but with fast convergence to optimal regions. With this purpose, this paper designs a novel mutation approach, termed as “DE/current-to-gselite/1”, by utilizing the Gaussian distribution to sample guiding exemplars around elites in the population to evolve individuals. Accordingly, a Gaussian sampling guided differential evolution (GSGDE) is devised to hopefully tackle optimization problems effectively. With the assistance of the Gaussian distribution, GSGDE mutates distinct individuals with very different guiding exemplars. Hence, high mutation diversity is expectedly maintained, which leads to that individuals could traverse the problem space in diverse directions. Thanks to the narrow sampling range of the Gaussian distribution, the generated guiding exemplars are likely better and thus individuals in the population are anticipated to move towards optimal regions fast. This is of great profit for fast convergence to high-quality solutions. Further, a dynamic parameter adjustment strategy is proposed to dynamically regulate the number of elites. Hereafter, GSGDE gradually shifts from concentrating on exploring problem space to focusing on exploiting found optimal areas. Cooperated with an existing adaptive parameter strategy, GSGDE is anticipated to strike a good balance between exploitation and exploration to traverse the problem space and hence likely obtain satisfactory performance. Experiments have been extensively carried out on the latest CEC2014 and CEC2017 problem suites with three settings of the dimensionality. Experimental results substantiate that GSGDE has a good scalability and attains highly competitive performance with or even significantly superior performance to 11 latest and representative DE methods. Particularly, its superiority becomes more and more significant as the dimensionality increases.

Background: A Chinese medical team managed Ebola virus disease (EVD) patients in Sierra Leone from October 2014 to March 2015 and attended to 693 suspected patients, of whom 288 had confirmed disease. Methods: A retrospective study was conducted of the 288 patients with confirmed disease. Clinical symptoms, manifestations, and serum viral load were analyzed and compared among the different groups for mortality and survival time. Results: Among the 288 confirmed EVD patients (149 male and 139 female, median age 28 years, and median log viral load 6.68), 98 died, 36 recovered, and 154 were lost to follow-up. Common symptoms were fever (77.78%), fatigue (64.93%), abdominal pain (64.58%), headache (62.85%), and diarrhea (61.81%). Compared to patients aged 106 copies/ml had a higher case fatality rate than those with