Adaptive particle swarm optimization

An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

[1-(4-Hydroxy­phen­yl)-1H-tetra­zol-5-ylsulfan­yl]acetic acid

The title compound, C9H8N4O3S, shows a layer structure constructed from inter­molecular O—H⋯O and O—H⋯N hydrogen bonds. Inter­atomic distances suggest that extensive, but not uniform, π-electron delocalization is present in the tetra­zole rings and extends over the exocyclic C—S bond

l-Glutamic acid hydro­chloride at 153 K

The title compound [systematic name: (S)-1,3-dicarboxy­propanaminium chloride], C5H10NO4 +·Cl−, has been investigated previously by Dawson [Acta Cryst. (1953). 6, 81–83], with R = 0.106 and without the location of H atoms, and then by Sequeira, Rajagopal & Chidambaram [Acta Cryst. (1972). B28, 2514–2519] using neutron diffraction with R = 0.043. The present determination at 153 K has R = 0.017 and all the H atoms are located. There are obvious differences in some C—C bond lengths between the present and previous studies. In the present structure, l-glutamic acid is protonated and is linked to the Cl− anion by an O—H⋯Cl hydrogen bond. The crystal structure is established by a three-dimensional network of O—H⋯O, N—H⋯O and N—H⋯Cl hydrogen bonds

Prevalence of Kaposi’s sarcoma-associated herpesvirus in Uygur and Han populations from the Urumqi and Kashgar regions of Xinjiang, China

Kaposi’s sarcoma-associated herpesvirus (KSHV) is the infectious etiologic agent associated with Kaposi’s sarcoma (KS), primary effusion lymphoma, and multicentric Castleman disease. It has been shown that high KSHV prevalence and high incidence of both classic KS and AIDSassociated KS are found mostly among people of Uygur ethnicity in Xinjiang, while people of Han ethnicity in Xinjiang have a higher KSHV seroprevalence than those of other Han populations in mainland China. However, it is still unclear why there is such geographical and population variation in KSHV distribution in China. In this work, we focused on the populations in the Kashgar region and Urumqi area, where a total of 1294 research subjects were randomly selected to investigate the potential correlation between KSHV prevalence and different ethnicities in endemic areas of Xinjiang, and to determine risk factors that may affect KSHV infection rates or KS incidence. We identified a high seroprevalence of KSHV and high peripheral blood DNA infection in the general Uygur and Han populations in both Urumqi and Kashgar regions of Xinjiang, and determined that advancing age, low education level, and stationary population status affect KSHV infection rates. Further, KSHV-positive Uygur participants were shown to have higher prevalence of neutralizing antibodies and neutralizing antibody titers than KSHV-positive Han participants