Statistical-physical method for simulating the transport of microplastic-antibiotic compound pollutants in typical bay area.

Microplastics and antibiotics are emerging pollutants in the environment and have received widespread attention globally. In coastal areas, microplastic and antibiotic pollution is ubiquitous and often overlapping. Microplastic-antibiotic compound pollutants that are formed through adsorption have thus become a major concern. However, modeling knowledge of microplastic transport in coastal areas is still limited, and research on the impact of compound pollutants caused by Polythene (PE)-antibiotics in such settings is in early stages. In this study, using a lattice Boltzmann method (LBM) and temporal Markov method (TMM) under a statistical-physical framework, we simulated pollutant transport and PE-antibiotic compound pollutants in coastal areas. First, a series of models are proposed, including an LBM wave-current coupling model, an LBM antibiotic transport model, an LBM particle-tracking model, a TMM microplastic transport model and the final LBM-TMM hybrid compound pollutant model. Then, the suitability and applicability of the models was validated using experimental data and numerical simulations. Finally, the models were applied to a study area, Laizhou Bay (China). The simulation results demonstrate that adsorption will reduce the concentration of antibiotics in the water environment. Within 44 days, the adsorbed antibiotic carried by PE particles migrate further, and the width of the pollution zone escalates from 234.2 m to 689.0 m.

Performability evaluation, validation and optimization for the steam generation system of a coal-fired thermal power plant.

The present paper talks over performability evaluation for a steam generation system of a Coal Fired Thermal Power Plant (CFTPP) using the concept of the Markov method. A steam generation system provides a suitable amount of steam for the sound functioning of the plant. The system comprises five subsystems, i.e., High-Pressure Heater, Economizer, Boiler Drum, Water Tubes, and Super Heater.  First, the transition diagram of the concerned system is designed based on the state probabilities of various subsystems. The differential equations are derived based on the mnemonic rule. After that, the performability model is developed by using the normalizing condition. The performability levels for various subsystems are obtained by placing the appropriate value of failure and repair rates in the developed model. The performability of each subsystem is evaluated based on performability matrices. It is observed that the economizer subsystem is most critical in which the availability increased from 0.7640 to 0.8827, i.e. (11.87 %). In contrast, boiler drum is the least crucial subsystem with availability enhanced from 0.8627 to 0.8657 (i.e., 0.3 %). The results show that the economizer subsystem must be given top priority, and the boiler drum be given the least priority from the maintenance outlook. The performability levels obtained through the Markov method are compared with those obtained through the Artificial Neural Network to validate. Moreover, machine learning (artificial neural network) and optimization technique (particle swarm optimization) is also employed to check the adequacy of the results and optimized process parameters.•The aim of the present study is evaluate the performance of steam generation system of a coal fired thermal power plant.•The probabilistic approach (i.e. Makov Method) is used to formulate the transition diagram of the steam generation system. Then, the first-order differential equations are obtained using the mnemonic rule and further solved recursively.•The results show that the economizer system must be given top priority, and the boiler drum subsystem must be given the least priority from the maintenance outlook.

A novel statistical method for behaviour sequence analysis and its application to birdsong.

Complex vocal signals, such as birdsong, contain acoustic elements that differ in both order and duration. These elements may convey socially relevant meaning, both independently and through their interactions, yet statistical methods that combine order and duration data to extract meaning have not, to our knowledge, been fully developed. Here we design novel semi-Markov methods, Bayesian estimation and classification trees to extract order and duration information from behavioural sequences and apply these methods to songs produced by male European starlings, Sturnus vulgaris, in two social contexts in which the function of song differs: a spring (breeding) and autumn (nonbreeding) context. Additionally, previous data indicate that damage to the medial preoptic nucleus (POM), a brain area known to regulate male sexually motivated behaviour, affects structural aspects of starling song such that males in a sexually relevant context (i.e. spring) sing shorter songs than appropriate for this context. We further test the utility of our statistical approach by comparing attributes of song structure in POM-lesioned males to song produced by control spring and autumn males. Spring and autumn songs were statistically separable based on the duration and order of phrase types. Males produced more structurally complex aspects of song in spring than in autumn. Spring song was also longer and more stereotyped than autumn song, both attributes used by females to select mates. Songs produced by POM-lesioned males in some cases fell between measures of spring and autumn songs but differed most from songs produced by autumn males. Overall, these statistical methods can effectively extract biologically meaningful information contained in many behavioural sequences given sufficient sample sizes and replication numbers.

A computational model for predicting fusion peptide of retroviruses.

As a pivotal domain within envelope protein, fusion peptide (FP) plays a crucial role in pathogenicity and therapeutic intervention. Taken into account the limited FP annotations in NCBI database and absence of FP prediction software, it is urgent and desirable to develop a bioinformatics tool to predict new putative FPs (np-FPs) in retroviruses. In this work, a sequence-based FP model was proposed by combining Hidden Markov Method with similarity comparison. The classification accuracies are 91.97% and 92.31% corresponding to 10-fold and leave-one-out cross-validation. After scanning sequences without FP annotations, this model discovered 53,946 np-FPs. The statistical results on FPs or np-FPs reveal that FP is a conserved and hydrophobic domain. The FP software programmed for windows environment is available at https://sourceforge.net/projects/fptool/files/?source=navbar.