Preference-based multiobjective artificial bee colony algorithm for optimization of superheated steam temperature control

In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel reference point based preference expression method is addressed.The fitness assignment function is defined based on the nondominated rank and the newly defined preference distance.An archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the archive.The experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved simultaneously.The simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.

Right pp Semigroups with Multiplicative Right Adequate Transversals

In this paper,the concept of right adequate transversals of rpp semigroups is introduced.We establish the structure of rpp semigroups with multiplicative right adequate transversals in terms of right normal bands and right adequate semigroups.In particular, some special cases are considered.

On-line Monitoring for Phosphorus Removal Process and Bacterial Community in Sequencing Batch Reactor

For efficient energy consumption and control of effluent quality, the cycle duration for a sequencing batch reactor (SBR) needs to be adjusted by real-time control according to the characteristics and loading of waste-water. In this study, an on-line information system for phosphorus removal processes was established. Based on the analysis for four systems with different ecological community structures and two operation modes, anaerobic-aerobic process and anaerobic-anaerobic process, the characteristic patterns of oxidation-reduction potential (ORP) and pH were related to phosphorous dynamics in the anaerobic, anoxic and aerobic phases, for determination of the end of phosphorous removal. In the operation mode of anaerobic-aerobic process, the pH profile in the anaerobic phase was used to estimate the relative amount of phosphorous accumulating organisms (PAOs) and glycogen accumulat-ing organisms (GAOs), which is beneficial to early detection of ecology community shifts. The on-line sensor val-ues of pH and ORP may be used as the parameters to adjust the duration for phosphorous removal and community shifts to cope with influent variations and maintain appropriate operation conditions.

Pilot Project on in Situ Aerobisation of Old Landfills： Results from Konstanz Dorfweiher Landfill

A new in situ treatment technique was being utilized on a part of the Dorfweiher landfill in Konstanz, Germany to reduce the aftercare period. From 2010 to 2012, the landfill was aerated intermittently with low pressure. Outgoing air was treated passively in an open biofilter which covers the landfill surface. The landfill was aerated by means of 80 air injection wells arranged area-wide in a 10 m grid. An elaborate measuring process and technological controls are being utilized in the pilot scheme. The data collected offer interesting insights about the processes during aeration, allowing optimization of the aeration strategy depending on changes of the conditions in the landfill. During the three-year aeration phase, conditions have changed inside the landfill section in various ways. In numerous zones of the landfill body, the aeration caused aerobic conditions with a decline of methane production. Accelerated settlings are measured up to 11%. The effects of the aerobic stabilization on the landfill are evaluated in a two-year monitoring phase started in 2013.

Multi-Domain Collaborative Recommendation with Feature Selection

Collaborative f iltering, as one of the most popular techniques, plays an important role in recommendation systems. However,when the user-item rating matrix is sparse,its performance will be degenerate. Recently,domain-specific recommendation approaches have been developed to address this problem.The basic idea is to partition the users and items into overlapping domains, and then perform recommendation in each domain independently. Here, a domain means a group of users having similar preference to a group of products. However, these domain-specific methods consisting of two sequential steps ignore the mutual benefi t of domain segmentation and recommendation. Hence, a unified framework is presented to simultaneously realize recommendation and make use of the domain information underlying the rating matrix in this paper. Based on matrix factorization,the proposed model learns both user preferences of multiple domains and preference selection vectors to select relevant features for each group of products. Besides, local context information is utilized from the user-item rating matrix to enhance the new framework.Experimental results on two widely used datasets, e.g., Ciao and Epinions, demonstrate the effectiveness of our proposed model.

Preference transfer model in collaborative filtering for implicit data

Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.

New insights into substrate folding preference of plant OSCs

Sterols and triterpenes are structurally diverse bioactive molecules generated through cyclization of linear 2,3-oxidosqualene. Based on carbocationic intermediates generated during initial substrate preorganization step, oxidosqualene cyclases （OSCs） are roughly segregated into protosteryl cation group that mainly catalyzes tetracyclic products and dammarenyl cation group which mostly generates pentacyclic products. However, in contrast to well-studied cascade of ring-forming reactions, little is known about the mechanism underlying the initial sub- strate folding process. Previously, we have identified a cucurbitadienol synthase （Bi） and its null allele bi （C393Y） from cucumber. By integration of homology modeling, residue coevolution and site-directed mutagenesis, we discover that four covarying amino acids including C393 constitute a dynamic domain that may be involved in substrate folding process for Bi. We also reveal a group of co-conserved residues that closely associated with the segregation of plant OSCs. These residues may act col- laboratively in choice of specific substrate folding inter- mediate for OSCs. Thus, engineer plant OSCs from into five-ringed producer. our findings open a door to four-ringed skeleton catalysts