An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.     

Development of a water-in-oil-in-water multiple emulsion system integrating biomimetic aqueous-core lipid nanodroplets for protein entity stabilization. Part II: process and product characterization

The aqueous-core enclosed in lipid nanoballoons integrating multiple emulsions of the type water-in-oil-in-water mimic, at least in theory, the environment within viable cells, thus being suitable for housing hydrophilic protein entities such as bioactive proteins, peptides and bacteriophage particles. This study reports a complete physicochemical characterization of optimized biomimetic aqueous-core lipid nanoballoons housing hydrophilic (BSA) protein entities, evolved from a statistical 2³×3¹ factorial design study (three variables at two levels and one variable at three levels) that was the subject of the first paper of a series of three, aiming at complete stabilization of the three-dimensional structure of protein entities attempted via housing the said molecular entities within biomimetic aqueous-core lipid nanoballoons integrating a multiple (W/O/W) emulsion. The statistical factorial design followed led to the production of an optimum W/O/W multiple emulsion possessing quite homogeneous particles with an average hydrodynamic size of (186.2?±?2.6) nm and average Zeta potential of (?36.5?±?0.9) mV, and exhibiting a polydispersity index of 0.206?±?0.014. Additionally, the results obtained for the diffusion coefficient of the lipid nanoballoons integrating the optimized W/O/W multiple emulsion were comparable and of the same order of magnitude (10^?12 m² s^?1) as those published by other authors since, typically, diffusion coefficients for molecules range from 10^?10 to 10^?7 m² s^?1, but diffusion coefficients for nanoparticles are typically of the order of magnitude of 10^?12 m² s^?1.     

Object Recognition in X-ray Testing Using Adaptive Sparse Representations

In recent years, X-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items such as handguns and explosives. Human operators complete these tasks because automated recognition in baggage inspection is far from perfect. Research and development on X-ray testing is, however, ongoing into new approaches that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition by proposing a new approach called Adaptive Sparse Representation (XASR+). It consists of two stages: learning and testing. In the learning stage, for each object of training dataset, several patches are extracted from its X-ray images in order to construct representative dictionaries. A stop-list is used to remove very common words of the dictionaries. In the testing stage, test patches of the test image are extracted, and for each test patch a dictionary is built concatenating the ‘best’ representative dictionary of each object. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification methodology. Finally, the test image is classified by patch voting. Thus, our approach is able to deal with less constrained conditions including some contrast variability, pose, intra-class variability, size of the image and focal distance. We tested the effectiveness of our method for the detection of four different objects. In our experiments, the recognition rate was more than 97 % in each class, and more than 94 % if the object is occluded less than 15 %. Results show that XASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature.     

Machine scheduling in underground mining: an application in the potash industry

In this paper, a scheduling problem that occurs in potash mining is introduced, where a block excavation sequence has to be found taking into account a limited number of underground machines as well as safety-related restrictions. The aim is to minimize the maximum completion time of excavations, i.e., the makespan. The resulting problem can be transformed into a hybrid flow shop scheduling problem with reentry, unrelated machines, and job-precedences. A mixed-integer linear model is presented and small-scale instances are solved with CPLEX. In order to tackle medium- and large-scale instances heuristically, a basic and an advanced multi-start algorithm are developed, based on a specific priority rule-based construction procedure. In addition, a modified version of the Giffler and Thompson procedure is applied. Computational experiments are conducted on problem instances derived from real-world data in order to evaluate the performances of the proposed solution procedures.     

Atomically Precise Prediction of 2D Self‐Assembly of Weakly Bonded Nanostructures: STM Insight into Concentration‐Dependent Architectures

A joint experimental and computational study is reported on the concentration‐dependant self‐assembly of a flat C₃‐symmetric molecule on a graphite surface. As a model system a tripodal molecule, 1,3,5‐tris(pyridin‐3‐ylethynyl)benzene, has been chosen, which can adopt either C_3h or C_s symmetry when planar, as a result of pyridyl rotation along the alkynyl spacers. Density functional theory (DFT) simulations of 2D nanopatterns with different surface coverage reveal that the molecule can generate different types of self‐assembled motifs. The stability of fourteen 2D patterns and the influence of concentration are analyzed. It is found that ordered, densely packed monolayers and 2D porous networks are obtained at high and low concentrations, respectively. A concentration‐dependent scanning tunneling microscopy (STM) investigation of this molecular self‐assembly system at a solution/graphite interface reveals four supramolecular motifs, which are in perfect agreement with those predicted by simulations. Therefore, this DFT method represents a key step forward toward the atomically precise prediction of molecular self‐assembly on surfaces and at interfaces.     

Speeding up a Rollout algorithm for complex parallel machine scheduling

Rollout methodology is a constructive metaheuristic algorithm and its main characteristics are its modularity, the adaptability to different objectives and constraints and the easiness of implementation. Multi-heuristic Rollout extends the Rollout by incorporating several constructive heuristics in the Rollout framework and it is able to easily incorporate human experience inside its research patterns to fulfil complex requirements dictated by the application at hand. However, a drawback for both Rollout and multi-heuristic Rollout is often represented by the required computation time. This paper proposes some alternatives of the full multi-heuristic Rollout algorithm aimed at improving the efficiency by reducing the computational effort while preserving the effectiveness. Namely, we propose dynamic heuristics pruning and candidates reduction strategies. As illustrative case studies, we analyse complex deterministic identical parallel machine scheduling problems showing how Rollout procedures can be used to tackle several additional constraints arising in real contexts. More specifically, we considered both standard (batch production, family set-ups, release, due dates, etc.) and non-standard (machine unavailabilities, maximum campaign size) scheduling constraints. An extensive campaign of computational experiments shows the behaviour of the multi-heuristic Rollout approach and the effectiveness of the different proposed speed-up methods.     

Photoalignment and Surface‐Relief‐Grating Formation are Efficiently Combined in Low‐Molecular‐Weight Halogen‐Bonded Complexes

It is demonstrated that halogen bonding can be used to construct low‐molecular‐weight supramolecular complexes with unique light‐responsive properties. In particular, halogen bonding drives the formation of a photoresponsive liquid‐crystalline complex between a non‐mesogenic halogen bond‐donor molecule incorporating an azo group, and a non‐mesogenic alkoxystilbazole moiety, acting as a halogen bond‐acceptor. Upon irradiation with polarized light, the complex exhibits a high degree of photoinduced anisotropy (order parameter of molecular alignment > 0.5). Moreover, efficient photoinduced surface‐relief‐grating (SRG) formation occurs upon irradiation with a light interference pattern, with a surface‐modulation depth 2.4 times the initial film thickness. This is the first report on a halogen‐bonded photoresponsive low‐molecular‐weight complex, which furthermore combines a high degree of photoalignment and extremely efficient SRG formation in a unique way. This study highlights the potential of halogen bonding as a new tool for the rational design of high‐performance photoresponsive suprastructures.