'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

The 100th anniversary of the four-point probe technique: the role of probe geometries in isotropic and anisotropic systems

The electrical conductivity of solid-state matter is a fundamental physical property and can be precisely derived from the resistance measured via the four-point probe technique excluding contributions from parasitic contact resistances. Over time, this method has become an interdisciplinary characterization tool in materials science, semiconductor industries, geology, physics, etc, and is employed for both fundamental and application-driven research. However, the correct derivation of the conductivity is a demanding task which faces several difficulties, e.g. the homogeneity of the sample or the isotropy of the phases. In addition, these sample-specific characteristics are intimately related to technical constraints such as the probe geometry and size of the sample. In particular, the latter is of importance for nanostructures which can now be probed technically on very small length scales. On the occasion of the 100th anniversary of the four-point probe technique, introduced by Frank Wenner, in this review we revisit and discuss various correction factors which are mandatory for an accurate derivation of the resistivity from the measured resistance. Among others, sample thickness, dimensionality, anisotropy, and the relative size and geometry of the sample with respect to the contact assembly are considered. We are also able to derive the correction factors for 2D anisotropic systems on circular finite areas with variable probe spacings. All these aspects are illustrated by state-of-the-art experiments carried out using a four-tip STM/SEM system. We are aware that this review article can only cover some of the most important topics. Regarding further aspects, e.g. technical realizations, the influence of inhomogeneities or different transport regimes, etc, we refer to other review articles in this field.

Exposure to toxic Alexandrium minutum activates the detoxifying and antioxidant systems in gills of the oyster Crassostrea gigas

Harmful algal blooms of Alexandrium spp. dinoflagellates regularly occur in French coastal waters contaminating shellfish. Studies have demonstrated that toxic Alexandrium spp. disrupt behavioural and physiological processes in marine filter-feeders, but molecular modifications triggered by phycotoxins are less well understood. This study analyzed the mRNA levels of 7 genes encoding antioxidant/detoxifying enzymes in gills of Pacific oysters (Crassostrea gigas) exposed to a cultured, toxic strain of A. minutum, a producer of paralytic shellfish toxins (PST) or fed Tisochrysis lutea (T. lutea, formerly Isochrysis sp., clone Tahitian (T. iso)), a non-toxic control diet, in four repeated experiments. Transcript levels of sigma-class glutathione S-transferase (GST), glutathione reductase (GR) and ferritin (Fer) were significantly higher in oysters exposed to A. minutum compared to oysters fed T. lutea. The detoxification pathway based upon glutathione (GSH)-conjugation of toxic compounds (phase II) is likely activated, and catalyzed by GST. This system appeared to be activated in gills probably for the detoxification of PST and/or extra-cellular compounds, produced by A. minutum. GST, GR and Fer can also contribute to antioxidant functions to prevent cellular damage from increased reactive oxygen species (ROS) originating either from A. minutum cells directly, from oyster hemocytes during immune response, or from other gill cells as by-products of detoxification.

Sparse Signal Representation in Digital and Biological Systems

Theories of sparse signal representation, wherein a signal is decomposed as the sum of a small number of constituent elements, play increasing roles in both mathematical signal processing and neuroscience. This happens despite the differences between signal models in the two domains. After reviewing preliminary material on sparse signal models, I use work on compressed sensing for the electron tomography of biological structures as a target for exploring the efficacy of sparse signal reconstruction in a challenging application domain. My research in this area addresses a topic of keen interest to the biological microscopy community, and has resulted in the development of tomographic reconstruction software which is competitive with the state of the art in its field. Moving from the linear signal domain into the nonlinear dynamics of neural encoding, I explain the sparse coding hypothesis in neuroscience and its relationship with olfaction in locusts. I implement a numerical ODE model of the activity of neural populations responsible for sparse odor coding in locusts as part of a project involving offset spiking in the Kenyon cells. I also explain the validation procedures we have devised to help assess the model's similarity to the biology. The thesis concludes with the development of a new, simplified model of locust olfactory network activity, which seeks with some success to explain statistical properties of the sparse coding processes carried out in the network.

RESULTADOS DO 13º CONTECSI USP – CONGRESSO INTERNACIONAL DE GESTÃO DA TECNOLOGIA E SISTEMAS DE INFORMAÇÃO; 36th WCARS – WORLD CONTINUOUS AUDITING AND REPORTING SYSTEMS SYMPOSIUM; 2º TOI ECA USP

In this document, we wish to describe statistics, data and the importance of the 13th CONTECSI – International Conference on Information Systems and Technology Management, which took place in the University of São Paulo, from June 1st through 3rd and was organized by TECSI/EAC/FEA/USP/ECA/POLI. This report presents statistics of the 13th CONTECSI, Goals and Objectives, Program, Plenary Sessions, Doctoral Consortium, Parallel Sessions, Honorable Mentions and Committees. We would like to point out the huge importance of the financial aid given by CAPES, CNPq, FAPESP, as well as the support of FEA USP, POLI USP, ECA USP, ANPAD, AIS, ISACA, UNINOVE, Mackenzie, Universidade do Porto, Rutgers School/USA, São Paulo Convention Bureau and CCINT-FEA-USP.

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

Dielectric studies of Liquid Crystal Nanocomposites and Nanomaterial systems.

Liquid crystals (LCs) have revolutionized the display and communication technologies. Doping of LCs with inorganic nanoparticles such as carbon nanotubes, gold nanoparticles and ferroelectric nanoparticles have garnered the interest of research community as they aid in improving the electro-optic performance. In this thesis, we examine a hybrid nanocomposite comprising of 5CB liquid crystal and block copolymer functionalized barium titanate ferroelectric nanoparticles. This hybrid system exhibits a giant soft-memory effect. Here, spontaneous polarization of ferroelectric nanoparticles couples synergistically with the radially aligned BCP chains to create nanoscopic domains that can be rotated electromechanically and locked in space even after the removal of the applied electric field. The resulting non-volatile memory is several times larger than the non-functionalized sample and provides an insight into the role of non-covalent polymer functionalization. We also present the latest results from the dielectric and spectroscopic study of field assisted alignment of gold nanorods.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.

Auctioning versus grandfathering in cap-and-trade systems with market power and incomplete information

We compare auctioning and grandfathering as allocation mechanisms of emission permits when there is a secondary market with market power and firms have private information on their own abatement technologies. Based on real-life cases such as the EU ETS, we consider a multi-unit, multi-bid uniform auction. At the auction, each firm anticipates its role in the secondary market, either as a leader or a follower. This role affects each firms’ valuation of the permits (which are not common across firms) as well as their bidding strategies and it precludes the auction from generating a cost-effective allocation of permits, as it occurs in simpler auction models. Auctioning tends to be more cost-effective than grandfathering when the firms’ abatement cost functions are sufficiently different from one another, especially if the follower has lower abatement costs than the leader and the dispersion of the marginal costs is large enough.

Achieving Coherence between Strategies and Value Systems in Collaborative Networks

Part 8: Business Strategies Alignment

Ligand effects on the optical and chiroptical properties of the thiolated Au18cluster

The effect of chiral and achiral ligands protecting the inner Au9 core of the Au18(SR)14 cluster is studied based on density functional theory (DFT) and its corrected long-range interaction (DFT-D) approach. It was found that the electronic properties (energy levels) depend on the specific ligands, which induce distinct distortions on the Au–S framework. However, the substitution of S-c-C6H11 as SCH3 ligands may be considered to be correct given the obtained resemblance to the displayed bonding, optical and chiroptical properties. A further comparison of the CD and UV spectra displayed by the Au18 cluster protected by chiral and achiral ligands attests that more intense profiles are featured by ligands including phenyl rings and/or oxygen atoms such that the Au18 cluster protected by either achiral metamercaptobenzoic acid (m-MBA) or achiral SPh ligands displays more intense UV and CD signals. These results provide new insight into the effect of ligands on thiolated gold clusters

Thiolated Au18cluster: preferred Ag sites for doping, structures, and optical and chiroptical properties

Recently, the X-ray determined structure of the thiolated Au18 cluster has been reported. In this communication, we addressed a study of structures and chiroptical properties of thiolated Au18 cluster doped with up to ten Ag atoms, which have been calculated by Time Dependent Density Functional Theory (TD-DFT). The number of Ag atoms was steadily varied and more stable isomers showed optical and Circular Dichroism (CD) spectra distinct from that found for the parent Au18 cluster. Doping with more than four Ag atoms results in enhancement of the oscillator strength of the HOMO–LUMO peak and it is expected that this feature can be exploited for photoluminescence applications.

Sonochemical synthesis of CuO nanostructures and their morphology dependent optical and visible light driven photocatalytic properties

A controlled synthesis of CuO nanostructures with various morphologies were successfully achieved by presence/absence of low frequency (42 kHz) ultrasound with two different methods. The size, shape and morphology of the CuO nanostructures were tailored by altering the ultrasound, mode of addition and solvent medium. The crystalline structure and molecular vibrational modes of the prepared nanostructures were analysed through X-ray diffraction and FTIR measurement, respectively which confirmed that the nanostructures were phase pure high-quality CuO with monoclinic crystal structure. The morphological evaluation and elemental composition analysis were done using TEM and EDS attached with SEM, respectively. Furthermore, we demonstrated that the prepared CuO nanostructures could be served as an effective photocatalyst towards the degradation of methyl orange (MO) under visible light irradiation. Among the various nanostructures, the spherical shape CuO nanostructures were found to have the better catalytic activities towards MO dye degradation. The catalytic degradation performance of MO in the presence of CuO nanostructures showed the following order: spherical\nanorod \layered oval \nanoleaf \triangular \shuttles structures. The influence of loading and reusability of catalyst revealed that the efficiency of visible light assisted degradation of MO was effectively enhanced and more than 95 % of degradation was achieved after 3 cycles