Update on the pharmacology of Spirulina (Arthrospira), an unconventional food

Spirulina (Arthrospira), a filamentous, unicellular alga, is a cyanobacterium grown in certain countries as food for human and animal consumption. It is also used to derive additives in pharmaceuticals and foods. This alga is a rich source of proteins, vitamins, amino acids, minerals, and other nutrients. Its main use, therefore, is as a food supplement. Over the last few years, however, it has been found to have many additional pharmacological properties. Thus, it has been experimentally proven, in vivo and in vitro that it is effective to treat certain allergies, anemia, cancer, hepatotoxicity, viral and cardiovascular diseases, hyperglycemia, hyperlipidemia, immunodeficiency, and inflammatory processes, among others. Several of these activities are attributed to Spirulina itself or to some of its components including fatty acids omega-3 or omega-6, beta-carotene, alpha-tocopherol, phycocyanin, phenol compounds, and a recently isolated complex, Ca-Spirulan (Ca-SP). This paper aims to update and critically review the results published over the last few years with regards to these properties. The conclusion is that even if this cyanobacterium has been one of the most extensively studied from the chemical, pharmacological and toxicological points of view, it is still necessary to expand the research in order to have more consistent data for its possible use in human beings.     

Surface Modification of Titania Nanoparticles Using Ultrathin Ceramic Films

Surface modification of titania nanoparticles was achieved by coating them with ultrathin alumina films using atomic layer deposition. The coating process was performed in a fluidized bed reactor at low pressure and under mechanical vibration. Films were deposited using self-limiting, sequential surface reactions of trimethylaluminum and water. The composition of alumina-coated particles was verified using infrared spectroscopy. The deposited films had an average growth rate of 0.2 nm/coating cycle and were highly uniform and conformal as demonstrated by transmission electron microscopy and X-ray spectroscopy. Deposited alumina films were amorphous as verified through X-ray diffraction and high-resolution electron microscopy. The coating process did not promote particle sintering as validated via particle size and surface area analysis.     

Effect of elemental sulfur in the crosslinking reaction of poly(vinyl chloride) with tetramethyl thiuram disulfide

The effect of elemental sulfur (S₈) and tetramethyl thiuram disulfide (TMTD) on the chemical crosslinking of PVC is studied. The tensile strength, elongation at break, and gel content of crosslinked and uncrosslinked PVC molded samples, cured at different times in hot air, are evaluated. It was found that crosslinking PVC with TMTD improves its mechanical properties, and adding S₈ produces a slight improvement. Also, the S₈ presence accelerated the PVC/TMTD crosslinking reaction when the crosslinking reaction was carried out under shear forces, but not when the curing was done in hot air, where the S₈ retards the process.     

Frontline robots in tourism and hospitality: service enhancement or cost reduction?

Electronic Markets - Robots are being implemented in many frontline services, from waiter robots in restaurants to robotic concierges in hotels. A growing number of firms in hospitality and tourism...     

Mechanochemical tuning of molecular weight distribution of styrene homopolymers as postpolymerization modification in solvent-free solid-state

Mechanochemical degradation by planetary ball milling (PM) is used for postpolymerization modification of styrene homopolymers (PS). A complete factorial design was chosen to study the effect of radical scavengers, milling time, initial molecular weight, and revolution radius (R_p), on the shape of molecular weight distributions (MWDs) of PS. Size-exclusion chromatography analysis shows the feasibility of fine-tuning MWD of PS at up to 40% conversion. Distributions ranged from unimodal to bimodal in a PM with R_p = 150 mm at different stage of milling, whereas in a PM with R_p of 60.8 mm the adjustment of unimodal distributions is achieved. Initial polydispersity is more important to develop bimodal distributions when compared with initial molecular weight. Fourier transform infrared and X-ray electron spectrometry analysis show some suppression of PS degradation and complete oxidation inhibition of macromolecular radicals with the incorporation of radical scavengers, which we considered as additional aids when adjusting the MWDs.     

Parallel simulation of Population Dynamics P systems: updates and roadmap

Population Dynamics P systems are a type of multienvironment P systems that serve as a formal modeling framework for real ecosystems. The accurate simulation of these probabilistic models, e.g. with Direct distribution based on Consistent Blocks Algorithm, entails large run times. Hence, parallel platforms such as GPUs have been employed to speedup the simulation. In 2012, the first GPU simulator of PDP systems was presented. However, it was able to run only randomly generated PDP systems. In this paper, we present current updates made on this simulator, involving an input modu le for binary files and an output module for CSV files. Finally, the simulator has been experimentally validated with a real ecosystem model, and its performance has been tested with two high-end GPUs: Tesla C1060 and K40.     

Pose consensus in networks of heterogeneous robots with variable time delays

This paper proposes a novel pose (position and orientation) consensus controller for networks of heterogeneous robots modeled in the operational space. The proposed controller is a distributed proportional plus damping scheme that, with a slight modification, solves both the leader–follower and leaderless consensus problems. A singularity‐free representation, unit quaternion, is used to describe the robots orientation, and the network is represented by an undirected and connected interconnection graph. Furthermore, it is shown that the controller is robust to interconnection variable time delays. Experiments with a network of two 6‐degrees‐of‐freedom robots are presented to illustrate the performance of the proposed scheme. Copyright © 2014 John Wiley & Sons, Ltd.     

Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank

The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six‐step procedure. The back‐propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.     

Real-time lane tracking using Rao-Blackwellized particle filter

A novel approach to real-time lane modeling using a single camera is proposed. The proposed method is based on an efficient design and implementation of a particle filter which applies the concepts of the Rao-Blackwellized particle filter (RBPF) by separating the state into linear and non-linear parts. As a result the dimensionality of the problem is reduced, which allows the system to perform in real-time in embedded systems. The method is used to determine the position of the vehicle inside its own lane and the curvature of the road ahead to enhance the performance of advanced driver assistance systems. The effectiveness of the method has been demonstrated implementing a prototype and testing its performance empirically on road sequences with different illumination conditions (day and nightime), pavement types, traffic density, etc. Results show that our proposal is capable of accurately determining if the vehicle is approaching the lane markings (Lane Departure Warning), and the curvature of the road ahead, achieving processing times below 2 ms per frame for laptop CPUs, and 12 ms for embedded CPUs.     

Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.