MASCEM: a multiagent system that simulates competitive electricity markets

Around the world, the electricity industry, which has long been dominated by vertically integrated utilities, is experiencing major changes in the structure of its markets and regulations. Owing to new regulations, it's evolving into a distributed industry in which market forces drive electricity's price. The industry is becoming competitive; a market environment is replacing the traditional centralized-operation approach. This transformation is often called the deregulation of the electricity market. MASCEM, a multiagent simulator system, is a valuable framework for evaluating new rules, new behavior, and new participants in the numerous electricity markets that are moving toward liberalization and competition.     

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

AKT, is a serine/threonine protein kinase comprising three isoforms—namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT’ inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.     

Fabry Disease and the Heart: A Comprehensive Review

Fabry disease (FD) is an X-linked lysosomal storage disorder caused by mutations of the GLA gene that result in a deficiency of the enzymatic activity of α-galactosidase A and consequent accumulation of glycosphingolipids in body fluids and lysosomes of the cells throughout the body. GB3 accumulation occurs in virtually all cardiac cells (cardiomyocytes, conduction system cells, fibroblasts, and endothelial and smooth muscle vascular cells), ultimately leading to ventricular hypertrophy and fibrosis, heart failure, valve disease, angina, dysrhythmias, cardiac conduction abnormalities, and sudden death. Despite available therapies and supportive treatment, cardiac involvement carries a major prognostic impact, representing the main cause of death in FD. In the last years, knowledge has substantially evolved on the pathophysiological mechanisms leading to cardiac damage, the natural history of cardiac manifestations, the late-onset phenotypes with predominant cardiac involvement, the early markers of cardiac damage, the role of multimodality cardiac imaging on the diagnosis, management and follow-up of Fabry patients, and the cardiac efficacy of available therapies. Herein, we provide a comprehensive and integrated review on the cardiac involvement of FD, at the pathophysiological, anatomopathological, laboratory, imaging, and clinical levels, as well as on the diagnosis and management of cardiac manifestations, their supportive treatment, and the cardiac efficacy of specific therapies, such as enzyme replacement therapy and migalastat.     

The biodrying process of sewage sludge – a review

Abstract

Sewage treatment can be classified into three phases: liquid, gaseous, and solid. Treatment of solids is performed in several steps, and the greatest difficulty in removing water from sewage sludge is due to the bound water present in the sludge. Thus, biodrying, a drying method that can be classified as biological, has been recently applied to treat this type of waste. This review paper aims to report and compile data from 49 biodrying assays of sewage sludge found in the literature (through the present, 2019) into one synthesis table, to discuss and compare the numerous variables found in these papers and their implications for biodrying, and to suggest possibilities for future research. This paper additionally intends to improve knowledge of biodrying and to consequently contribute to the monitoring and understanding of the process.     

Synthesis, characterization and crystal structure of a novel thiocyanate–ruthenium(II) complex

The cis-[Ru(dppb)(Me-bipy)(NCS)₂], dppb = 1,4-bis (diphenylphosphino)butane, Me-bipy = 4,4′-dimethyl-2,2′-bipyridine, and NCS = thiocyanate, was synthesized and characterized by spectroscopic and electrochemical techniques and its structure was determined by crystal X-ray analysis. The crystal structure reveals that the coordination geometry around the Ru(II) center is distorted octahedron where two molecules of thiocyanate are bonded to the ruthenium through nitrogen atom in cis orientation. The half-wave formal potential value E_1/2 = 0.8 V (versus Ag/AgCl) observed is considerable higher than that for the cis-[RuCl₂(dppb)(Me-bipy)] complex, E_1/2 = 0.6 V (versus Ag/AgCl), well illustrating the strong π-acceptor effect the NCS ligand toward the backbonding interaction with the Ru(II) metal center. The MLCT absorption bands of the thiocyanate complex present a higher molar absorptivity (about 12%) compared with the cis-[RuCl₂(dppb)(Me-bipy)] complex, in the same experimental conditions. These properties make the complex potentially promising for the photosensitization process.     

Scalable and QoS-Aware Dynamic Slot Assignment and Piconet Partitioning to Enhance the Performance of Bluetooth Ad Hoc Networks

Bluetooth is a radio technology for Wireless Personal Area Networks in the 2.4 GHz ISM frequency band and allows short-range devices to be connected in the form of ad hoc networks. The Bluetooth medium access control protocol is based on a strict master/slave concept wherein any communication between slave devices has to go through the master. While this model is simple, the use of such a nonoptimal packet forwarding scheme incurs much longer delays between any two slave-devices as double the bandwidth is used by the master. In addition, if two or more devices want to communicate as a group, this can only be achieved by either multiple unicast transmissions or a piconet-wide broadcast from the master. To handle these issues efficiently, we propose a novel combination of Dynamic Slot Assignment (DSA) and piconet partitioning. With DSA, the piconet master dynamically assigns slots to slaves so as to allow them to communicate directly with each other without any intervention from the master. Our proposed communication architecture provides for enhanced Quality of Service (QoS), better admission control, and multidevice conversation, which make a multicast-like communication feasible within the piconet. To widen the scope of DSA, we propose a QoS-aware Enhanced DSA (EDSA) version where dynamic piconet partitioning and scatternet support are exploited by grouping devices into piconets as per their connection endpoints, enabling it to be employed over a scatternet. We have performed extensive simulations and observe that these schemes drastically enhance Bluetooth performance in terms of the delay and the throughput, while significantly reducing the network power consumption.     

Identification of components of Brazilian honey by 1H NMR and classification of its botanical origin by chemometric methods

The potential of NMR spectroscopy to differentiate honeys concerning to the nectar employed in its production was evaluated. The application of chemometric methods to ¹H NMR spectra has allowed to discriminate the honeys produced in the state of São Paulo, being identified the signals of responsible substances for the discrimination. Application of PCA and HCA methods to ¹H NMR data have resulted in the natural clustering of the samples. Wildflower honeys were characterized by higher concentration of phenylalanine and tyrosine. Citrus honeys showed higher amounts of sucrose than other compounds, while eucalyptus honeys had higher amount of lactic acid than the others. Assa-peixe honeys showed spectra similar to eucalyptus and citrus. Sugar-cane honeys showed some signals similar to eucalyptus and citrus honeys, but also showed the tyrosine and phenylalanine signals. Adulterated honeys showed 5-hydroxymethylfurfural, citric acid and ethanol signals. KNN, SIMCA and PLS-DA methods were used to build predictive models for honey classification. In the commercial honeys prediction KNN, SIMCA and PLS-DA models correctly classified 66.7; 22.2 and 72.2% of the samples, respectively.