A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles

The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.

Sensitive bi-enzymatic biosensor based on polyphenoloxidases–gold nanoparticles–chitosan hybrid film–graphene doped carbon paste electrode for carbamates detection

A bi-enzymatic biosensor (LACC–TYR–AuNPs–CS/GPE) for carbamates was prepared in a single step by electrodeposition of a hybrid film onto a graphene doped carbon paste electrode (GPE). Graphene and the gold nanoparticles (AuNPs) were morphologically characterized by transmission electron microscopy, X-ray photoelectron spectroscopy, dynamic light scattering and laser Doppler velocimetry. The electrodeposited hybrid film was composed of laccase (LACC), tyrosinase (TYR) and AuNPs entrapped in a chitosan (CS) polymeric matrix. Experimental parameters, namely graphene redox state, AuNPs:CS ratio, enzymes concentration, pH and inhibition time were evaluated. LACC–TYR–AuNPs–CS/GPE exhibited an improved Michaelis–Menten kinetic constant (26.9 ± 0.5 M) when compared with LACC–AuNPs–CS/GPE (37.8 ± 0.2 M) and TYR–AuNPs–CS/GPE (52.3 ± 0.4 M). Using 4-aminophenol as substrate at pH 5.5, the device presented wide linear ranges, low detection limits (1.68×10− 9 ± 1.18×10− 10 – 2.15×10− 7 ± 3.41×10− 9 M), high accuracy, sensitivity (1.13×106 ± 8.11×104 – 2.19×108 ± 2.51×107 %inhibition M− 1), repeatability (1.2–5.8% RSD), reproducibility (3.2–6.5% RSD) and stability (ca. twenty days) to determine carbaryl, formetanate hydrochloride, propoxur and ziram in citrus fruits based on their inhibitory capacity on the polyphenoloxidases activity. Recoveries at two fortified levels ranged from 93.8 ± 0.3% (lemon) to 97.8 ± 0.3% (orange). Glucose, citric acid and ascorbic acid do not interfere significantly in the electroanalysis. The proposed electroanalytical procedure can be a promising tool for food safety control.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Distributed Energy Resources Management using Plug-in Hybrid Electric Vehicles as a Fuel-Shifting Demand Response Resource

In the smart grids context, distributed energy resources management plays an important role in the power systems’ operation. Battery electric vehicles and plug-in hybrid electric vehicles should be important resources in the future distribution networks operation. Therefore, it is important to develop adequate methodologies to schedule the electric vehicles’ charge and discharge processes, avoiding network congestions and providing ancillary services. This paper proposes the participation of plug-in hybrid electric vehicles in fuel shifting demand response programs. Two services are proposed, namely the fuel shifting and the fuel discharging. The fuel shifting program consists in replacing the electric energy by fossil fuels in plug-in hybrid electric vehicles daily trips, and the fuel discharge program consists in use of their internal combustion engine to generate electricity injecting into the network. These programs are included in an energy resources management algorithm which integrates the management of other resources. The paper presents a case study considering a 37-bus distribution network with 25 distributed generators, 1908 consumers, and 2430 plug-in vehicles. Two scenarios are tested, namely a scenario with high photovoltaic generation, and a scenario without photovoltaic generation. A sensitivity analyses is performed in order to evaluate when each energy resource is required.

Backside-surface imprinting as a new strategy to generate specific plastic antibody materials

A backside protein-surface imprinting process is presented herein as a novel way to generate specific synthetic antibody materials. The template is covalently bonded to a carboxylated-PVC supporting film previously cast on gold, let to interact with charged monomers and surrounded next by another thick polymer. This polymer is then covalently attached to a transducing element and the backside of this structure (supporting film plus template) is removed as a regular “tape”. The new sensing layer is exposed after the full template removal, showing a high density of re-binding positions, as evidenced by SEM. To ensure that the templates have been efficiently removed, this re-binding layer was cleaned further with a proteolytic enzyme and solution washout. The final material was named MAPS, as in the back-side reading of SPAM, because it acts as a back-side imprinting of this recent approach. It was able to generate, for the first time, a specific response to a complex biomolecule from a synthetic material. Non-imprinted materials (NIMs) were also produced as blank and were used as a control of the imprinting process. All chemical modifications were followed by electrochemical techniques. This was done on a supporting film and transducing element of both MAPS and NIM. Only the MAPS-based device responded to oxLDL and the sensing layer was insensitive to other serum proteins, such as myoglobin and haemoglobin. Linear behaviour between log(C, μg mL−1) versus charged tranfer resistance (RCT, Ω) was observed by electrochemical impedance spectroscopy (EIS). Calibrations made in Fetal Calf Serum (FCS) were linear from 2.5 to 12.5 μg mL−1 (RCT = 946.12 × log C + 1590.7) with an R-squared of 0.9966. Overall, these were promising results towards the design of materials acting close to the natural antibodies and applied to practical use of clinical interest.

Hybrid Maneuver for Gradient Search with Multiple Coordinated AUVs

This work presents a hybrid maneuver for gradient search with multiple AUV's. The mission consists in following a gradient field in order to locate the source of a hydrothermal vent or underwater freshwater source. The formation gradient search exploits the environment structuring by the phenomena to be studied. The ingredients for coordination are the payload data collected by each vehicle and their knowledge of the behaviour of other vehicles and detected formation distortions.

Life Cycle Assessment of the Hybrid Vehicle – Toyota Prius

Die Luftverschmutzung, die globale Erwärmung sowie die Verknappung der endlichen Ressourcen sind die größten Bedenken der vergangenen Jahrzehnte. Die Nachfrage nach jeglicher Mobilität steigt rapide. Dementsprechend bemüht ist die Automobilindustrie Lösungen für Mobilität unter dem Aspekt der Nachhaltigkeit und dem Umweltschutz anzubieten. Die Elektrifizierung hat sich hierbei als der beste Weg herausgestellt, um die Umweltprobleme sowie die Abhängigkeit von fossilen Brennstoffen zu lösen. Diese Arbeit soll einen Einblick über die Umweltauswirkungen des Hybridfahrzeuges Toyota Prius geben. Hierbei findet eine Gliederung in vier verschiedene Lebensphasen statt. Im Anschluss bietet die Sachbilanz die Möglichkeit die Umweltauswirkungen mit verschiedenen Antriebsmöglichkeiten und Brennstoffen zu vergleichen. Das Modell hat gezeigt, dass der Toyota Prius während der Nutzung einen hohen Einfluss auf das Treibhauspotenzial aufweist. Durch die Nutzung anderer Brennstoffe, wie beispielsweise Ethanol oder Methanol lassen sich die Auswirkungen am Treibhauspotenzial sowie der Verbrauch an abiotischen Ressourcen reduzieren. Vergleicht man die Elektromobilität mit der konventionellen, so ist festzustellen, dass diese Art der Mobilität die derzeit beste Möglichkeit zur Reduzierung der Umweltbelastungen bietet. Die Auswirkungen der Elektromobilität sind im hohen Maße abhängig von der Art des verwendeten Strommixes.

Is mental practice an effective adjunct therapeutic strategy for upper limb motor restoration after stroke? A systematic review and meta- analysis

Stroke is one of the most common conditions requiring rehabilitation, and its motor impairments are a major cause of permanent disability. Hemiparesis is observed by 80% of the patients after acute stroke. Neuroimaging studies showed that real and imagined movements have similarities regarding brain activation, supplying evidence that those similarities are based on the same process. Within this context, the combination of mental practice (MP) with physical and occupational therapy appears to be a natural complement based on neurorehabilitation concepts. Our study seeks to investigate if MP for stroke rehabilitation of upper limbs is an effective adjunct therapy. PubMed (Medline), ISI knowledge (Institute for Scientific Information) and SciELO (Scientific Electronic Library) were terminated on 20 February 2015. Data were collected on variables as follows: sample size, type of supervision, configuration of mental practice, setting the physical practice (intensity, number of sets and repetitions, duration of contractions, rest interval between sets, weekly and total duration), measures of sensorimotor deficits used in the main studies and significant results. Random effects models were used that take into account the variance within and between studies. Seven articles were selected. As there was no statistically significant difference between the two groups (MP vs control), showed a - 0.6 (95% CI: -1.27 to 0.04), for upper limb motor restoration after stroke. The present meta-analysis concluded that MP is not effective as adjunct therapeutic strategy for upper limb motor restoration after stroke.

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.

Crowdsourcing innovation: a strategy to leverage enterprise innovation

Innovation is recognized by academics and practitioners as an essential competitive enabler for any company to survive, to remain competitive and to grow. Investments in tasks of R&D have not always brought the expected results. But that doesn't mean that the outcomes would not be useful to other companies of the same business area or even from another area. Thus, there is much knowledge already available in the market that can be helpful to some and profitable to others. So, the ideas and expertise can be found outside a company's boundaries and also exported from within. Information, knowledge, experience, wisdom is already available in the millions of the human beings of this planet, the challenge is to use them through a network to produce new ideas and tips that can be useful to a company with less costs. This was the reason for the emergence of the area of crowdsourcing innovation. Crowdsourcing innovation is a way of using the Web 2.0 tools to generate new ideas through the heterogeneous knowledge available in the global network of individuals highly qualified and with easy access to information and technology. So, a crowdsourcing innovation broker is an organization that mediates the communication and relationship between the seekers - companies that aspire to solve some problem or to take advantage of any business opportunity - with a crowd that is prone to give ideas based on their knowledge, experience and wisdom. This paper makes a literature review on models of open innovation, crowdsourcing innovation, and technology and knowledge intermediaries, and discusses this new phenomenon as a way to leverage the innovation capacity of enterprises. Finally, the paper outlines a research design agendafor explaining crowdsourcing innovation brokering phenomenon, exploiting its players, main functions, value creation process, and knowledge creation in order to define a knowledge metamodel of such intermediaries.