Water Demand Forecasting Model for the Metropolitan Area of São Paulo,Brazil

This work is concerned with forecasting water demand in the metropolitan area of São Paulo (MASP) through water consumption, meteorological and socio-environmental variables using an Artificial Neural Network (ANN) system. Possible socio-environmental and meteorological conditions affecting water consumption at Cantareira water treatment station (WTS) in the MASP, Brazil were analyzed for the year 2005. Eight model configurations were developed and used for the Cantareira WTS. The best performance was obtained for 12-h average of the input variables. The ANN model performed best with three times steps in advance. The hourly forecasting was obtained with acceptable error levels. Model results indicate an overall tendency for small errors. The proposed method is useful tool for water demand forecasting and water systems management. The paper is an important contribution since it takes into account weather variables and introduces some diagnostic studies on water consumption in one of the largest urban environments of the planet with its unique peculiarities such as anthropic affects on weather and climate that feeds back into the water consumption. The averaging is a low pass filter indeed and we used it to improve Signal to Noise Ratio (SNR).     

Engineered ecosystem for on-site wastewater treatment in tropical areas

There is a worldwide demand for decentralized wastewater treatment options. An on-site engineered ecosystem (EE) treatment plant was designed with a multistage approach for small wastewater generators in tropical areas. The array of treatment units included a septic tank, a submersed aerated filter, and a secondary decanter followed by three vegetated tanks containing aquatic macrophytes intercalated with one tank of algae. During 11 months of operation with a flow rate of 52 L h(-1), the system removed on average 93.2% and 92.9% of the chemical oxygen demand (COD) and volatile suspended solids (VSS) reaching final concentrations of 36.3 ± 12.7 and 13.7 ± 4.2 mg L(-1), respectively. Regarding ammonia-N (NH(4)-N) and total phosphorus (TP), the system removed on average 69.8% and 54.5% with final concentrations of 18.8 ± 9.3 and 14.0 ± 2.5 mg L(-1), respectively. The tanks with algae and macrophytes together contributed to the overall nutrient removal with 33.6% for NH(4)-N and 26.4% for TP. The final concentrations for all parameters except TP met the discharge threshold limits established by Brazilian and EU legislation. The EE was considered appropriate for the purpose for which it was created.     

Glitch and Laser Fault Attacks onto a Secure AES Implementation on a SRAM-Based FPGA

Programmable devices are an interesting alternative when implementing embedded systems on a low-volume scale. In particular, the affordability and the versatility of SRAM-based FPGAs make them attractive with respect to ASIC implementations. FPGAs have thus been used extensively and successfully in many fields, such as implementing cryptographic accelerators. Hardware implementations, however, must be protected against malicious attacks, e.g. those based on fault injections. Protections have been usually evaluated on ASICs, but FPGAs can be vulnerable as well. This work presents thus fault injection attacks against a secured AES architecture implemented on a SRAM-based FPGA. The errors are injected during the computation by means of voltage glitches and laser attacks. To our knowledge, this is one of the first works dealing with dynamic laser fault injections. We show that fault attacks on SRAM-based FPGAs may behave differently with respect to attacks against ASIC, and they need therefore to be addressed by specific countermeasures, that are also discussed in this paper. In addition, we discuss the different effects obtained by the two types of attacks.     

Charge Photogeneration in Non-Fullerene Organic Solar Cells: Influence of Excess Energy and Electrostatic Interactions

In organic solar cells, photogenerated singlet excitons form charge transfer (CT) complexes, which subsequently split into free charge carriers. Here, the contributions of excess energy and molecular quadrupole moments to the charge separation process are considered. The charge photogeneration in two separate bulk heterojunction systems consisting of the polymer donor PTB7-Th and two non-fullerene acceptors, ITIC and h-ITIC, is investigated. CT state dissociation in these donor–acceptor systems is monitored by charge density decay dynamics obtained from transient absorption experiments. The electric field dependence of charge carrier generation is studied at different excitation energies by time delayed collection field (TDCF) and sensitive steady-state photocurrent measurements. Upon excitation below the optical gap, free charge carrier generation becomes less field dependent with increasing photon energy, which challenges the view of charge photogeneration proceeding through energetically lowest CT states. The average distance between electron–hole pairs at the donor–acceptor interface is determined from empirical fits to the TDCF data. The delocalization of CT states is larger in PTB7-Th:ITIC, the system with larger molecular quadrupole moment, indicating the sizeable effect of the electrostatic potential at the donor–acceptor interface on the dissociation of CT complexes.     

Original Fuel‐Cell Membranes from Crosslinked Terpolymers via a “Sol–gel” Strategy

Hybrid organic/inorganic membranes that include a functionalized (‐SO₃H), interconnected silica network, a non‐porogenic organic matrix, and a ‐SO₃H‐functionalized terpolymer are synthesized through a sol–gel‐based strategy. The use of a novel crosslinkable poly(vinylidene fluoride‐ter‐perfluoro(4‐methyl‐3,6‐dioxaoct‐7‐ene sulfonyl fluoride)‐ter‐vinyltriethoxysilane) (poly(VDF‐ter‐PFSVE‐ter‐VTEOS)) terpolymer allows a multiple tuning of the different interfaces to produce original hybrid membranes with improved properties. The synthesized terpolymer and the composite membranes are characterized, and the proton conductivity of a hybrid membrane in the absence of the terpolymer is promising, since 8 mS cm^?1 is reached at room temperature, immersed in water, with an experimental ion‐exchange‐capacity (IEC_exp) value of 0.4 meq g^?1. Furthermore, when the composite membranes contain the interfaced terpolymer, they exhibit both a higher proton conductivity (43 mS cm^?1 at 65 °C under 100% relative humidity) and better stability than the standard hybrid membrane, arising from the occurrence of a better interface between the inorganic silica and the poly[(vinylidene fluoride)‐co‐hexafluoropropylene] (poly(VDF‐co‐HFP)) copolymer network. Accordingly, the hybrid SiO₂‐SO₃H/terpolymer/poly(VDF‐co‐HFP) copolymer membrane has potential use as an electrolyte in a polymer‐electrolyte‐membrane fuel cell operating at intermediate temperatures.     

High‐Surface‐Area Nanoporous Boron Carbon Nitrides for Hydrogen Storage

Nano‐ and mesoporous boron carbon nitrides with very high surface areas up to 1560 m² g^?1 are obtained by pyrolysis of a graphitic carbon nitride mpg‐C₃N₄ infiltrated with a borane complex. This reactive hard‐templating approach provides easy composition and texture tuning by temperature adjustment between 800 and 1400 °C. The process yields B_xC_yN_zO_vH_w materials as direct copies of the initial template with controlled compositions of 0.15 ≤ x ≤ 0.36, 0.10 ≤ y ≤ 0.12, 0.14 ≤ z ≤ 0.32, and 0.11 ≤ v ≤ 0.28. The nano and mesoporosities can also be tuned in order to provide hierarchical materials with specific surface areas ranging from 610 to 1560 m² g^?1. Such high values, coupled with resistance against air oxidation up to 700 °C, suggest potential materials for gas storage and as catalyst supports. Indeed, it is demonstrated that these compounds exhibit high and tunable H₂ uptakes from 0.55 to 1.07 wt.% at 77 K and 1 bar, thus guiding further search of materials for hydrogen storage.     

Characterizing ADSL customer behaviours by network traffic data-mining

The very rapid adoption of new applications by some segments of the adsl customers may have a strong impact on the quality of service delivered to all customers. This makes the segmentation of adsl customers according to their network usage a critical step both for a better understanding of the market and for the prediction and dimensioning of the network. Relying on a “bandwidth only” perspective to characterize network customer behaviour does not allow the discovery of usage patterns in terms of applications. In this paper, we shall describe how data mining techniques applied to network measurement data can help to extract qualitative and quantitative knowledge.