Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.Brazilian Scientific and Technological Development Council (CNPq)Brazilian Scientific and Technological Development Council (CNPq)Research Support Foundation of Sao Paulo (FAPESP)Research Support Foundation of Sao Paulo (FAPESP

Impact of high-pressure homogenization on the cell integrity of Tetradesmus obliquus and seed germination

ABSTRACT: Microalgae have almost unlimited applications due to their versatility and robustness to grow in different environmental conditions, their biodiversity and variety of valuable bioactive compounds. Wastewater can be used as a low-cost and readily available medium for microalgae, while the latter removes the pollutants to produce clean water. Nevertheless, since the most valuable metabolites are mainly located inside the microalga cell, their release implies rupturing the cell wall. In this study, Tetradesmus obliquus grown in 5% piggery effluent was disrupted using high-pressure homogenization (HPH). Effects of HPH pressure (100, 300, and 600 bar) and cycles (1, 2 and 3) were tested on the membrane integrity and evaluated using flow cytometry and microscopy. In addition, wheat seed germination trials were carried out using the biomass at different conditions. Increased HPH pressure or number of cycles led to more cell disruption (75% at 600 bar and 3 cycles). However, the highest increase in wheat germination and growth (40-45%) was observed at the lowest pressure (100 bar), where only 46% of the microalga cells were permeabilised, but not disrupted. Non-treated T. obliquus cultures also revealed an enhancing effect on root and shoot length (up to 40%). The filtrate of the initial culture also promoted shoot development compared to water (21%), reinforcing the full use of all the process fractions. Thus, piggery wastewater can be used to produce microalgae biomass, and mild HPH conditions can promote cell permeabilization to release sufficient amounts of bioactive compounds with the ability to enhance plant germination and growth, converting an economic and environmental concern into environmentally sustainable applications.info:eu-repo/semantics/publishedVersio

Variabilidade Sazonal e Interanual do Microclima em Área de Floresta no Sudoeste da Amazônia

The Amazon is a recognized global ecosystem, due to its high biodiversity and the magnitude of the mass and energy exchanges performed. In this study it sought to analyze the seasonal and interannual variations of microclimate in a site of tropical forest in southwestern Amazon. For this purpose, net radiation data were used, air temperature, relative humidity and wind speed collected in a micrometeorological tower belonging to the LBA, located in Jaru Biological Reserve, from 2004 to 2010. The results showed that existence of well-defined seasonal patterns was verified, with variations between wet, wet-dry, dry, dry-humid periods for all variables in question. Yet, through analysis of the interannual variations were observed warming of the increment signs and decreased moisture in the locality. This observation, although patchy, deserves special attention, since changes in the microclimate in the Amazon region may have yet unknown consequences.A Amazônia é um ecossistema de reconhecida importância global, devido à sua elevada biodiversidade e a magnitude das trocas de massa e energia por ela realizada. Considerando que as alterações no uso e ocupação do solo na região podem ocasionar mudanças nas condições micrometeorológicas locais, buscou-se com este estudo analisar os aspectos sazonais e variações interanuais do microclima em um sítio de estudos localizado em área de floresta tropical, no sudoeste da Amazônia. Para tanto, foram utilizados dados de saldo de radiação, temperatura do ar, umidade relativa do ar e velocidade do vento coletados em uma torre micrometeorológica pertencente ao Programa LBA, localizada na Reserva Biológica do Jaru, no período de 2004 a 2010. A existência de padrões sazonais bem definidos foi verificada, com variações entre os períodos úmido, úmido-seco, seco e seco-úmido, para todas as variáveis em questão. Ainda, por meio de análises das variações interanuais, foram observados indícios de incremento do aquecimento e diminuição da umidade na localidade. Essa observação, embora pontual, merece especial atenção, visto que modificações no microclima na região Amazônica podem apresentar consequências ainda desconhecidas

Combining biotechnology with circular bioeconomy: from poultry, swine, cattle, brewery, dairy and urban wastewaters to biohydrogen

ABSTRACT: The ability of microalgae to grow in nutrient-rich environments and to accumulate nutrients from wastewaters (WW) makes them attractive for the sustainable and low-cost treatment of WW. The valuable biomass produced can be further used for the generation of bioenergy, animal feed, fertilizers, and biopolymers, among others. In this study, Scenedesmus obliquus was able to remove nutrients from different wastewaters (poultry, swine and cattle breeding, brewery and dairy industries, and urban) with removal ranges of 95-100% for nitrogen, 63-99% for phosphorus and 48-70% for chemical oxygen demand. The biomass productivity using wastewaters was higher (except for poultry) than in synthetic medium (Bristol), the highest value being obtained in brewery wastewater (1025 mg/(L.day) of freeze-dried biomass). The produced biomass contained 31-53% of proteins, 12-36% of sugars and 8-23% of lipids, regardless of the type of wastewater. The potential of the produced Scenedesmus obliquus biomass for the generation of BioH(2) through batch dark fermentation processes with Enterobacter aerogenes was evaluated. The obtained yields ranged, in mL H-2/g Volatile Solids (VS), from 50.1 for biomass from anaerobically digested cattle WW to 390 for swine WW, whereas the yield with biomass cultivated in Bristol medium was 57.6 mL H-2/gvs.info:eu-repo/semantics/publishedVersio

Long-term thermal sensitivity of Earth’s tropical forests

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis