Polyhydroxyalkanoate as a slow-release carbon source for in situ bioremediation of contaminated aquifers: from laboratory investigation to pilot-scale testing in the field

A pilot-scale study aiming to evaluate the potential use of poly-3-hydroxy-butyrate (PHB) as an electron donor source for in situ bioremediation of chlorinated hydrocarbons in groundwater was conducted. Compared with commercially available electron donors, PHB offers a restricted fermentation pathway (i.e., through acetic acid and molecular hydrogen) by avoiding the formation of any residual carbon that could potentially spoil groundwater quality. The pilot study was carried out at an industrial site in Italy, heavily contaminated by different chlorinated aliphatic hydrocarbons (CAHs). Prior to field testing, PHB was experimentally verified as a suitable electron donor for biological reductive dechlorination processes at the investigated site by microcosm studies carried out on site aquifer material and measuring the quantitative transformation of detected CAHs to ethene. Owing to the complex geological characteristics of the aquifer, the use of a groundwater circulation well (GCW) was identified as a potential strategy to enable effective delivery and distribution of electron donors in less permeable layers and to mobilise contaminants. A 3-screened, 30-m-deep GCW coupled with an external treatment unit was installed at the site. The effect of PHB fermentation products on the in situ reductive dechlorination processes were evaluated by quantitative real-time polymerase chain reaction (qPCR). The results from the first 4 months of operation clearly demonstrated that the PHB fermentation products were effectively delivered to the aquifer and positively influenced the biological dechlorination activity. Indeed, an increased abundance of Dehalococcoides mccartyi (up to 6.6 fold) and reduced CAH concentrations at the installed monitoring wells were observed

IMECE2002-33205 CONSIDERATIONS FOR MINIMUM LAP TIME CALCULATION ON A RACE TRACK

ABSTRACT While methods 1'or vehicle modeling are well established for simulation of handling behavior, there is still a lack of driver models, which are important R)r the realization of closed-loop maneuvers in a virtual environment. This paper will present preliminary considerations for the development of such a driver model. First, trajectory planning strategies have to be generated and evaluated. To achieve this, a method will be deduced, which calculates the maximum velocity at each point of an arbitrary trajectory, taking into account simplified vehicle characteristics in terms of maximum longitudinal and lateral accelerations and considering the frictional ellipse. Thus, the minimum necessm-y time for each trajectory can be calculated, this being a possible parameter to rate the quality of a trajectory for a given course. The feasibility of the method is demonstrated with the Nuerburgring race track. (x.,y.) Radius of a curve. Time. Speed. NOMENCLATURE • Curvature Longitudinal acceleration Lateral acceleration Traveled distance Number of sampling points Coordinates of left road boarder at i-th ,;ampling point (~r, ri ~ Y ri ) Wi Si L(si,Si+l) Coordinates of right road boarder at i-th sampling point Coordinates of middle of the at i-th sampling point Width of road at i-th sampling point Sampling point number i Distance between sampling points number i and number i + 1 INTRODUCTION Since the invention of the automobile in the 18th century, there has been a continuous urge tot its improvement. First, open-loop test maneuvers like step-steer, ramp-steer etc. have been defined in order to rate and compare the dynamic behavior of different vehicle configurations objectively. Next, closed-loop maneuvers like double.-lane change were defined, which included the driver behavior in testing, thus leading to subjective evaluation of the vehicle behavior. Soon it became obvious that it is not sufficient to rely on hardware testing. Therefore, mathematical descriptions (i.e. vehicle models) of this dynamic system have been developed, which were able to predict the handling behavior depending on different vehicle setups. These vehicle models became more and more successful after enough computing power became available to perform complex calculations. Nowadays, vehicle development relics increasingly on computer simulation. Whereas open-loop testing in the virtual envi- ronment is well established, there is still a demand R)r improving closed-loop simulation, which necessarily requires driver modeling. These controllers, also named 'driver models', were supposed to reproduce the driving behavior of human drivers in a virtual world. The first approach was presented by Over the yeal's, more and more sophisticated models were created, also using modern modelling techniques, like neural networks, Fuzzy Models, etc. They emerged due to the large variety of al'eas they were applied to. For a complete survey of driver models please refer to Juergensohn The operation mode of all these driver models is basically the same: they manipulate the control inputs of the vehicle, forcing it to follow a predetermined path. This path is mostly defined a-priori and no interaction with the handling characteristics of the vehicle is considered. Consequently that path will not be optimal. A new approach to a driver model which overcomes this drawback was recently presented by Pmkop (Prokop, 2001), who implemented a short term path planning strategy in addition to the conventional PID controller. He also takes into account the actual position of the cm: on the road and the vehicle characteristics. Still, this short term path planning unit does not consider the whole geometry of the track. A further improvement to driver modelling can be achieved generating an optimal path for the entire track. Themlbrc, it is essential to consider the whole track geometry and the vehicle's handling capabilities. But, a method for the evaluation of the optimal path has not been established yet. This paper is going to present an approach to evaluate the quality of a given path. The goal is to calculate the minimum necessary time a cal" needs through a given path, them[bre using the lap time as a rating function. ..2i The length of each trajectory from figure I can be defined as Thus, two different travel times for the inner and the outer radius rj and r2 m'e obtained: METHODOLOGY A method to calculate the minimum time through a given path is now going to be described, Some preliminary considerations have to be made in advance. Travel Time Through a Curve If the path through a track is a straight line, then the calculation of travel time is trivial. If we are considering a curve, then the radius and the maximal lateral acceleration (atat_,nax) influence the result. An example is shown in tigure 1, where two different trajectories through a semi-circle are depicted. Using equations 1 and 4. we get: ttand t2--- Curvature U sing the previously introduced discretization, the curvature of the trajectory (middle of the road) is calculated at each sampling point, as shown in figure 3: As we can see from the last equation it is advantageous to choose a tighter path through a semi-circle. This simple result will be extended in the following sections to curves with variable radii 1 Discretization of the Track For a numerical treatment a discrctization of the U'ack is ne- Instability Zones In The limit of adhesion at,~o,,,x will be chosen here to be 10 "~. The corresponding velocity profile is depicted in Acceleration and Deceleration A car in motion can be subjected to acceleration or deceleration. Its velocity history can be calculated using the following equation: v(s) = ~ 2"along" S This dependency can be expressed mathematically with the frictional ellipse as described in the following section. The dependency between longitudinal and lateral acceleration is determined by the frictional ellipse from figure 7, yielding: Adhesion The relationship between lateral and longitudinal forces play an important role, Therefore the fi%tional ellipse has to be considereal, which is related to the adhesion between the tire and the ground, The calculation has to be recursive, since at each sampling point a new deceleration graph has to be calculated, as shown in figure 8. where n is the total number of sampling points over the test track. Examples This methodology is going to be demonstrated in the following example, which is depicted in figure 8. It shows a cutout of An accelerating car (small crosses) approaches the curve from the left. From each position a deceleration graph is calculated, checking if the graph violates the instability zones. As we can see in the figure, the car has to start braking at position s = 94 m in order not to exceed the maximum speed at the beginning of the curve. We can also observe (in the deceleration diagram) an important reduction of deceleration of the car at position s = 121 m. This is due to the fact that the car would be now traveling neat" the limit of lateral adhesion and wouldn't be able to brake as strongly as on the straight line (this results from equation 14). From this position on (s = 94 m) the velocity of the car (following crosses) will be on the last possible velocity diagram and then continue under the hatched area

Evaluating Machine-Learning Techniques for Detecting Smart Ponzi Schemes

International audienceEthereum is one of the most popular platforms for exchanging cryptocurrencies as well as the most established for peer to peer programming and smart contracts publishing [3]. The versatility of the Solidity language allows developers to program general-purpose smart contracts. Among the various smart contracts, there may be some fraudulent ones, whose purpose is to steal Ether from the network participants. A notorious example of such cases are Ponzi schemes, i.e. a financial frauds that require investors to be repaid through the investments of others who have just entered the scheme. Within the Ethereum blockchain, several contracts have been identified as being Ponzi schemes. The paper proposes a machine learning model that uses textual classification techniques to recognize contracts emulating the behavior of a Ponzi scheme. Starting from a contracts dataset containing exclusively Ponzi schemes uploaded between 2016 and 2018, we built models able to properly classify Ponzi schemes contracts. We tested several models, some of which returned an overall accuracy of 99% on classification. The best model turned out to be the linear Support Vector Machine and the Multinomial Naive Bayes model, which provides the best results in terms of metrics evaluation

Unexpected discovery of surgical gauze during a robotic radical prostatectomy identified as a capturing lymph node on magnetic resonance

Multiparametric magnetic resonance, plays a crucial role in several steps of the management of prostate cancer. Various factors could alter the interpretation and reduce the accuracy of MR. Among these the group of the retained surgical items, can produce serious implications for the health of patient, as well as medical-legal consequences. Here we report the case of a patient, with a prostate tumor, who performed a mp-MRI of the prostate, where it was reported as collateral finding, compatible thesis with lymphadenopathy. During robotic assisted radical prostatectomy, was found a gauze, which persisted asymptomatic, retained after a previous right inguinal hernioplast

Microbial Community Changes in a Chlorinated Solvents Polluted Aquifer Over the Field Scale Treatment With Poly-3-Hydroxybutyrate as Amendment

This study investigated the organohalide-respiring bacteria (OHRB) and the supporting microbial populations operating in a pilot scale plant employing poly-3-hydroxybutyrate (PHB), a biodegradable polymer produced by bacteria from waste streams, for the in situ bioremediation of groundwater contaminated by chlorinated solvents. The bioremediation was performed in ground treatment units, including PHB reactors as slow release source of electron donors, where groundwater extracted from the wells flows through before the re-infiltration to the low permeability zones of the aquifer. The coupling of the biological treatment with groundwater recirculation allowed to drastically reducing the contamination level and the remediation time by efficiently stimulating the growth of autochthonous OHRB and enhancing the mobilization of the pollutants. Quantitative PCR performed along the external treatment unit showed that the PHB reactor may efficiently act as an external incubator to growing Dehalococcoides mccartyi, known to be capable of fully converting chlorinated ethenes to innocuous end-products. The slow release source of electron donors for the bioremediation process allowed the establishment of a stable population of D. mccartyi, mainly carrying bvcA and vcrA genes which are implicated in the metabolic conversion of vinyl chloride to harmless ethene. Next generation sequencing was performed to analyze the phylogenetic diversity of the groundwater microbiome before and after the bioremediation treatment and allowed the identification of the microorganisms working closely with organohalide-respiring bacteria

Detecting QTLs and putative candidate genes involved in budbreak and flowering time in an apple multiparental population

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitièresIn temperate trees, growth resumption in spring time results from chilling and heat requirements, and is an adaptive trait under global warming. Here, the genetic determinism of budbreak and flowering time was deciphered using five related full-sib apple families. Both traits were observed over 3 years and two sites and expressed in calendar and degree-days. Best linear unbiased predictors of genotypic effect or interaction with climatic year were extracted from mixed linear models and used for quantitative trait locus (QTL) mapping, performed with an integrated genetic map containing 6849 single nucleotide polymorphisms (SNPs), grouped into haplotypes, and with a Bayesian pedigree-based analysis. Four major regions, on linkage group (LG) 7, LG10, LG12, and LG9, the latter being the most stable across families, sites, and years, explained 5.6–21.3% of trait variance. Co-localizations for traits in calendar days or growing degree hours (GDH) suggested common genetic determinism for chilling and heating requirements. Homologs of two major flowering genes, AGL24 and FT, were predicted close to LG9 and LG12 QTLs, respectively, whereas Dormancy Associated MADs-box (DAM) genes were near additional QTLs on LG8 and LG15. This suggests that chilling perception mechanisms could be common among perennial and annual plants. Progenitors with favorable alleles depending on trait and LG were identified and could benefit new breeding strategies for apple adaptation to temperature increase