American municipalities. Accounting, paving, street cleaning, sewers and sewage, municipal law

No numbers were issued from May to Sept. 1911

Cleaner wrasse prefer client mucus: support for partner control mechanisms in cleaning interactions

Recent studies on cleaning behaviour suggest that there are conflicts between cleaners and their clients over what cleaners eat. The diet of cleaners usually contains ectoparasites and some client tissue. It is unclear, however, whether cleaners prefer client tissue over ectoparasites or whether they include client tissue in their diet only when searching for parasites alone is not profitable. To distinguish between these two hypotheses, we trained cleaner fish Labroides dimidiatus to feed from plates and offered them client mucus from the parrotfish Chlorurus sordidus, parasitic monogenean flat-worms, parasitic gnathiid isopods and boiled flour glue as a control. We found that cleaners ate more mucus and monogeneans than gnathiids, with gnathiids eaten slightly more often than the control substance. Because gnathiids are the most abundant ectoparasites, our results suggest a potential for conflict between cleaners and clients over what the cleaner should eat, and support studies emphasizing the importance of partner control in keeping cleaning interactions mutualistic.

Feeding ecology of the fish ectoparasite Gnathia sp. (Crustacea: Isopoda) from the Great Barrier Reef, and its implications for fish cleaning behaviour

The feeding rate of a parasitic gnathiid isopod on fish was examined. Individual fish, Hemigymnus melapterus, were exposed to gnathiid larvae and sampled after 5, 10, 30, 60, and 240 min. I recorded whether larvae had an engorged gut, an engorged gut containing red material, or had dropped off the fish after having completed engorgement; variation among sampling times and larval stages was analyzed using generalized linear mixed model analyses. The likelihood that larvae had an engorged gut increased with time and varied with larval stage. First stage (1.45 mm) larvae. After 30 min, however, most (>93%) larvae had an engorged gut regardless of their larval stage. The likelihood of red material in the gut of third stage larvae increased over time (46% after 30 min, 70% after 60 min, and 86% after 240 min) while that of first and second stage larvae remained relatively low (

Cleaner shrimp use a to advertise cleaning rocking dance service to clients

Signals transmit information to receivers about sender attributes, increase the fitness of both parties, and are selected for in cooperative interactions between species to reduce conflict [1, 2]. Marine cleaning interactions are known for stereotyped behaviors [3-6] that likely serve as signals. For example, dancing and tactile dancing in cleaner fish may serve to advertise cleaning services to client fish [7] and manipulate client behavior [8], respectively. Cleaner shrimp clean fish [9], yet are cryptic in comparison to cleaner fish. Signals, therefore, are likely essential for cleaner shrimp to attract clients. Here, we show that the yellow-beaked cleaner shrimp [110] Urocaridella sp. c [11] uses a stereotypical side-to-side movement, or rocking dance, while approaching potential client fish in the water column. This dance was followed by a cleaning interaction with the client 100% of the time. Hungry cleaner shrimp, which are more willing to clean than satiated ones [12], spent more time rocking and in closer proximity to clients Cephaiopholis cyanostigma than satiated ones, and when given a choice, clients preferred hungry, rocking shrimp. The rocking dance therefore influenced client behavior and, thus, appears to function as a signal to advertise the presence of cleaner shrimp to potential clients.

Client fish ectoparasite loads and cleaner shrimp Urocaridella sp c hunger levels affect cleaning behaviour

Cleaning is a classic example of mutualism and determining the factors that maintain the balance between the costs and benefits for mutualist partners can assist our understanding of how cleaning relationships are maintained. Optimal foraging theory suggests two factors that might help to maintain the relationship between cleaners and their clients: client ectoparasite load and cleaner hunger levels. The ecological relevance and importance of foraging by cleaner fish in marine systems has been demonstrated repeatedly, yet there is little information available on this behaviour in cleaner shrimp. To determine whether cleaner shrimp base their choice of client fish on food patch quality (i.e. client fish ectoparasite load) we offered the yellow-beaked cleaner shrimp Urocaridella sp. c a choice of parasitized and unparasitized rock cods, Cephalopholis cyanostigma. To determine whether cleaner shrimp hunger levels influence cleaning time, we manipulated hunger levels in Urocaridella sp. c and examined their behaviour towards parasitized client fish. Cleaner shrimp preferred parasitized to unparasitized client fish and food-deprived cleaner shrimp cleaned parasitized rock cods more frequently than satiated cleaner shrimp did. Therefore, variations in client fish ectoparasite load and cleaner shrimp hunger level are two factors that affect the balance in this mutualism. Finally, our results meet some of the assumptions of biological market theory, a framework used to understand cooperative interactions, and thus this framework is suggested for future studies on this cleaning system.

Erosion-corrosion of mild steel in hot caustic. Part II: The effect of acid cleaning

Corrosion rates of 1020 steel in 2.75 M NaOH solution at a temperature of 160 degrees C and velocities of 0.32 and 2.5 m/s were studied. The focus was on the effect of the acid cleaning which was performed by using strong, inhibited sulphuric acid in between the exposures to caustic. In situ electrochemical methods were used to measure the corrosion rate such as the potentiodynamic sweep and the polarization resistance method. Also used were the weight-loss method and scanning electron microscopy (SEM). Eight electrodes/coupons were used to monitor the metal loss rate, four were placed at the low velocity section, while the other four were placed in the high velocity section of a high temperature flow. The first three coupons in each section were placed within the disturbed flow region, while the fourth was placed in a fully developed flow region. During the exposure of mild steel to the inhibited acid, following the first caustic period, the corrosion rate increased significantly to between 3 and 10mm/y with a few electrodes experiencing as high as 50 mm/y. The second caustic period following the acidic period typically started with very high corrosion rates (20-80 mm/y). The length of this corrosion period was typically 2-3 h with a few exceptions when the high corrosion period lasted 7-10 h. Following the very high corrosion rates experienced at the beginning of the second caustic period, the corrosion rates were reduced sharply (as the corrosion potential increased) to nearly the same levels as those observed during the passive part of the first caustic period. (c) 2005 Elsevier Ltd. All rights reserved.

HTHP syngas cleaning concept of two stage biomass gasification for FT synthesis

The EU intends to increase the fraction of fuels from biogenic energy sources from 2% in 2005 to 8% in 2020. This means a minimum of 30 million TOE/a of fuels from biomass. This makes technical-scale generation of syngas from high-grade biomass, e.g. straw, hay, bark, or paper/cardboard waste, and the production of synthetic fuels by Fischer-Tropsch (FT) synthesis highly attractive. The BTL concept (Biomass to Liquids) of the Karlsruhe Research Center, labeled bioliq, focuses on this challenge by locally concentrating the biomass energy content by fast pyrolysis in a coke/oil slurry followed by slurry conversion to syngas in a central entrained flow gasifier at 1200C and pressures above 4MPa. FT synthesis generates intermediate products for synthetic fuels. To prevent the sensitive catalysts from being poisoned the syngas must be free of tar and particulates. Trace concentrations of H2S, COS, CS2, HCl, NH3, and HCN must be on the order of a few ppb. Moreover, maximum conversion efficiency will be achieved by cleaning the gas above the synthesis conditions. (T>350C, P>4MPa). The concept of an innovative dry HTHP syngas cleaning process is presented. Based on HT particle filtration and suitable sorption and catalysis processes for the relevant contaminants, an overall concept will be derived, which leads to a syngas quality required for FT synthesis in only two combined stages. Results of filtration experiments on a pilot scale are presented. The influence of temperature on the separation and conversion, respectively, of particulates and gaseous contaminants is discussed on the basis of experimental results obtained on a laboratory and pilot scale. Extensive studies of this concept are performed in a scientific network comprising the Karlsruhe Research Center and five universities; funding is provided by the Helmholtz Association of National Research Centers in Germany.

High temperature gas cleaning for advanced syngas utilization:Paper # 95

Biofuels and chemicals from biomass mean the gasification of biogenic feedstocks and the synthesis via methanol, dimethylester (DME) or Fischer-Tropsch products. To prevent the sensitive synthesis catalysts from poisoning the syngas must be free of tar and particulates. The trace concentrations of S-, C1-, N-species, alkali and heavy metals must be of the order of a few ppb. Moreover maximum conversion efficiency will be achieved performing the gas cleaning above the synthesis conditions. The concept of an innovative dry HTHP syngas cleaning is presented. Based on the HT particle filtration and suitable sorption and catalysis processes for the relevant contaminants a total concept will be derived, which leads to a syngas quality required for synthesis catalysts in only 2 combined stages. The experimental setup for the HT gas cleaning behind the 60 kWtherm entrained flow gasifier REGA of the institute is described. Results from HT filter experiments in pilot scale are presented. The performance of 2 natural minerals for HC1 and H2S sorption is discussed with respect to the parameters temperature, surface and residence time. Results from lab scale investigations on low temperature tar catalysts' performance (commercial and proprietary development) are discussed finally.

Miles v Insitu Cleaning 2012

Case law report - online

The effect of self-focusing on laser space-debris cleaning

A ground-based laser system for space-debris cleaning will use powerful laser pulses that can self-focus while propagating through the atmosphere. We demonstrate that for the relevant laser parameters, this self-focusing can noticeably decrease the laser intensity on the target. We show that the detrimental effect can be, to a great extent, compensated for by applying the optimal initial beam defocusing. The effect of laser elevation on the system performance is discussed.

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Novel online data cleaning protocols for data streams in trajectory, Wireless Sensor Networks

The promise of Wireless Sensor Networks (WSNs) is the autonomous collaboration of a collection of sensors to accomplish some specific goals which a single sensor cannot offer. Basically, sensor networking serves a range of applications by providing the raw data as fundamentals for further analyses and actions. The imprecision of the collected data could tremendously mislead the decision-making process of sensor-based applications, resulting in an ineffectiveness or failure of the application objectives. Due to inherent WSN characteristics normally spoiling the raw sensor readings, many research efforts attempt to improve the accuracy of the corrupted or "dirty" sensor data. The dirty data need to be cleaned or corrected. However, the developed data cleaning solutions restrict themselves to the scope of static WSNs where deployed sensors would rarely move during the operation. Nowadays, many emerging applications relying on WSNs need the sensor mobility to enhance the application efficiency and usage flexibility. The location of deployed sensors needs to be dynamic. Also, each sensor would independently function and contribute its resources. Sensors equipped with vehicles for monitoring the traffic condition could be depicted as one of the prospective examples. The sensor mobility causes a transient in network topology and correlation among sensor streams. Based on static relationships among sensors, the existing methods for cleaning sensor data in static WSNs are invalid in such mobile scenarios. Therefore, a solution of data cleaning that considers the sensor movements is actively needed. This dissertation aims to improve the quality of sensor data by considering the consequences of various trajectory relationships of autonomous mobile sensors in the system. First of all, we address the dynamic network topology due to sensor mobility. The concept of virtual sensor is presented and used for spatio-temporal selection of neighboring sensors to help in cleaning sensor data streams. This method is one of the first methods to clean data in mobile sensor environments. We also study the mobility pattern of moving sensors relative to boundaries of sub-areas of interest. We developed a belief-based analysis to determine the reliable sets of neighboring sensors to improve the cleaning performance, especially when node density is relatively low. Finally, we design a novel sketch-based technique to clean data from internal sensors where spatio-temporal relationships among sensors cannot lead to the data correlations among sensor streams.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.