Sustainable desalination : a case of renewable energy

Paper presented at the 6th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, South Africa, 30 June - 2 July, 2008.A new energy-efficient and sustainable desalination system has been developed in this research. This system operates under near-vacuum conditions created by exploiting natural means of gravity and barometric pressure head. The system can be driven by low grade heat sources such as solar energy or waste heat streams. Theoretical and experimental studies were conducted to evaluate and demonstrate the feasibility of the proposed process. Theoretical studies included thermodynamic analysis and process modelling to evaluate the performance of the process driven by the following alternate energy sources: solar thermal energy, solar photovoltaic/thermal energy, geothermal energy, and process waste heat emissions. Experimental studies included prototype scale demonstration of the process using direct solar and a combination of solar photovoltaic/thermal sources. In the tests using direct solar energy, freshwater production of 5 L/d was achieved using direct solar energy alone, at efficiencies ranging from 65 to 75%. In the tests using solar photovoltaic/thermal energy, freshwater production of 10 L/d was achieved, at efficiencies ranging from 65 to 90%. Specific energy required for this process to produce 1 kg of freshwater was 2926 kJ, all of which was derived from solar energy.vk201

Application of Structured Decision Making to Wildlife Management in Montana

Good decision-making is essential to conserving wildlife populations. Whereas there may be multiple ways to address a problem, perfect solutions rarely exist. Managers are therefore tasked with identifying optimal decisions that will best achieve desired outcomes. Structured decision making (SDM) is a method of decision analysis used to identify the most effective, efficient, and realistic optimal decisions while accounting for values and priorities of the decision maker. The stepwise process includes identifying the management problem, defining objectives for solving the problem, developing alternative approaches to achieve the objectives, and formally evaluating which alternative is most likely to accomplish the objectives. The SDM process can be more effective than informal decision-making because it provides a transparent way to quantitatively evaluate decisions for addressing multiple management objectives while incorporating science, uncertainty, and risk tolerance. We illustrate the application of this process to management needs, including an SDM-based decision tool developed to identify optimal decisions for proactively managing risk of pneumonia epizootics in bighorn sheep (Ovis canadensis). Pneumonia epizootics are a major challenge for managers, including in terms of knowing how or when to manage risk. The decision tool facilitates analysis of alternative decisions for how to manage herds based on predictions from a risk model, herd-specific objectives, and predicted costs and benefits of each alternative. Managers can be confident resulting decisions are most effective, efficient, and realistic because they explicitly account for important considerations managers implicitly weigh when making decisions, including competing management objectives, uncertainty in potential outcomes and risk tolerance

Using Hunter Survey Data To Estimate Wolf Population Sizes In Montana, 2007-2009

Reliable knowledge of the status and trend of carnivore populations is critical to their conservation. In the Northern Rocky Mountains, wildlife managers need a time- and costefficient method for monitoring the large, growing population of gray wolves (Canis lupus) at a state-wide scale. We explored how hunter survey data could be incorporated into a multiyear patch occupancy model framework to estimate the abundance and distribution of wolf packs, wolves, and breeding pairs in Montana for 2007- 2009. We used hunter observations of wolves to estimate the probability that a given landscape patch was occupied by a wolf pack, and used additional data/models in combination with occupancy model output to provide estimates of total number of wolves and number of breeding pairs. Our modeling framework also allowed us to examine how geographic and ecological factors influenced occupancy and detection of wolf packs. Our models provided estimates of number of packs, number of wolves, and number of breeding pairs that were within 20 percent of Montana Fish, Wildlife, and Parks minimum counts for 2007-2009. We found occupancy was positively related to forest cover, rural roads, and elevation and detection probability was positively related to hunter effort and forest cover. We believe that patch occupancy models based on hunter surveys offer promise as a method for accurately monitoring elusive carnivores at state-wide scales in a time- and cost-efficient manner

Proactive Management of Pneumonia Epizootics in Bighorn Sheep in Montana—Project Update

Pneumonia epizootics are a major challenge for effective management of bighorn sheep (Ovis canadensis). Approximately half of the herds in Montana have suffered die-offs since the 1980s, many of which were pneumonia events. A set of models that identify risk of pneumonia and the best management decisions given that risk would be of great value for proactive management of pneumonia epizootics. Our first objective is to design and test a risk model that will help predict a herd’s risk of pneumonia. We hypothesize that various factors increase risk through pathogen exposure, pathogen spread, and disease susceptibility. Analysis of these factors comparing herds with and without recent pneumonia histories using Bayesian logistic regression will allow us to design a risk model. Our second objective is to develop a proactive decision model that incorporates estimates of pneumonia risk to help evaluate costs and benefits of alternative proactive actions appropriate to those estimates. We will use a Structured Decision Making framework, which provides a deliberative, transparent, and defensible decision-making process that is particularly valuable in complex decision-making environments such as wildlife disease management. Together the resulting risk and decision models, to be completed this year, will help managers estimate pneumonia risk and identify the best management action based on both the severity of each herd’s predicted risk and costs and benefits of competing management alternatives. Ultimately, this project will demonstrate the development and application of risk and decision models for proactive wildlife health programs in Montana Fish, Wildlife and Parks

Combining Hunter Surveys and Territorial Dynamics to Monitor Wolf Pack Abundance and Distribution in Montana

Carnivores are difficult to monitor on large spatial scales. We developed a patch occupancy model (POM) using hunter surveys to monitor gray wolves (Canis lupus) in Montana, and evaluated the ability of these models to provide wildlife managers with a time-and cost-efficient monitoring technique. We used hunter’s sightings of wolves as our index of occupancy and explored how classifying a patch as occupied based on different minimum number of wolves sighted (1,2,3,4, or 5) or different minimum number of hunters sighting wolves (1,2,3,4,or 5) affected results. We also evaluated how our definition of a “patch” influenced the occupancy estimates by creating POMs with 3 different patch sizes that corresponded to the variation in wolf territory sizes in Montana. We ran multiple models with different patch sizes predicting occupancy classified according to different levels of minimum wolf sightings and minimum hunters seeing wolves. We assessed model accuracy by comparing POM estimates to the Montana Fish, Wildlife, and Parks (FWP) minimum wolf pack count. Our preliminary results showed that patch size did not strongly influence occupancy estimates and that a patch should only be identified as occupied if ? 2 to ? 4 hunters each observed ? 2 to ? 4 wolves in that patch. Within this range, FWP’s minimum wolf pack count fell within the 95-percent confidence interval of POM estimates for 33 percent of the models

Unraveling the cytotoxic potential of Temozolomide loaded into PLGA nanoparticles

BACKGROUND: Nanotechnology has received great attention since a decade for the treatment of different varieties of cancer. However, there is a limited data available on the cytotoxic potential of Temozolomide (TMZ) formulations. In the current research work, an attempt has been made to understand the anti-metastatic effect of the drug after loading into PLGA nanoparticles against C6 glioma cells. Nanoparticles were prepared using solvent diffusion method and were characterized for size and morphology. Diffusion of the drug from the nanoparticles was studied by dialysis method. The designed nanoparticles were also assessed for cellular uptake using confocal microscopy and flow cytometry. RESULTS: PLGA nanoparticles caused a sustained release of the drug and showed a higher cellular uptake. The drug formulations also affected the cellular proliferation and motility. CONCLUSION: PLGA coated nanoparticles prolong the activity of the loaded drug while retaining the anti-metastatic activity

Clinical Performance Feedback Intervention Theory (CP-FIT): a new theory for designing, implementing, and evaluating feedback in health care based on a systematic review and meta-synthesis of qualitative research

Background: Providing health professionals with quantitative summaries of their clinical performance when treating specific groups of patients (“feedback”) is a widely used quality improvement strategy, yet systematic reviews show it has varying success. Theory could help explain what factors influence feedback success, and guide approaches to enhance effectiveness. However, existing theories lack comprehensiveness and specificity to health care. To address this problem, we conducted the first systematic review and synthesis of qualitative evaluations of feedback interventions, using findings to develop a comprehensive new health care-specific feedback theory. Methods: We searched MEDLINE, EMBASE, CINAHL, Web of Science, and Google Scholar from inception until 2016 inclusive. Data were synthesised by coding individual papers, building on pre-existing theories to formulate hypotheses, iteratively testing and improving hypotheses, assessing confidence in hypotheses using the GRADE-CERQual method, and summarising high-confidence hypotheses into a set of propositions. Results: We synthesised 65 papers evaluating 73 feedback interventions from countries spanning five continents. From our synthesis we developed Clinical Performance Feedback Intervention Theory (CP-FIT), which builds on 30 pre-existing theories and has 42 high-confidence hypotheses. CP-FIT states that effective feedback works in a cycle of sequential processes; it becomes less effective if any individual process fails, thus halting progress round the cycle. Feedback’s success is influenced by several factors operating via a set of common explanatory mechanisms: the feedback method used, health professional receiving feedback, and context in which feedback takes place. CP-FIT summarises these effects in three propositions: (1) health care professionals and organisations have a finite capacity to engage with feedback, (2) these parties have strong beliefs regarding how patient care should be provided that influence their interactions with feedback, and (3) feedback that directly supports clinical behaviours is most effective. Conclusions: This is the first qualitative meta-synthesis of feedback interventions, and the first comprehensive theory of feedback designed specifically for health care. Our findings contribute new knowledge about how feedback works and factors that influence its effectiveness. Internationally, practitioners, researchers, and policy-makers can use CP-FIT to design, implement, and evaluate feedback. Doing so could improve care for large numbers of patients, reduce opportunity costs, and improve returns on financial investments

Dual-purpose power-desalination plant augmented by thermal energy storage system

Paper presented to the 10th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Florida, 14-16 July 2014.This paper presents a novel application of a sensible Thermal Energy Storage (TES) system for simultaneous energy conservation and water desalination in power plants. First, the TES mitigates negative effects of high ambient temperatures on the performance of air cooled condenser (ACC) that cools a 500 MW combined cycle power plant (CCPP); next, the same TES satisfies the cooling requirements in a 0.25 mgd multi-effect distillation (MED) plant. Stack gases from CCPP are used to drive an absorption refrigeration system (ARS) which maintains the chilled water temperature in a TES tank. A process model integrating CCPP, ARS, TES, and MED has been developed to optimize the volume of the TES. Preliminary analysis showed that a tank volume of 2950 m3 was adequate in meeting the cooling requirements of both ACC and MED in both hot and cold seasons. The proposed TES has the potential to save 2.5% of the power loss in a CCPP/ACC on a hot summer day. Further, our modeling results reveal that a desalination capacity of 0.25-0.43 mgd can be achieved with top brine temperatures between 100 ºC and 70 ºC of MED. The proposed integrated system, process modeling and simultaneous advantages of enhanced CCPP performance and sustainable desalination system will be discussed in the presentation.dc201

Energiebesparing door verminderde circulatie : aan/uit- versus frequentieregeling

De standaardnorm voor de luchtcirculatie bij de bewaring van tulpenbollen is 500 m3 lucht/uur/m 3 bollen. Eerder onderzoek wees uit dat deze hoeveelheid flink verminderd kan worden. In een demo-proefopstelling is dit verminderen d.m.v. een frequentieregeling vergeleken met een aan/uit regeling. Doel van deze opstelling was te demonstreren dat hiermee relatief veel energie bespaard kan worden zonder nadelige effecten op de bollen. Hiertoe zijn gedurende de bewaarperiode het ethyleen- en CO2 -gehalte, de relatieve luchtvochtigheid (RV) en de temperatuur tussen de bollen continue gemeten en digitaal opgeslagen. Daarnaast zijn bij verschillende kistenstapelingen en bij verschillende frequenties de totale luchthoeveelheid per stapeling en de luchtstroom per kist gemeten. De energie meterstanden zijn 3 maal per week bijgehouden

A New Determination of the High Redshift Type Ia Supernova Rates with the Hubble Space Telescope Advanced Camera for Surveys

We present a new measurement of the volumetric rate of Type Ia supernova up to a redshift of 1.7, using the Hubble Space Telescope (HST) GOODS data combined with an additional HST dataset covering the North GOODS field collected in 2004. We employ a novel technique that does not require spectroscopic data for identifying Type Ia supernovae (although spectroscopic measurements of redshifts are used for over half the sample); instead we employ a Bayesian approach using only photometric data to calculate the probability that an object is a Type Ia supernova. This Bayesian technique can easily be modified to incorporate improved priors on supernova properties, and it is well-suited for future high-statistics supernovae searches in which spectroscopic follow up of all candidates will be impractical. Here, the method is validated on both ground- and space-based supernova data having some spectroscopic follow up. We combine our volumetric rate measurements with low redshift supernova data, and fit to a number of possible models for the evolution of the Type Ia supernova rate as a function of redshift. The data do not distinguish between a flat rate at redshift > 0.5 and a previously proposed model, in which the Type Ia rate peaks at redshift >1 due to a significant delay from star-formation to the supernova explosion. Except for the highest redshifts, where the signal to noise ratio is generally too low to apply this technique, this approach yields smaller or comparable uncertainties than previous work.Comment: Accepted for publication in Ap