Basic energy efficiency of plasma production in electrical discharge and electron beam reactors

Non-thermal plasma processing is an emerging technology for the abatement of volatile organic compounds (VOCs) and nitrogen oxides (NO{sub x}) in atmospheric pressure gas streams. Either electrical discharge of electron beam methods can produce these plasmas. This paper presents a comparative assessment of various non-thermal plasma reactors. The goal of our project is two-fold: (1) to understand the feasibility and scalability of various non-thermal plasma reactors by focusing on the energy efficiency of the electron and chemical kinetics, and (2) to optimize process parameters and provide performance and economic data. Experimental results using a compact electron beam reactor, pulsed corona reactor and dielectric-barrier discharge will be presented. These reactors have been used to study the removal of NO{sub x} and a wide variety of VOCs. The effects of background gas decomposition and gas temperature on the decomposition chemistry have been studied. The decomposition mechanisms are discussed to illustrate how the chemistry could strongly affect the economics of the process. An analysis of the electron kinetics show that electrical discharge reactors are the most suitable only for processes requiring O radicals. For pollution control applications requiring copious amounts of electrons, ions, N atoms or OH radicals, the sue of electron beam reactors is generally the best way of minimizing the electrical power consumption

Effects of sources and meteorology on particulate matter in the Western Mediterranean Basin: an overview of the DAURE campaign

DAURE (Determination of the Sources of Atmospheric Aerosols in Urban and Rural Environments in the Western Mediterranean) was a multidisciplinary international field campaign aimed at investigating the sources and meteorological controls of particulate matter in the Western Mediterranean Basin (WMB). Measurements were simultaneously performed at an urban-coastal (Barcelona, BCN) and a rural-elevated (Montseny, MSY) site pair in NE Spain during winter and summer. State-of-the-art methods such as 14C analysis, proton-transfer reaction mass spectrometry, and high-resolution aerosol mass spectrometry were applied for the first time in the WMB as part of DAURE. WMB regional pollution episodes were associated with high concentrations of inorganic and organic species formed during the transport to inland areas and built up at regional scales. Winter pollutants accumulation depended on the degree of regional stagnation of an air mass under anticyclonic conditions and the planetary boundary layer height. In summer, regional recirculation and biogenic secondary organic aerosols (SOA) formation mainly determined the regional pollutant concentrations. The contribution from fossil sources to organic carbon (OC) and elemental carbon (EC) and hydrocarbon-like organic aerosol concentrations were higher at BCN compared with MSY due to traffic emissions. The relative contribution of nonfossil OC was higher at MSY especially in summer due to biogenic emissions. The fossil OC/EC ratio at MSY was twice the corresponding ratio at BCN indicating that a substantial fraction of fossil OC was due to fossil SOA. In winter, BCN cooking emissions were identified as an important source of modern carbon in primary organic aerosol

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review

Introduction Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bia

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

Atomic and molecular physics of plasma-based environmental technologies for abatement of volatile organic compounds

Non-thermal plasma techniques represent a new generation of air emission control technology that potentially could treat large-volume emissions containing dilute concentrations of volatile organic compounds (VOCs). In order to apply non-thermal plasmas in an industrial scale, it is important to establish the electrical power requirements and byproducts of the process. There is a need for reliable data concerning the primary decomposition mechanisms and subsequent chemical kinetics associated with non-thermal processing of VOCs. There are many basic atomic and molecular physics issues that are essential in evaluating the economic performance of non- thermal plasma reactors. These studies are important in understanding how the input electrical power is dissipated in the plasma and how efficiently it is converted to the production of the plasma species (radicals, ions, or electrons) responsible for the decomposition of the VOCs. This paper will present results from the basic experimental and theoretical studies aimed at identifying the reaction mechanisms responsible for the primary decomposition of various types of VOCs

Detection of noncyling cows by heatmount decectors and ultrasound before treatment with progesterone

Dairy Research, 2007 is known as Dairy Day, 2007Our objective was to determine accuracy of identifying anovulatory lactating dairy cows before the application of a timed AI protocol [with or without progesterone supplementation via a controlled internal drug release (CIDR) insert and 2 different timings of AI] by using heatmount detectors and a single ovarian ultrasound examination. At 6 Midwest locations, 1,072 cows were enrolled in a Presynch protocol (2 injections of prostaglandin F2α (PGF2α) 14 days apart) with the second injection administered 14 days before initiating the Ovsynch protocol (injection of gonadotropin releasing hormone (GnRH) 7 days before and 48 hours after PGF2α injection, with timed AI at 0 or 24 hours after the second GnRH injection). Heatmount detectors were applied to cows at the time of the first Presynch injection, assessed 14 days later at the second Presynch injection and again at initiation of the Ovsynch protocol, and ovaries were examined for presence of a visible corpus luteum (CL) by ultrasound before initiation of treatment. Treatments were assigned to cows based on presence or absence of a visible CL: 1) anovulatory (no CL + CIDR insert for 7 d); 2) anovulatory (no CL + no CIDR); and 3) cycling (CL present). Further, every other cow in the 3 treatments was assigned to be inseminated concurrent with the second GnRH injection of Ovsynch (0 hour) or 24 hours later. Pregnancy was diagnosed at 33 and 61 days after the second GnRH injection. Heatmount detectors and a single ultrasound examination both underestimated proportions of cows classified as anovulatory or having no prior luteal activity compared with those classifications determined by concentrations of progesterone in blood serum. Overall accuracy of heatmount detectors and ultrasound was 71 and 84%, respectively. Application of progesterone to cows without a CL at the time of the first injection of GnRH reduced incidence of ovulation but improved pregnancy rates at day 33 or 61 compared with nontreated cows without a CL at the onset of the Ovsynch protocol. Pregnancy rates and pregnancy survival did not differ for cows having a CL before treatment compared with those not having a CL but treated with progesterone. Pregnancy rates were 1.5-fold greater for cows ovulating in response to the first GnRH injection. Timing of AI at 0 or 24 hours after the second GnRH injection did not alter pregnancy rates, but cows having prior luteal activity before treatment had improved pregnancy rates compared with anovulatory cows. We conclude that identifying anovulatory cows by ultrasound was more accurate than by heatmount detectors. Subsequent treatment of potential anovulatory cows with progesterone failed to improve fertility but had benefit for cows with prior estrous cycles at the onset of the timed AI (TAI) protocol, regardless of luteal status before the final luteolytic injection of PGF2α

Effect of Paroxetine or Quetiapine Combined With Oxycodone vs Oxycodone Alone on Ventilation During Hypercapnia A Randomized Clinical Trial

Importance Opioids can cause severe respiratory depression by suppressing feedback mechanisms that increase ventilation in response to hypercapnia. Following the addition of boxed warnings to benzodiazepine and opioid products about increased respiratory depression risk with simultaneous use, the US Food and Drug Administration evaluated whether other drugs that might be used in place of benzodiazepines may cause similar effects.Objective To study whether combining paroxetine or quetiapine with oxycodone, compared with oxycodone alone, decreases the ventilatory response to hypercapnia.Design, Setting, and Participants Randomized, double-blind, crossover clinical trial at a clinical pharmacology unit (West Bend, Wisconsin) with 25 healthy participants from January 2021 through May 25, 2021.Interventions Oxycodone 10 mg on days 1 and 5 and the following in a randomized order for 5 days: paroxetine 40 mg daily, quetiapine twice daily (increasing daily doses from 100 mg to 400 mg), or placebo.Main Outcomes and Measures Ventilation at end-tidal carbon dioxide of 55 mm Hg (hypercapnic ventilation) using rebreathing methodology assessed for paroxetine or quetiapine with oxycodone, compared with placebo and oxycodone, on days 1 and 5 (primary) and for paroxetine or quetiapine alone compared with placebo on day 4 (secondary).Results Among 25 participants (median age, 35 years [IQR, 30-40 years]; 11 female [44%]), 19 (76%) completed the trial. The mean hypercapnic ventilation was significantly decreased with paroxetine plus oxycodone vs placebo plus oxycodone on day 1 (29.2 vs 34.1 L/min; mean difference [MD], -4.9 L/min [1-sided 97.5% CI, -infinity to -0.6]; P = .01) and day 5 (25.1 vs 35.3 L/min; MD, -10.2 L/min [1-sided 97.5% CI, -infinity to -6.3]; P < .001) but was not significantly decreased with quetiapine plus oxycodone vs placebo plus oxycodone on day 1 (33.0 vs 34.1 L/min; MD, -1.2 L/min [1-sided 97.5% CI, -infinity to 2.8]; P = .28) or on day 5 (34.7 vs 35.3 L/min; MD, -0.6 L/min [1-sided 97.5% CI, -infinity to 3.2]; P = .37). As a secondary outcome, mean hypercapnic ventilation was significantly decreased on day 4 with paroxetine alone vs placebo (32.4 vs 41.7 L/min; MD, -9.3 L/min [1-sided 97.5% CI, -infinity to -3.9]; P < .001), but not with quetiapine alone vs placebo (42.8 vs 41.7 L/min; MD, 1.1 L/min [1-sided 97.5% CI, -infinity to 6.4]; P = .67). No drug-related serious adverse events were reported.Conclusions and Relevance In this preliminary study involving healthy participants, paroxetine combined with oxycodone, compared with oxycodone alone, significantly decreased the ventilatory response to hypercapnia on days 1 and 5, whereas quetiapine combined with oxycodone did not cause such an effect. Additional investigation is needed to characterize the effects after longer-term treatment and to determine the clinical relevance of these findings