Membrane stripping enables effective electrochemical ammonia recovery from urine while retaining microorganisms and micropollutants

Ammonia recovery from urine avoids the need for nitrogen removal through nitrification/denitrification and re-synthesis of ammonia (NH3) via the Haber-Bosch process. Previously, we coupled an alkalifying electrochemical cell to a stripping column, and achieved competitive nitrogen removal and energy efficiencies using only electricity as input, compared to other technologies such as conventional column stripping with air. Direct liquid-liquid extraction with a hydrophobic gas membrane could be an alternative to increase nitrogen recovery from urine into the absorbent while minimizing energy requirements, as well as ensuring microbial and micropollutant retention. Here we compared a column with a membrane stripping reactor, each coupled to an electrochemical cell, fed with source-separated urine and operated at 20 A m−2. Both systems achieved similar nitrogen removal rates, 0.34 ± 0.21 and 0.35 ± 0.08 mol N L−1 d−1, and removal efficiencies, 45.1 ± 18.4 and 49.0 ± 9.3%, for the column and membrane reactor, respectively. The membrane reactor improved nitrogen recovery to 0.27 ± 0.09 mol N L−1 d−1 (38.7 ± 13.5%) while lowering the operational (electrochemical and pumping) energy to 6.5 kWhe kg N−1 recovered, compared to the column reactor, which reached 0.15 ± 0.06 mol N L−1 d−1 (17.2 ± 8.1%) at 13.8 kWhe kg N−1. Increased cell concentrations of an autofluorescent E. coli MG1655 + prpsM spiked in the urine influent were observed in the absorbent of the column stripping reactor after 24 h, but not for the membrane stripping reactor. None of six selected micropollutants spiked in the urine were found in the absorbent of both technologies. Overall, the membrane stripping reactor is preferred as it improved nitrogen recovery with less energy input and generated an E. coli- and micropollutant-free product for potential safe reuse. Nitrogen removal rate and efficiency can be further optimized by increasing the NH3 vapor pressure gradient and/or membrane surface area

Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: an integration of innovation diffusion theory and TAM with trust

The growing presence of online travel communities is leading to great developments in the travel industry. Grounded in the innovation diffusion theory (IDT) and the technology acceptance model (TAM), this paper seek to develop and empirically test a comprehensive framework to examine the antecedents of customers' intention to participate in online travel community. Using SEM to analyse the data collected from a sample of 495 members, the results indicate that innovation diffusion theory and TAM with trust provide an appropriate model for explaining consumers' intention to participate; this intention in turn has a positive influence on intention to purchase and positive WOM. Furthermore, religiosity plays an important role in understanding consumers' behavioural intention. The results offer important implications for online service provider and are likely to stimulate further research in the area of online travel community

Exploring data provenance in handwritten text recognition infrastructure:Sharing and reusing ground truth data, referencing models, and acknowledging contributions. Starting the conversation on how we could get it done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, and ways to reference and acknowledge contributions to the creation and enrichment of data within these Machine Learning systems. We discuss how one can publish Ground Truth data in a repository and, subsequently, inform others. Furthermore, we suggest appropriate citation methods for HTR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of Machine Learning in archival and library contexts, and how the community should begin toacknowledge and record both contributions and data provenance

Exploring Data Provenance in Handwritten Text Recognition Infrastructure: Sharing and Reusing Ground Truth Data, Referencing Models, and Acknowledging Contributions. Starting the Conversation on How We Could Get It Done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, as well as ways to reference and acknowledge contributions to the creation and enrichment of data within these systems. We discuss how one can place Ground Truth data in a repository and, subsequently, inform others through HTR-United. Furthermore, we want to suggest appropriate citation methods for ATR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of machine learning in archival and library contexts, and how the community should begin to acknowledge and record both contributions and data provenance

Value of measuring diurnal peak flow variability in the recognition of asthma: a study in general practice

In this study we analysed the value of measuring diurnal peak Bow variability (DPV) in general practice for diagnosing asthma or chronic obstructive pulmonary disease (COPD), One hundred and eighty-two subjects, aged 18-75 yrs, with undiagnosed asthma or COPD, presenting with a persistent cough recorded a peak flow diary twice daily for 2 weeks. A diagnosis of asthma or COPD was based on the recurrence of airway symptoms in the past year accompanied by spirometric measurements and a provocative dose of methacholine causing a 20% fall in forced expiratory volume in one second. DPV was expressed as amplitude percentage highest of the day. Cut-off values of 15% and 20% (DPV15%, DPV20%) were employed and the number of days that these values were reached, was assessed. The influence of age, sex and pack-years smoking on DPV was analysed by logistic regression. The a priori probability to have asthma (n=69) or COPD (n=12) was 45% (81/182) and increased to >70% with a DPV20% for at least 3, and a DPV15% for at least 4 days. Scoring formulas for asthma (DPV15% (number of days present) + 4 (if female sex)) and for asthma and COPD combined (8x DPV15% (number of days present) + 24 (if female sex) + pack-years smoking) predicted which subjects were at risk for having asthma (or COPD), Simple formulas based on the number of days with diurnal peak flow variability at 15%, female sex and pack-years can predict which patients with persistent cough are likely to have asthma or chronic obstructive pulmonary disease