Exact solution, scaling behaviour and quantum dynamics of a model of an atom-molecule Bose-Einstein condensate

We study the exact solution for a two-mode model describing coherent coupling between atomic and molecular Bose-Einstein condensates (BEC), in the context of the Bethe ansatz. By combining an asymptotic and numerical analysis, we identify the scaling behaviour of the model and determine the zero temperature expectation value for the coherence and average atomic occupation. The threshold coupling for production of the molecular BEC is identified as the point at which the energy gap is minimum. Our numerical results indicate a parity effect for the energy gap between ground and first excited state depending on whether the total atomic number is odd or even. The numerical calculations for the quantum dynamics reveals a smooth transition from the atomic to the molecular BEC.Comment: 5 pages, 4 figure

Drone-based Water Sampling and Characterization of Three Freshwater Harmful Algal Blooms in the United States

Freshwater harmful algal blooms (HABs), caused mostly by toxic cyanobacteria, produce a range of cyanotoxins that threaten the health of humans and domestic animals. Climate conditions and anthropogenic influences such as agricultural run-off can alter the onset and intensity of HABs. Little is known about the distribution and spread of freshwater HABs. Current sampling protocols in some lakes involve teams of researchers that collect samples by hand from a boat and/or from the shoreline. Water samples can be collected from the surface, from discrete-depth collections, and/or from depth-integrated intervals. These collections are often restricted to certain months of the year, and generally are only performed at a limited number of collection sites. In lakes with active HABs, surface samples are generally sufficient for HAB water quality assessments. We used a unique DrOne Water Sampling SystEm (DOWSE) to collect water samples from the surface of three different HABs in Ohio (Grand Lake St Marys, GLSM and Lake Erie) and Virginia (Lake Anna), United States in 2019. The DOWSE consisted of a 3D-printed sampling device tethered to a drone (uncrewed aerial system, or UAS), and was used to collect surface water samples at different distances (10–100 m) from the shore or from an anchored boat. One hundred and eighty water samples (40 at GLSM, 20 at Lake Erie, and 120 at Lake Anna) were collected and analyzed from 18 drone flights. Our methods included testing for cyanotoxins, phycocyanin, and nutrients from surface water samples. Mean concentrations of microcystins (MCs) in drone water samples were 15.00, 1.92, and 0.02 ppb for GLSM, Lake Erie, and Lake Anna, respectively. Lake Anna had low levels of anatoxin in nearly all (111/120) of the drone water samples. Mean concentrations of phycocyanin in drone water samples were 687, 38, and 62 ppb for GLSM, Lake Erie, and Lake Anna, respectively. High levels of total phosphorus were observed in the drone water samples from GLSM (mean of 0.34 mg/L) and Lake Erie (mean of 0.12 mg/L). Lake Anna had the highest variability of total phosphorus with concentrations that ranged from 0.01 mg/L to 0.21 mg/L, with a mean of 0.06 mg/L. Nitrate levels varied greatly across sites, inverse with bloom biomass, ranging from below detection to 3.64 mg/L, with highest mean values in Lake Erie followed by GLSM and Lake Anna, respectively. Drones offer a rapid, targeted collection of water samples from virtually anywhere on a lake with an active HAB without the need for a boat which can disturb the surrounding water. Drones are, however, limited in their ability to operate during inclement weather such as rain and heavy winds. Collectively, our results highlight numerous opportunities for drone-based water sampling technologies to track, predict, and respond to HABs in the future

Labor Productivity - The Use of Staffing Guides and Other Productivity Methods in U.S. Hotels: A Survey Study

This paper examines the issue of labor productivity in hotels. It elaborates on various measurement methods used by American hoteliers including staffing guides, with a special focus on productivity standards. Advantages of physical, financial and mixed methods such as percentage methods, revenue per employee, value-added, Data Envelope Analysis and Stochastic Frontier Analysis are discussed. While the use of percentages and staffing guides were found to be commonplace, results revealed that some fairly stable standards were already in place in the surveyed hotels. Results also revealed that at least in the surveyed companies, few hoteliers attempted to monitor or improve revenue per employee, focusing instead on physical labor inputs and outputs or simple labor percentages

Enterprise transformation: Why are we interested, what is it, and what are the challenges?

© IIE, INCOSE. The concept of enterprise transformation has become increasingly popular as companies recognize the need to achieve an integrated perspective within and across organizational boundaries to address complex challenges. Yet, there is little clarity concerning what constitutes an “enterprise” or indeed “enterprise transformation.” This article is conceived as an initial step along the journey towards this clarity. There is considerable work to be done in delineating this area of interest and this article is offered as a stimulus for debate on what constitutes enterprise transformation. Drawing on themes from the management and systems engineering disciplines, the article will propose four characteristics of “enterprise” as a unit for transformation and look at why this holistic unit of analysis has become critical to businesses. The article will also ask what constitutes transformation, and offer characterizing criteria to distinguish this magnitude of change from more incremental changes. A recent empirical case study will be examined to further elucidate challenges faced in defining, leading, and transforming multi-organizational enterprises. Finally, a near-term research agenda is outlined for the evolving discipline of enterprise transformation

Measuring stress in medical education: validation of the Korean version of the higher education stress inventory with medical students

Background: Medical students face a variety of stressors associated with their education; if not promptly identified and adequately dealt with, it may bring about several negative consequences in terms of mental health and academic performance. This study examined psychometric properties of the Korean version of the Higher Education Stress Inventory (K-HESI). Methods: The reliability and validity of the K-HESI were examined in a large scale multi-site survey involving 7110 medical students. The K-HESI, Beck Depression Inventory (BDI) and questions regarding quality of life (QOL) and self-rated physical health (SPH) were administered. Results: Exploratory factor analysis of the K-HESI identified seven factors: Low commitment; financial concerns; teacher-student relationship; worries about future profession; non-supportive climate; workload; and dissatisfaction with education. A subsequent confirmatory factor analysis supported the 7-factor model. Internal consistency of the K-HESI was satisfactory (Cronbach's a = .78). Convergent validity was demonstrated by its positive association with the BDI. Known group validity was supported by the K-HESI's ability to detect significant differences on the overall and subscale scores of K-HESI according to different levels of QOL and SPH. Conclusions: The K-HESI is a psychometrically valid tool that comprehensively assesses various relevant stressors related to medical education. Evidence-based stress management in medical education empirically guided by the regular assessment of stress using reliable and valid measure is warranted.open

Testing for heterogeneity among the components of a binary composite outcome in a clinical trial

<p>Abstract</p> <p>Background</p> <p>Investigators designing clinical trials often use composite outcomes to overcome many statistical issues. Trialists want to maximize power to show a statistically significant treatment effect and avoid inflation of Type I error rate due to evaluation of multiple individual clinical outcomes. However, if the treatment effect is not similar among the components of this composite outcome, we are left not knowing how to interpret the treatment effect on the composite itself. Given significant heterogeneity among these components, a composite outcome may be judged as being invalid or un-interpretable for estimation of the treatment effect. This paper compares the power of different tests to detect heterogeneity of treatment effect across components of a composite binary outcome.</p> <p>Methods</p> <p>Simulations were done comparing four different models commonly used to analyze correlated binary data. These models included: logistic regression for ignoring correlation, logistic regression weighted by the intra cluster correlation coefficient, population average logistic regression using generalized estimating equations (GEE), and random effects logistic regression.</p> <p>Results</p> <p>We found that the population average model based on generalized estimating equations (GEE) had the greatest power across most scenarios. Adequate power to detect possible composite heterogeneity or variation between treatment effects of individual components of a composite outcome was seen when the power for detecting the main study treatment effect for the composite outcome was also reasonably high.</p> <p>Conclusions</p> <p>It is recommended that authors report tests of composite heterogeneity for composite outcomes and that this accompany the publication of the statistically significant results of the main effect on the composite along with individual components of composite outcomes.</p

Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response

Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs

Single-Dose Intravenous Toxicity Study of IRDye 800CW in Sprague-Dawley Rats

Objective: Fluorophore-labeled contrast imaging agents are moving toward clinical use for a number of applications. The near-infrared dye IRDye 800CW is frequently used in its N-hydroxysuccinamide (NHS) ester form for labeling these agents. Following conjugation or breakdown of a labeled ligand, excess NHS ester is converted to the carboxylate form. To prepare for clinical use as a near-infrared fluorophore, a toxicity study was conducted on IRDye 800CW carboxylate. Methods: Male and female Sprague–Dawley rats were given a single intravenous or intradermal administration of IRDye 800CW carboxylate; Indocyanine Green was used as a comparative control. Animals were injected with varying doses of the test and control articles and observed for up to 14 days. Clinical chemistry, hematological, and pharmacokinetic analyses were performed on subgroups of animals. Organs were analyzed for content of the test article. Tissues were analyzed microscopically for pathological changes. Results: Based on hematologic, clinical chemistry, and histopathologic evaluation, single administration of IRDye 800CW carboxylate intravenously at dose levels of 1, 5, and 20 mg/kg or 20 mg/kg intradermally produced no pathological evidence of toxicity. Conclusion: A dose of 20 mg/kg was identified as the no observed adverse effect level following IV or ID routes of administration of IRDye 800CW

Successful recruitment to trials : findings from the SCIMITAR+ Trial

BACKGROUND: Randomised controlled trials (RCT) can struggle to recruit to target on time. This is especially the case with hard to reach populations such as those with severe mental ill health. The SCIMITAR+ trial, a trial of a bespoke smoking cessation intervention for people with severe mental ill health achieved their recruitment ahead of time and target. This article reports strategies that helped us to achieve this with the aim of aiding others recruiting from similar populations. METHODS: SCIMITAR+ is a multi-centre pragmatic two-arm parallel-group RCT, which aimed to recruit 400 participants with severe mental ill health who smoke and would like to cut down or quit. The study recruited primarily in secondary care through community mental health teams and psychiatrists with a smaller number of participants recruited through primary care. Recruitment opened in October 2015 and closed in December 2016, by which point 526 participants had been recruited. We gathered information from recruiting sites on strategies which led to the successful recruitment in SCIMITAR+ and in this article present our approach to trial management along with the strategies employed by the recruiting sites. RESULTS: Alongside having a dedicated trial manager and trial management team, we identified three main themes that led to successful recruitment. These were: clinicians with a positive attitude to research; researchers and clinicians working together; and the use of NHS targets. The overriding theme was the importance of relationships between both the researchers and the recruiting clinicians and the recruiting clinicians and the participants. CONCLUSIONS: This study makes a significant contribution to the limited evidence base of real-world cases of successful recruitment to RCTs and offers practical guidance to those planning and conducting trials. Building positive relationships between clinicians, researchers and participants is crucial to successful recruitment

Risk Factors and Immunity in a Nationally Representative Population following the 2009 Influenza A(H1N1) Pandemic

Understanding immunity, incidence and risk factors of the 2009 influenza A(H1N1) pandemic (2009 H1N1) through a national seroprevalence study is necessary for informing public health interventions and disease modelling.We collected 1687 serum samples and individual risk factor data between November-2009 to March-2010, three months after the end of the 2009 H1N1 wave in New Zealand. Participants were randomly sampled from selected general practices countrywide and hospitals in the Auckland region. Baseline immunity was measured from 521 sera collected during 2004 to April-2009. Haemagglutination inhibition (HI) antibody titres of ≥1∶40 against 2009 H1N1 were considered seroprotective as well as seropositive. The overall community seroprevalence was 26.7% (CI:22.6–29.4). The seroprevalence varied across age and ethnicity. Children aged 5–19 years had the highest seroprevalence (46.7%;CI:38.3–55.0), a significant increase from the baseline (14%;CI:7.2–20.8). Older adults aged ≥60 had no significant difference in seroprevalence between the serosurvey (24.8%;CI:18.7–30.9) and baseline (22.6%;CI:15.3–30.0). Pacific peoples had the highest seroprevalence (49.5%;CI:35.1–64.0). There was no significant difference in seroprevalence between both primary (29.6%;CI:22.6–36.5) and secondary healthcare workers (25.3%;CI:20.8–29.8) and community participants. No significant regional variation was observed. Multivariate analysis indicated age as the most important risk factor followed by ethnicity. Previous seasonal influenza vaccination was associated with higher HI titres. Approximately 45.2% of seropositive individuals reported no symptoms.Based on age and ethnicity standardisation to the New Zealand Population, about 29.5% of New Zealanders had antibody titers at a level consistent with immunity to 2009 H1N1. Around 18.3% of New Zealanders were infected with the virus during the first wave including about one child in every three. Older people were protected due to pre-existing immunity. Age was the most important factor associated with infection followed by ethnicity. Healthcare workers did not appear to have an increased risk of infection compared with the general population