Estimation of bacterial production through the coupling of (methyl 3H) Thymidine incorporation and several parameters over a Microcystis aeruginosa bloom in a hypereutrophic reservoir

The coupling of the changes in bacterial quantitative and metabolic aspects during Microcystis aeruginosa bloom conditions together with several environmental parameters was studied in the hypereutrophic Villerest reservoir, France. Bacterial abundance varied from 5.20 to 21.28 × 10⁶ bacteria-mL^?1, while bacterial biomass ranged between 75 and 507 μg C mL^?1. These results confirmed the highly eutrophic status of the Villerest reservoir. The relative quantitative importance of attached bacteria increased as Microcystis proliferated. Methyl-³H incorporation and D-(U-¹⁴C) glucose uptake varied from 8.99 to 60.57 × 10⁶ cells-mL^?1, and 0.016 to 1.587 μg CL^?1 h^?1, respectively. Correlations between several abiotic and biotic parameters showed that phytoplankton regulated bacterial growth by releasing organic carbon which is directly uptaken by heterotrophic bacteria. In the hypolimnion, the sedimentation of decaying algae represented a substantial substrate for bacterial growth.     

Improving an outpatient clinic utilization using decision analysis-based patient scheduling

This study presents a predictive model to be used in scheduling patients in an urban outpatient clinic. The model is based upon actual patient characteristics from a physical therapy clinic within an urban health and wellness center situated in a public university. A number of reported patients' characteristics such as age, education level, distance from the clinic, historical attendance records, etc. were examined to determine if they significantly impacted the patients' missing scheduled appointments (no-shows.) Decision tree analysis was used to develop a model that assessed the likelihood of a patient's no-show, using key patient characteristics and attendance records. Such a model can be used to assist with scheduling patients in an outpatient clinic, while attempting to increase the clinic's overall utilization. Four tree growing criteria were examined to develop the model with the strongest predictive power. Predictive power of each method was assessed by using the entire dataset as well as using split sampling. The results were then compared with those of a Bayesian networks model and a neural networks model. In addition, the trade-off between the selected decision tree model's predictive power versus simplicity of the associated classification rules was examined. We also assessed the impact of various levels of overbooking on the clinic's utilization when using patients' schedules based on the predictive model.     

Forecasting technology diffusion: a new generalisation of the logistic model

The present research proposes a new generalisation of the logistic model aiming at technology diffusion forecasting. Regarding criticisms and failures reported in the literature to apply logistic function for long-term forecasting, in our work we focused on short-term accuracy of forecast. To formulate the model, based on mathematical approximation, at first the differential equation governing the diffusion process is found and then by solving the derived differential equation, the forecast function is obtained. In all steps, mathematical tools from numerical analysis are used. We compared the New Generalized Logistic Model with eight of the most renowned models in the literature. The model led to more accurate fits and forecasts than those obtained from other models we applied for comparison.     

Technology-based ventures and sustainable development: Cointegrating and causal relationships with a panel data approach

The aim of this article is to provide new empirical evidence on the causality between proxy variables of technology entrepreneurship and proxy variable of sustainable economic performance in a vector error correction model. It covers a sample of 13 countries participated to Global Entrepreneurship Monitor studies under the period 2002–2013. Building on a theoretical background that considers the adoption of new technologies through a dynamic process of creative destruction based on innovation as the most important factor for achieving long-term economic growth, the empirical investigation uses robust econometric techniques that are capable of estimating long-run cointegrating relationships in panel data.

Our results support the idea that total entrepreneurship activity related to the technology sector leads to improve the sustainability of a nation in the long run. More importantly, our paper helps understand the nature of liaison between the creation of innovative and high-technology business and the presence of favorable social and environmental conditions for the well-being of a population.     

A new method for measuring congestion in data envelopment analysis

In data envelopment analysis (DEA), there are two principal methods for identifying and measuring congestion: Those of Färe et al. [Färe R, Grosskopf S. When can slacks be used to identify congestion. An answer to W. W. Cooper, L. Seiford, J. Zhu. Socio-Economic Planning Sciences 2001;35:1–10] and Cooper et al. [Cooper WW, Deng H, Huang ZM, Li SX. A one-model approach to congestion in data envelopment analysis. Socio-Economic Planning Sciences 2002;36:231–8]. In the present paper, we focus on the latter work in proposing a new method that requires considerably less computation. Then, by proving a selected theorem, we show that our proposed methodology is indeed equivalent to that of Cooper et al.     

Relative contribution of bacteria, algae and ciliates to the microplankton biomass in an eutrophic lake

Biomass in size-fractionated plankton (0.2–1.2 μm; 1.2–12/μm; 12–45 μm and 45–160 μm), together with that of ciliated protozoans (size fraction 1–160 μm), were assessed during stratification, in an eutrophic lake (Lake Aydat, Massif Central, France). Samples (51) were gathered from depths of 2, 7 and 14 m during 17 weekly samplings in the centre of the lake. Three biomass estimations were performed: cell-counts, chlorophyll a and ATP assays. Ciliates were counted live. Five species groups dominated the ciliate community: Loxodes striatus, Frontonia spp., Spirostomum ambiguum, Strombidium spp. and Dexiotrica plagia. Frontonia always had the highest cell concentration (54–64% of total cell counts), while the bulk of biomass was divided between Loxodes (42–70% of total) and Frontonia (29–53% of total). In terms of carbon, both the bacteria and ciliate contributions to the total microplanktonic biomass (size fraction 1–160 μm, i.e. bacteria + phytoplankton + ciliates) depends on the choice of the phytoplankton biomass estimation technique used. Thus, the contributions increase respectively from 16% and 19%, when phytoplankton biomass is estimated from chlorophyll α concentrations, to 24% and 27%, when phytoplankton biomass is based on biovolume. Relationships between bacteria, phytoplankton and ciliated protozoans reveal that ATP data are greatly overestimated by bacteria and ciliate interferences, especially in the metalimnion and hypolimnion. In the epilimnion, this interference is less. Interpretations of phytoplankton dynamics and metabolic activity from ATP assays should be made with extreme care, especially in eutrophic environments.     

Notes on Sensitivity and Stability of the Classifications of Returns to Scale in Data Envelopment Analysis

As its title suggests, this note constitutes a critique of the paper by Seiford and Zhu (1999), Sensitivity and Stability of the Classifications of Returns to Scale (RTS) in Data Envelopment Analysis. By means of counter examples, we discuss some problems related to results presented in that paper.     

Dimensions of aberrant driving behaviours in Tunisia: identifying the relation between Driver Behaviour Questionnaire results and accident data

In this study, the Manchester Driver Behaviour Questionnaire (DBQ) was used to examine the self-reported driving behaviours of a group of Tunisian drivers (N = 900) and to collect socio-demographic data, driver behaviours and DBQ items. A sample of Tunisian drivers above 18 years was selected. The aim of the present study was to investigate the factorial structure of the DBQ in Tunisia. The principal component analysis identified three factor solutions: inattention errors, dangerous errors and dangerous violations. Logistic regression analysis showed that dangerous errors, dangerous violations and speeding preference factors predicted crash involvement in Tunisia. Speeding is the most common form of aberrant behaviour reported by drivers in the current sample. It remains one of the major road safety concerns.     

Evolution of a new surface water quality index for Karoon catchment in Iran

Water quality standards are developed worldwide by national and international agencies for pollution control decision-making. Use-based water quality classification criteria and Water Quality Indices (WQIs) also play an important role in the assessment of the suitability of water resources for various applications. The present study proposes a better overall index for water quality in Iran and its application in Karoon River by exploring the behavior and limitations of conventional methods for quality evaluation. For this purpose, six variables were employed. Water quality determinants of the new index include Dissolved Oxygen, Total Dissolved Solids, Turbidity, Nitrate, Fecal coliform and pH. Besides, the mathematical equations applied to transform the actual concentration values into quality indices have been formulated. This study compares a new index called the Iranian Water Quality Index with other pre-existing indices such as NSFWQI, Oregon, CPCB WQI, MDOE WQI, Kaurish and Younos WQI, and Ahmed Said WQI. Results revealed that the overall quality of the surface water falls under the 'good' class. A case study of Karoon River is made to illustrate the application of this new index system.     

Conditional volatility in affine term-structure models: Evidence from Treasury and swap markets

We study the ability of three-factor affine term-structure models to extract conditional volatility using interest rate swap yields for 1991–2005 and Treasury yields for 1970–2003. For the Treasury sample, the correlation between model-implied and EGARCH volatility is between 60% and 75%. For the swap sample, this correlation is rather low or negative. We find that these differences in model performance are primarily due to the timing of the swap sample, and not to institutional differences between swap and Treasury markets. We conclude that the ability of multifactor affine models to extract conditional volatility depends on the sample period, but that overall these models perform better than has been argued in the literature.