Measuring the performance of environmentally sustainable supply chains instead of chain constitute has become a challenge despite the convergence of the underlining principles of sustainable supply chain management. This challenge is exacerbated by the fact that supply chains are inherently dynamic and complex and also because multiple measures can be used to characterize performances. By identifying some of the critical issues in the literature regarding performance measurements, this paper contributes to the existing body of literature by adopting an environmental performance measurement approach for economic sectors. It uses economic sectors and evaluates them on a sectoral level in specific countries as well as part of the Global Value Chain based on the established multi-regional input-output (MRIO) modeling framework. The MRIO model has been used to calculate direct and indirect (that is supply chain or upstream) environmental effects such as CO2, SO2, biodiversity, water consumption and pollution to name just a few of the applications. In this paper we use MRIO analysis to calculate emissions and resource consumption intensities and footprints, direct and indirect impacts, and net emission flows between countries. These are exemplified by using carbon emissions, sulphur oxide emissions and water use in two highly polluting industries; Electricity production and Chemical industry in 33 countries, including the EU-27, Brazil, India and China, the USA, Canada and Japan from 1995 to 2009. Some of the highlights include: On average, direct carbon emissions in the electricity sector across all 27 member states of the EU was estimated to be 1368 million tons and indirect carbon emissions to be 470.7 million tons per year representing 25.6% of the EU-27 total carbon emissions related to this sector. It was also observed that from 2004, sulphur oxide emissions intensities in electricity production in India and China have remained relatively constant at about 62.8 gSOx/, respectively, $ and 84.4 gSOx/$ although being higher than in other countries. In terms of water use, the high water use intensity in China (1040.27 L/$) and India (961.63 L/$), which are among the highest in the sector in the electricity sector is exacerbated by both countries being ranked as High Water Stress Risk countries. The paper also highlights many advantages of the MRIO approach including: a 15-year time series study (which provides a measurement of environmental performance of key industries and an opportunity to assess technical and technological change during the investigated time period), a supply chain approach that provides a consistent methodological framework and accounts for all upstream supply chain environmental impacts throughout entire global supply chains. The paper also discusses the implications of the study to environmental sustainability performance measurement in terms of the level of analysis from a value chain hierarchy perspective, methodological issues, performance indicators, environmental exchanges and policy relevance.
This study has the purpose of assessing the changes in the efficiency scores caused by any additional variable introduced in a Stochastic Frontier Analysis, one of the most spread parametric methods which is able to differentiate the technical inefficiency of the unit assessed from the statistical noise and other exogenous factors that affect efficiency. The study will begin with the model Health Adjusted Life expectancy as input and Life Expectancy as output. After analyzing the results, maternal mortality will be added as input in the set and the model will be re-run. Data will be interpreted. A third input, Gini index, will be introduced in the last part of the analysis, in order to assess the new results of the model. As secondary objective, the study will evaluate the efficiency of the 27 European Union Health Systems and the changes from one model to another. The results show that by adding variables, the stochastic frontier and the efficiency scores change. Nonetheless, the direction of change is not random and the results are consistent with the theory.
- MeSH
- Humans MeSH
- Mathematics MeSH
- Maternal Mortality * MeSH
- Life Expectancy MeSH
- Statistics as Topic * MeSH
- Pregnancy MeSH
- Check Tag
- Humans MeSH
- Pregnancy MeSH
- Female MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- MeSH
- Electric Stimulation methods instrumentation MeSH
- Human Experimentation MeSH
- Financing, Organized MeSH
- Muscle Fibers, Skeletal physiology MeSH
- Muscle, Skeletal physiology MeSH
- Humans MeSH
- Motor Neurons physiology MeSH
- Muscle Relaxation physiology MeSH
- Statistics as Topic methods MeSH
- Muscle Contraction physiology MeSH
- Check Tag
- Humans MeSH
Responsivity is a conversion qualification of a measurement device given by the functional dependence between the input and output quantities. A concentration-response-dependent calibration curve represents the most simple experiment for the measurement of responsivity in mass spectrometry. The cyanobacterial hepatotoxin microcystin-LR content in complex biological matrices of food additives was chosen as a model example of a typical problem. The calibration curves for pure microcystin and its mixtures with extracts of green alga and fish meat were reconstructed from the series of measurement. A novel approach for the quantitative estimation of ion competition in ESI is proposed in this paper. We define the correlated responsivity offset in the intensity values using the approximation of minimal correlation given by the matrix to the target mass values of the analyte. The estimation of the matrix influence enables the approximation of the position of a priori unknown responsivity and was easily evaluated using a simple algorithm. The method itself is directly derived from the basic attributes of the theory of measurements. There is sufficient agreement between the theoretical and experimental values. However, some theoretical issues are discussed to avoid misinterpretations and excessive expectations.
- MeSH
- Peptides, Cyclic analysis MeSH
- Mass Spectrometry * MeSH
- Ions chemistry isolation & purification MeSH
- Humans MeSH
- Microcystins isolation & purification MeSH
- Food Additives analysis MeSH
- Regression Analysis MeSH
- Cyanobacteria isolation & purification MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
The instrument for continuous divergent flow IEF based on our principles set and outlined previously was further extended and tested. The separation and focusing area of a trapezoidal shape had a porous bed made from a nonwoven textile material with thickness decreasing from narrow input to wide output. A narrow end was used as a single input to continuously bring a single solution into separation space with a flow rate of 0.18 mL/min. Two pairs of electrodes were positioned close to both narrow and wide ends of the separation area with equilibrium state voltage of 75 V at the narrow input and 384 V at the wide output. Under dynamic equilibrium state, the zones of both pH gradient components and analytes were separated close to the input point and focused with increasing resolution while transporting through the separation space. The long-term stability experiments had shown the suitability of the device for preparative analysis; the zones of pI markers, hemoglobin and cytochrome C remained focused and separated over 15 h with deviations from the mean focusing positions ranging from 1.26 to 3.96% of the bed output width.
The main goal of this paper is to propose application of modern multidimensional systems identification algorithms of the subspace identification theory in the context of fMRI data analysis. The methods originated in 1990s in the field of process control and identification and yield robust linear model parameter estimates for systems with many inputs, outputs and states. Our ultimate goal is to establish an alternative to the DCM analysis procedure which would eliminate its main drawbacks, namely the need to pre-define the models structure. The paper discusses results based on simulated data provided by the DCM simulator in the SPM toolbox. Several scenarios are presented, with varying amount of noise and number of data samples.
- MeSH
- Algorithms MeSH
- Electronic Data Processing MeSH
- Bayes Theorem MeSH
- Financing, Organized MeSH
- Oxygen metabolism MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Brain Mapping methods MeSH
- Brain metabolism pathology MeSH
- Computer Simulation MeSH
- Signal Processing, Computer-Assisted MeSH
- Software MeSH
- Models, Statistical MeSH
- Models, Theoretical MeSH
- Check Tag
- Humans MeSH
Despite significant advances in the understanding of protein structure-function relationships, revealing protein folding pathways still poses a challenge due to a limited number of relevant experimental tools. Widely-used experimental techniques, such as calorimetry or spectroscopy, critically depend on a proper data analysis. Currently, there are only separate data analysis tools available for each type of experiment with a limited model selection. To address this problem, we have developed the CalFitter web server to be a unified platform for comprehensive data fitting and analysis of protein thermal denaturation data. The server allows simultaneous global data fitting using any combination of input data types and offers 12 protein unfolding pathway models for selection, including irreversible transitions often missing from other tools. The data fitting produces optimal parameter values, their confidence intervals, and statistical information to define unfolding pathways. The server provides an interactive and easy-to-use interface that allows users to directly analyse input datasets and simulate modelled output based on the model parameters. CalFitter web server is available free at https://loschmidt.chemi.muni.cz/calfitter/.
This paper presents the use of an artificial neural network (NN) approach for predicting the muscle forces around the elbow joint. The main goal was to create an artificial NN which could predict the musculotendon forces for any general muscle without significant errors. The input parameters for the network were morphological and anatomical musculotendon parameters, plus an activation level experimentally measured during a flexion/extension movement in the elbow. The muscle forces calculated by the 'Virtual Muscle System' provide the output. The cross-correlation coefficient expressing the ability of an artificial NN to predict the "true" force was in the range 0.97-0.98. A sensitivity analysis was used to eliminate the less sensitive inputs, and the final number of inputs for a sufficient prediction was nine. A variant of an artificial NN for a single specific muscle was also studied. The artificial NN for one specific muscle gives better results than a network for general muscles. This method is a good alternative to other approaches to calculation of muscle force.
- MeSH
- Algorithms * MeSH
- Models, Biological * MeSH
- Muscle, Skeletal physiology MeSH
- Humans MeSH
- Elbow Joint physiology MeSH
- Stress, Mechanical MeSH
- Neural Networks, Computer * MeSH
- Computer Simulation MeSH
- Movement physiology MeSH
- Reproducibility of Results MeSH
- Pattern Recognition, Automated methods MeSH
- Range of Motion, Articular MeSH
- Sensitivity and Specificity MeSH
- Muscle Contraction physiology MeSH
- Muscle Strength physiology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
This article deals with continuous-time Linear Time-Invariant (LTI) Single-Input Single-Output (SISO) systems affected by unstructured multiplicative uncertainty. More specifically, its aim is to present an approach to the construction of uncertain models based on the appropriate selection of a nominal system and a weight function and to apply the fundamentals of robust stability investigation for considered sort of systems. The initial theoretical parts are followed by three extensive illustrative examples in which the first order time-delay, second order and third order plants with parametric uncertainty are modeled as systems with unstructured multiplicative uncertainty and subsequently, the robust stability of selected feedback loops containing constructed models and chosen controllers is analyzed and obtained results are discussed.
- MeSH
- Uncertainty * MeSH
- Programming, Linear * MeSH
- Publication type
- Journal Article MeSH
Tunnels and channels facilitate the transport of small molecules, ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chemical properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. Herein we present a new version of CAVER enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. A trajectory from a molecular dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the analysis of molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estimated the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that analysis of molecular dynamics simulation is essential for the estimation of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochemical phenomena in the area of molecular transport, molecular recognition and enzymatic catalysis. The software is freely available as a multiplatform command-line application at http://www.caver.cz.
- MeSH
- Algorithms * MeSH
- Hydrolases chemistry metabolism MeSH
- Protein Conformation * MeSH
- Crystallography MeSH
- Proteins chemistry metabolism MeSH
- Cluster Analysis MeSH
- Molecular Dynamics Simulation MeSH
- Software * MeSH
- Computational Biology methods MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
