Systematic Selection Method Research Articles

A Theoretical Derivation and Comparison Method for the Optimal Location for Energy Dissipation Boxes

In long-distance, high-elevation gravitational water supply systems, it is essential to incorporate energy dissipation to lower pipeline pressures. The energy dissipation box is a novel device for pressure reduction, extensively utilized in gravitational flow transition systems. Despite its appealing contribution, systematic selection methods still need to be used regarding its optimum location. Hence, this paper considers the sum-of-the-maximum hydrostatic pressure head (SMHPH) and derives an extreme location equation for the energy dissipation box (EDB) in the design stage, and then, it comprehensively accounts for the overcurrent capacity and proposes a theoretical comparison and selection method for the optimal location (OL) between the critical and extreme locations. A theoretical analysis with an engineering case study is conducted to compare the theoretical OL, and numerical simulations are carried out to compare the pressure protection effect of the same box cross-sectional area and initial water volume of the theoretical OL and other possible locations with different initial water depths. The results show that the location of the EDB in the design stage should comprehensively consider the overcurrent capacity and the SMHPH. On the basis of both conditions, the oscillation amplitudes of the pressure gradient beyond the box are significantly decreased as the OL approaches closer to the downstream pipe. If the initial water depth in the box is large, the EDB provides greater protection to the pressure head (PH) by decreasing the cross-sectional area and then maximizing the utilization of water depth in the box and decreasing the volume of the EDB.

Design and Implementation of Novel Rotor Side Brushless Controller With Bidirectional Wireless Power Transmission for Doubly-Fed Machine

This paper proposes a novel rotor side brushless controller (RSBC) with bidirectional wireless power transmission (WPT) for a doubly-fed motor (DFM), which can realize the brushless DFM without changing the stator and rotor structures. The WPT-RSBC consists of a grid side converter, a series–series compensated resonant converter, and a rotor side converter. According to the operational characteristics of DFMs, the design requirements of the WPT-RSBC are given in this paper. The systematic selection method for four-quadrant operations of the DFM is introduced, and the variable bus voltage control strategy is proposed to improve its transmission efficiency. The mathematical model and small-signal model of each subsystem in the WPT-RSBC are given, and the stability of the primary and secondary DC bus is analyzed. An experimental platform is implemented to verify the feasibility of the WPT-RSBC. For the prototype, the air-gap length is 1 mm, and the RSBC's transmission efficiency can be up to 92%.

SYSTEMATIC SELECTION OF BLOCKCHAIN PLATFORMS USING FUZZY AHP-TOPSIS

Various businesses and industries such as financial, medical care management, supply chain management, data management, Internet of Things (IoT) and government supremacy, have been using blockchain technology to develop systems. During the selection of blockchain platforms, many criteria need to be taken into account depending on the organization, project and use case requirements. This study proposes a systematic selection method based on the Fuzzy AHP-TOPSIS approach which compares and selects alternative blockchain platforms against a set of selection criteria that cover both features and non-functional properties. A case study was conducted to evaluate the applicability of the proposed selection method. The proposed selection method which consists of three main stages was applied for the comparison and selection of the most appropriate blockchain platform for two projects. In the case study, three blockchain platforms were selected and ranked for each project based on selection criteria derived from the project requirements. Both project representatives showed strong agreement with the applicability aspects of the proposed selection method. It is concluded that the proposed selection criteria and selection method can be applied practically to support the decision-makers in blockchain platform selection for real-world projects.

Radius selection using kernel density estimation for the computation of nonlinear measures.

When nonlinear measures are estimated from sampled temporal signals with finite-length, a radius parameter must be carefully selected to avoid a poor estimation. These measures are generally derived from the correlation integral, which quantifies the probability of finding neighbors, i.e., pair of points spaced by less than the radius parameter. While each nonlinear measure comes with several specific empirical rules to select a radius value, we provide a systematic selection method. We show that the optimal radius for nonlinear measures can be approximated by the optimal bandwidth of a Kernel Density Estimator (KDE) related to the correlation sum. The KDE framework provides non-parametric tools to approximate a density function from finite samples (e.g., histograms) and optimal methods to select a smoothing parameter, the bandwidth (e.g., bin width in histograms). We use results from KDE to derive a closed-form expression for the optimal radius. The latter is used to compute the correlation dimension and to construct recurrence plots yielding an estimate of Kolmogorov-Sinai entropy. We assess our method through numerical experiments on signals generated by nonlinear systems and experimental electroencephalographic time series.

Simulation of Sea Breeze Events in Gulf of Jakarta under Different Synoptic Condition: An Application of WRF Model

The behaviour of the sea breeze developing along the Gulf of Jakarta during days with different synoptic condition is examined using observational data and simulations from the Weather Research and Forecasting (WRF). An objective and systematic selection method is used to detect sea breeze days from observational data. The simulation used three nested grids at 27, 9 and 3 km resolution covering Gulf of Jakarta and its joining region for sea breeze event at November 22th, 2016 which formed under offshore large-scale ambient wind, January 20th, 2017 which formed under westerly large-scale ambient wind, and August 10th, 2017 which formed under easterly large-scale ambient wind. Results show that the development of sea breeze circulation under offshore ambient wind produced lower 10-m wind speed simulation, while sea breeze conditions under westerly large-scale ambient wind are associated with highest surface wind speeds reaching 9.5 ms-1 in Gulf of Jakarta. Sea breeze simulation also indicates the formation of calm zone which is 22 - 56 km away from the Jakarta Gulf coastline. Model also shows the formation of thermal internal boundary layer that are about 300-700 m near the coast gradually merged with the mixing layer inland. Performance of WRF on the simulated circulation is studied by conducting value of correlation, RMSE, and MBE. Overall, model simulation agrees fairly well with the observations taken at a near-surface meteorological station.

A Systematic Selection Method Between Neutral and Control of Running Footwear Insole Products

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Erratum to: A global approach for sparse representation of uncertainty in Life Cycle Assessments of waste management systems

Identification of key inputs and their effect on results from Life Cycle Assessment (LCA) models is fundamental. Because parameter importance varies greatly between cases due to the interaction of sensitivity and uncertainty, these features should never be defined a priori. However, exhaustive parametrical uncertainty analyses may potentially be complicated and demanding, both with analytical and sampling methods. Therefore, we propose a systematic method for selection of critical parameters based on a simplified analytical formulation that unifies the concepts of sensitivity and uncertainty in a Global Sensitivity Analysis (GSA) framework. The proposed analytical method based on the calculation of sensitivity coefficients (SC) is evaluated against Monte Carlo sampling on traditional uncertainty assessment procedures, both for individual parameters and for full parameter sets. Three full-scale waste management scenarios are modelled with the dedicated waste LCA model EASETECH and a full range of ILCD recommended impact categories. Common uncertainty ranges of 10 % are used for all parameters, which we assume to be normally distributed. The applicability of the concepts of additivity of variances and GSA is tested on results from both uncertainty propagation methods. Then, we examine the differences in discernibility analyses results carried out with varying numbers of sampling points and parameters. The proposed analytical method complies with the Monte Carlo results for all scenarios and impact categories, but offers substantially simpler mathematical formulation and shorter computation times. The coefficients of variation obtained with the analytical method and Monte Carlo differ only by 1 %, indicating that the analytical method provides a reliable representation of uncertainties and allows determination of whether a discernibility analysis is required. The additivity of variances and the GSA approach show that the uncertainty in results is determined by a limited set of important parameters. The results of the discernibility analysis based on these critical parameters vary only by 1 % from discernibility analyses based on the full set, but require significantly fewer Monte Carlo runs. The proposed method and GSA framework provide a fast and valuable approximation for uncertainty quantification. Uncertainty can be represented sparsely by contextually identifying important parameters in a systematic manner. The proposed method integrates with existing step-wise approaches for uncertainty analysis by introducing a global importance analysis before uncertainty propagation.

A systematic selection method for the development of cancer staging systems

The tumor-node-metastasis (TNM) staging system has been the anchor of cancer diagnosis, treatment, and prognosis for many years. For meaningful clinical use, an orderly, progressive condensation of the T and N categories into an overall staging system needs to be defined, usually with respect to a time-to-event outcome. This can be considered as a cutpoint selection problem for a censored response partitioned with respect to two ordered categorical covariates and their interaction. The aim is to select the best grouping of the TN categories. A novel bootstrap cutpoint/model selection method is proposed for this task by maximizing bootstrap estimates of the chosen statistical criteria. The criteria are based on prognostic ability including a landmark measure of the explained variation, the area under the receiver operating characteristic (ROC) curve, and a concordance probability generalized from Harrell's c-index. We illustrate the utility of our method by applying it to the staging of colorectal cancer.

A characterisation of sea-breeze events in the eastern Cantabrian coast (Spain) from observational data and WRF simulations

The behaviour of the sea breeze along the north coast of Spain is investigated using observations of two topographically contrasting sites together with simulations from the Weather Research and Forecasting (WRF) model. An objective and systematic selection method is used to detect sea-breeze days from a database of two summer months. The direction and intensity of the sea breeze are significantly affected by the topography of the area; indeed, the estimated sea-breeze intensity shows an opposite relationship with the cross-shore temperature gradient for both sites. WRF simulations reproduce the onset of the sea breeze, but some characteristics are not adequately simulated: they generally overestimate the wind speed, smooth the temperature evolution and they do not represent the correct interaction with the terrain-induced flows. Additionally, four sensitivity experiments are performed with the WRF model varying the Planetary Boundary Layer (PBL) scheme, as well as the grid analysis nudging for an anomalous case study which is incorrectly filtered. As the two simulations considering nudging reproduce an unreal (not observed) sea breeze, this day turns out to be of great interest: it allows to evaluate the influence of the passage of the sea-breeze front (SBF) in other variables mainly related to turbulence. Furthermore, the best model scores are obtained for the PBL scheme that does not use a TKE closure.

Mobile Phones and Psychosocial Therapies with Vulnerable People: a First State of the Art.

Mobile phones are becoming a communication tool commonly used by people all over the world; and they are started to be adopted in psychosocial therapies involving vulnerable people. We are herein presenting the results of an academic literature review. We identified scientific papers published between 2006 and 2015 resorting to academic databases available on the Internet, applying a systematic selection method based on quality criteria. Secondly, we analysed contents, highlighting the scarcity of research involving vulnerable people. The available literature specialized in psychosocial therapies offers investigation results which involve mobile phones and patients in general, focusing particularly on the clinical psychology field and, to a lesser extent, on the social work field. Particularly significant are the investigation works developed in the United States. In the present paper we introduce a first "state of the art", identifying opportunities and also the limitations surrounding the use of mobile phones in psychosocial therapies targeting the vulnerable. Issues concerning privacy and data confidentiality, and the access of vulnerable people to mobile phones and how they use them, pose significant challenges; but they offer the opportunity to reach isolated or impoverished populations, or even to facilitate access to social and healthcare services. We close this paper formulating possible orientations, hypotheses and goals to design new investigation works involving vulnerable populations.

A global approach for sparse representation of uncertainty in Life Cycle Assessments of waste management systems

Identification of key inputs and their effect on results from Life Cycle Assessment (LCA) models is fundamental. Because parameter importance varies greatly between cases due to the interaction of sensitivity and uncertainty, these features should never be defined a priori. However, exhaustive parametrical uncertainty analyses may potentially be complicated and demanding, both with analytical and sampling methods. Therefore, we propose a systematic method for selection of critical parameters based on a simplified analytical formulation that unifies the concepts of sensitivity and uncertainty in a Global Sensitivity Analysis (GSA) framework. The proposed analytical method based on the calculation of sensitivity coefficients (SC) is evaluated against Monte Carlo sampling on traditional uncertainty assessment procedures, both for individual parameters and for full parameter sets. Three full-scale waste management scenarios are modelled with the dedicated waste LCA model EASETECH and a full range of ILCD recommended impact categories. Common uncertainty ranges of 10 % are used for all parameters, which we assume to be normally distributed. The applicability of the concepts of additivity of variances and GSA is tested on results from both uncertainty propagation methods. Then, we examine the differences in discernibility analyses results carried out with varying numbers of sampling points and parameters. The proposed analytical method complies with the Monte Carlo results for all scenarios and impact categories, but offers substantially simpler mathematical formulation and shorter computation times. The coefficients of variation obtained with the analytical method and Monte Carlo differ only by 1 %, indicating that the analytical method provides a reliable representation of uncertainties and allows determination of whether a discernibility analysis is required. The additivity of variances and the GSA approach show that the uncertainty in results is determined by a limited set of important parameters. The results of the discernibility analysis based on these critical parameters vary only by 1 % from discernibility analyses based on the full set, but require significantly fewer Monte Carlo runs. The proposed method and GSA framework provide a fast and valuable approximation for uncertainty quantification. Uncertainty can be represented sparsely by contextually identifying important parameters in a systematic manner. The proposed method integrates with existing step-wise approaches for uncertainty analysis by introducing a global importance analysis before uncertainty propagation.

New variety breeding of Dioscorea alata, cultivar "Wenshanyao No.1"

To breed a new yam cultivar of Dioscorea alata, the different and excellent germplasm resources were investigated within artificially cultivated population and some superior individuals, with a higher yield and medicinal properties, were selected. Considering results of the yield and medicinal properties during 2006-2013 cropping season, strains and lines were established and selected. As a result, the yield of the new developed cultivar (Wenshanyao No. 1, WSY01-1) reached 2217. 0 kg per 667 m2 (fresh weight) and 348.3 kg per 667 m2 (dry weight), and increased 23.8% and 23.9% comparing with control cultivars (landraces). Comparing with control cultivars, the level of polysaccharide, allantoin, and dioscin increased 36.9%, 48.3%, 20.9%, and reached 12.2%, 1.30%, 579.7 µg · g(-1), respectively. This result showed that the systematic selection method can significantly improve yield and medicinal properties of D. alata, and the developed " Wenshanyao No. 1" exhibits wide spreading prospects.

Psychosocial and medical factors affecting treatment compliance in patients attending psychiatric hospital: a study from Kashmir

Background: Compliance with medication is decisive for treatment of the psychiatric disorders and is necessary for determining the outcome and prognoses of psychiatric patients. While the causes of poor compliance are multifactorial, the psychiatrist should be aware of such factors and may be able to implement interventions to address those factors. The objective of study was to find out the various medical and social reasons affecting treatment Compliance among patients suffering from psychiatric disorders. Methods: A Cross-Sectional study from 2011 to 2012 was conducted in IMHANS (Institute of Mental Health and Neurosciences) Srinagar (J&K), a questionnaire was designed, and the questionnaire included questions on socio-demographic variables, psychiatric illnesses, and Medical and psycho-social affecting treatment compliance. A systematic selection method for choosing the respondents was opted, questionnaire was administered on 200 (n=200) patients who attended the Outpatient department during the period. Simple random sampling method was applied for selection of respondents, the first time visitors to OPD were exclude along with repetition of respondents. Results: Out of 200 respondents studied in the study 41.5 % were males and 58.5% were females. Maximum number of patients (31.5%) studied were in the age group below the 30 years. 3.5% of respondents were in the age group above 70 years. Out of total 200 respondents in the study 74 % of the respondents are in compliance with recommended medicine whereas non-compliance was found in the 26% of studied population. Complications (13.46%) ascending out by usage of psychiatric medicine can be attributed as one of the major case of treatment non-compliance in psychiatric patients, among the psychiatric patients. Accessibility of psychiatric medicine and Financial constrain was also one of the reasons behind the medicine non-compliance (7.69%). Patients with no insight to psychiatric disease also include a good percentage of (5.76 %) of medicine non- compliance. Conclusions: Non-compliance is a dominant factor which causes possibly causes readmission in psychiatric wards. Compliance in psychiatric patients in general could be enhanced and improved by adequate intervention via patient counselling and patient medicinal care and education.

Polarization Matching in AlGaN-Based Multiple-Quantum-Well Deep Ultraviolet Laser Diodes on AlN Substrates Using Quaternary AlInGaN Barriers

A prototypical design of AlGaN deep ultraviolet (DUV) laser diodes (LDs) on AlN substrates employing tapered electron blocking layer is presented. Two-dimensional optoelectronic simulation predicts lasing at a target wavelength of 250 nm. Degradation of optical gain associated with spatial separation of electron and hole wave functions inside the active region may be considerably reduced in designs featuring quaternary AlInGaN barriers, by virtue of polarization charge matching. A systematic method for selection of polarization-free quaternary barrier compositions is proposed for 250 nm DUV LD designs, accompanied by a sensitivity analysis. The selection procedure presented here is readily applied to LDs and light-emitting diodes operating at other wavelengths.

Configuration selection in the MCSCF method. I. Application to the B1∑+ state of HF

Due to the relatively small number of configurations used in MC wavefunctions, proper selection of configurations is essential. Systematic selection methods, allowing for the anticipated changes in orbital characteristics, are developed for an excited state system, the B1∑+ state of HF. Configurations qualifying for inclusion into the MC wavefunction must have either a large coefficient in CI trial functions, or a large energy lowering relative to a few reference configurations. The MCSCF results for the B1∑+ state of HF compare well with CI results obtained here and obtained previously by other authors.

Application of sensor arrays based on thin films of conducting polymers for chemical recognition of volatile organic solvents

The use of thin conducting polymer films (polyaniline, polypyrrole and poly-3-methylthiophene doped with ordinary mineral acids or monovalent anions, as well as heteropolyacids of Keggin type or corresponding heteropolyanions) as sensitive layers in the sensor arrays for chemical recognition of volatile organic analytes is investigated. The discrimination ability of various sensor subsets is evaluated by statistical analysis of the data retrieved from responses of 20 different polymer sensors to the vapors of nine volatile organic solvents. An approach for constructing optimal sensor array is developed and the method for systematic selection of separate sensors from the given set to achieve the required analyte recognition reliability is proposed.

Selection Criteria for the Analysis of Data-Driven Clusters in Cerebral fMRI

Functional MRI (fMRI) may be possible without a priori models of the cerebral hemodynamic response. First, such data-driven fMRI requires that all cerebral territories with distinct patterns be identified. Second, a systematic selection method is necessary to prevent the subjective interpretation of the identified territories. This paper addresses the second point by proposing a novel method for the automated interpretation of identified territories in data-driven fMRI. Selection criteria are formulated using: 1) the temporal cross-correlation between each identified territory and the paradigm and 2) the spatial contiguity of the corresponding voxel map. Ten event-design fMRI data sets are analyzed with one prominent algorithm, fuzzy c-means clustering, before applying the selection criteria. For comparison, these data are also analyzed with an established, model-based method: statistical parametric mapping. Both methods produced similar results and identified potential activation in the expected territory of the sensorimotor cortex in all ten data sets. Moreover, the proposed method classified distinct territories in separate clusters. Selected clusters have a mean temporal correlation coefficient of 0.39+/-0.07 (n=19) with a mean 2.7+/-1.4 second response delay. At most, four separate contiguous territories were observed in 87% of these clusters. These results suggest that the proposed method may be effective for exploratory fMRI studies where the hemodynamic response is perturbed during cerebrovascular disease.

Future directions for in‐situ product removal (ISPR)

AbstractThis paper summarizes the main findings of a round‐table discussion held to examine the key bottlenecks in the further application and industrial implementation of in‐situ product removal (ISPR) techniques.** It is well established that ISPR can yield great benefits for processes limited by inhibitory or toxic products, as well as unstable products or reactions that are thermodynamically unfavorable. However, several issues for industrial implementation were revealed in the discussion. Most notably implementation will be dependent on (1) research into the appropriate process structure, (2) methods to achieve process robustness, (3) systematic selection methods for separation operations and (4) the nature of the product market. Here, these four issues will be discussed as a basis for future work in this area. Copyright © 2007 Society of Chemical Industry

A Systematic Method for Selection of Promising Serum Protein Biomarkers to Improve Prostate Cancer (PCa1) Detection

Immunoassays for prostate-specific antigen (PSA) are used routinely as an aid to detect prostate cancer (PCa). PSA permits early detection of PCa but lacks specificity (1)(2). In a typical screening population of African American men ≥40 years of age and other men ≥50 years of age, ∼4% of the men with a PSA concentration >4 μg/L, which is the upper reference limit for PSA, will have prostate cancer (1). Most of these cancers can be detected using PSA. At issue, however, is the comorbidity of men with benign prostatic hyperplasia whose age and PSA concentrations are within these ranges. For example, 50% of men >60 years of age have benign prostatic hyperplasia (3), and many will have PSA >4 μg/L. The low specificity of PSA measurement as a test for PCa necessitates many prostate biopsies, of which ∼75% are negative and theoretically unnecessary. Additional biomarkers may improve sensitivity and specificity of PSA. We theorized that bioinformatics and analysis of the existing scientific literature could identify promising protein biomarker candidates that, if combined with PSA, might improve clinical sensitivity and especially specificity. The work flow we used to select candidate biomarkers is diagramed in the flow chart in Fig. 1⇓ . We searched Medline and other relevant databases from Nerac Corporation (Tolland, CT) for publications that appeared in 1999–2004 with the terms prostate cancer and markers or biomarkers or early detection or aggressiveness or staging or grading. We also used prostate-specific genes, prostate-specific proteins, and several well-known institutions and investigators in the field of prostate cancer research as search terms. Once compiled, the titles and abstracts were read to find proteins or genes documented to be differentially regulated in tissue or serum or plasma PCa samples compared with normal or benign samples. A list of >2700 citations was …

Optimal Heck Cross‐Coupling Catalysis: A Pseudo‐Pharmaceutical Approach

AbstractThe problem of predicting the activity of ligands and solvents in homogeneous catalysis is addressed. A systematic selection method for ligands and solvents is presented, using a combination of stereoelectronic and electrotopological descriptors to describe the ligands and solvents in a set of 500 Heck reactions. Libraries of ‘virtual ligands’ and ‘virtual solvents’ are projected on the reaction space, and their reactivity is then predicted using multivariate models. The applications of this approach to combinatorial catalysis and reducing the amount of ‘garbage data’ are discussed.