Sort Of Model Research Articles

Surfactant chemistry and polymer choice affect single-wall carbon nanotube extraction conditions in aqueous two-polymer phase extraction

Quantitative determination of the effects of surfactant chemistry and polymer chain length on the concentration conditions necessary to yield extraction of specific single-wall carbon nanotube (SWCNT) species in an aqueous two-polymer phase extraction (ATPE) separation are reported. In particular, the effects of polyethylene glycol (PEG) chain length, surfactant ratios, and systematic structural variations of alkyl surfactants and bile salts on the surfactant ratios necessary for extraction were investigated using a recently reported fluorescence-based method. Alkyl surfactant tail length was observed to strongly affect the amount of surfactant necessary to cause PEG-phase extraction of nanotube species in ATPE, while variation in the anionic sulfate/sulfonate head group chemistry has less impact on the concentration necessary for extraction. Substitution of different bile salts results in different surfactant packings on the SWCNTs, with substitution greatly affecting the alkyl surfactant concentrations required for (n,m) extraction. Finally, distinct alkyl-to-bile surfactant ratios were found to extract specific (n,m) SWCNTs across the whole effective window of absolute concentrations, supporting the hypothesized competitive adsorption mechanism model of SWCNT sorting. Altogether, these results provide valuable insights into the underlying mechanisms behind ATPE-based SWCNT separations, towards further development and optimization of the ATPE method for SWCNT chirality and handedness sorting.

Credit granting sorting model for financial organizations

Considering the environment of risks and influences inherent in the decision-making process for credit-granting operations, it has become a matter of survival for financial organizations to seek to improve how they engage in effective decision-making to ensure that their returns on invested capital will meet the expectations established at the beginning of the process. A credit-granting sorting model for financial organizations is proposed. The model aggregates the perspectives of different decision-makers to support an organization in the process of credit analysis and, consequently, to improve its operationality. The decision-making model is based on the ELECTRE TRI-B multicriteria method. It sorts credit-granting proposals into three classes, namely credit approved, technical analysis, and credit rejected. The group decision emerges from the decision rules of the organization’s executive board. This new proposed approach to dealing with credit-granting results in the systematization of credit analysis, reduction of doubt among decision-makers, avoidance of the emergence of informal groups, reduction of conflicts within a financial organization, and external interferences.

Multi-element Analysis of Honey from Amhara Region-Ethiopia for Quality, Bioindicator of Environmental Pollution, and Geographical Origin Discrimination.

Honey is a widely utilized sweetener containing mainly sugars with many other minor ingredients such as metallic elements. The analysis aimed to develop a chemometric model for tracing the geographical origin, evaluating nutritional quality, assessing pollution effect, and searching for marker metals for the region's honey. Forty-seven honey samples were collected directly from the apiarists at seven administrative zones. The contents of 14 metals were analyzed using inductively coupled plasma optical emission spectrometry after standard sample digestion. The findings showed us the major elements ranged from 24.8 to 1996mg/kg of the honey sample with K > Ca > Na > Mg. The minimum and maximum values for the trace metals were 2.35mg/kg and 163mg/kg, respectively, in the order of Fe > Cr > Zn > Ni > Mn > Cu > Co. From this data, the region's honey has its own contribution as a source of major and trace elements. Furthermore, mean values for the toxic heavy metals were 0.57 to 1.85 for Pb, 1.03 to 1.21 for Cd, and 2.85 to 6.21 for As in mg/kg. Thus, the pollution level in the environment seems to be at an alarming rate. Using principal components analysis (PCA), the first four principal components explained 80.16% of the total variation. The region's honey was best classified into five major clusters using linear discriminant analysis (LDA) with an average discrimination power of 89.91%. The LDA sorting model was verified by the cross-validation method. The verification revealed that the model has 92.11% recognition power and 93.33% prediction ability.

Qualitative analysis of post-consumer and post-industrial waste via near-infrared, visual and induction identification with experimental sensor-based sorting setup

Sensor-based sorting in waste management is a method to separate valuable material or contaminants from a waste stream. Depending on the separation property different types of sensors are used. Separation properties and their corresponding sensors are e.g. molecular composition with near-infrared sensors, colour with visual spectroscopy or colour line scan cameras, or electric conductivity with electromagnetic sensors.The methods described in this paper deal with the development of sorting models for a specific near-infrared, a visual spectroscopy and an induction sensor. For near-infrared and visual spectroscopy software is required to create sorting models, while for induction only machine settings have to be adjusted and optimized for a specific sorting task. These sensors are installed in the experimental sensor-based sorting setup at the Chair of Waste Processing Technology and Waste Management located at the Montanuniversitaet Leoben. This sorting stand is a special designed machine for the university to make experiments on sensor-based sorting in lab scale. It can be used for a variety of waste streams depending on the grain size and the pre-conditioning for the sensor-based sorting machine. In detail the methods to create these sorting models are described and validated with plastic, glass and metal waste.•Near-infrared spectroscopy measures the molecular composition of near-infrared-active particles.•Visual spectroscopy measures the absorption of visible light by chemical compounds.•Induction sensors use induced currents to detect nearby metal objects.

Matching of small packages of traditional Chinese medicine based on improved RANSAC algorithm

The vision system model of small package sorting of traditional Chinese medicine was constructed, and the chessboard calibration method was used to calibrate the binocular camera. Surf algorithm and orb algorithm are analyzed by experiments, and orb algorithm with high real-time performance is used to extract feature points. The fast nearest neighbor FLANN algorithm is used to quickly match the feature points, and the matched feature points are screened according to Lowe’s algorithm and v-axis coordinate consistency principle, to solve the problem of false matching points. The method of selecting feature points based on matching quality ranking to calculate the homography matrix is designed, which solves the random sampling problem of the RANSAC algorithm and effectively shortens the time of feature matching.

A Labor Market Sorting Model of Hysteresis and Scarring

A Labor Market Sorting Model of Hysteresis and Scarring

DISTRIBUTIONAL AND HOUSING PRICE EFFECTS FROM PUBLIC TRANSIT INVESTMENT: EVIDENCE FROM VANCOUVER

AbstractWe estimate a residential sorting model using microdata to study an expansion of the Vancouver rapid transit network. Our results indicate that many aspects of household preferences were stable over time; however, preferences for transit access became more homogeneous with respect to income. Simulations show that these preference changes, along with endogenous housing appreciation and rising income levels in connected neighborhoods, result in larger benefits for higher‐income households. This distributional pattern is driven by the gains of households working in pre‐connected neighborhoods. In contrast, the benefits to those working in newly connected neighborhoods disproportionately favor lower‐income households.

Learning the parameters of an outranking-based sorting model with characteristic class profiles from large sets of assignment examples

We address the problem of learning the parameters of the outranking-based multiple criteria sorting model from large sets of assignment examples. We focus on a recently devised method called Electre TRI-rC, incorporating a single characteristic profile to describe each decision class. We introduce four algorithms aimed at the problem. They use different optimization techniques, including an evolutionary algorithm, linear programming combined with a genetic approach, simulated annealing, and a dedicated heuristic. We present the results of the experiments carried out on both artificial and real-world data sets. They reveal an impact of the comparison and veto thresholds, various sorting rules, and ensembles on the classification accuracy of the proposed algorithms. From a broader perspective, we contribute to cross-fertilizing the fields of Multiple Criteria Decision Aiding and Machine Learning for supporting real-world decision-making.

The use of web technologies in the transportation of medical waste generated during the treatment of COVID-19 on the territory of the Russian Federation

In this study, explores the use of web technologies as a way to ensure that medical waste transportation is undertaken in a proper and safe way. Insofar as an incorrect waste sorting is regarded as a source of an epidemiological threat within further transportation, is proposed a synthesis of a waste sorting model. The model is based on a natural science approach to managerial decision-making. The model enables to identify and prove violations concerning medical waste sorting. Described four interconnected components of the model: an initial stage, when vaccination processes are not taking place; the vaccination is completed and medical waste is sorted according to the rules; the vaccination is completed, but the rules of medical waste sorting are violated; all the problems related to medical waste sorting are solved, so its transportation is possible.

Expanding the collection portfolio of plastic packaging: Impact on quantity and quality of sorted plastic waste fractions

Expanding the curbside collection system for household plastic packaging could help in accomplishing the ambitious recycling targets set by the European Union. In this study, the effects of such expansion in terms of quality and quantity of sorted plastic waste fractions were investigated. By applying a validated sorting model, the flows of packages in Belgium from their use phase until the output of the sorting plant and the quantity and composition of sorted waste fractions were simulated. These data were subsequently used to calculate existing performance indicators, such as grade and recovery, as well as new indicators, such as the Simpson's Diversity Index (SDI), which is a measure for the compositional diversity of sorted waste fractions. The highest SDI was calculated for the fraction named ‘other films’, namely 82%, showing that this fraction comprised the most diverse polymer mixture. Results indicated that certain sorted fractions achieve high product grade levels, e.g. more than 99% for the PS rigid fraction, whereas others had significantly lower product grades of approximately 80% for the PE films. Material Flow Analysis showed that by expanding the collection portfolio the collection rate of plastic packaging increased from 33.6 to 64.4 m%, of which 77.3% is simulated to be separated in the targeted waste fractions. However, this will be insufficient to meet the recycling target of 50% by 2025 as in total only 49.8% of plastic packaging is sorted correctly. Hence, additional improvements in both recycling technologies and packaging design are needed to further increase plastic recycling rates.

Who Benefits From Brownfield Cleanup and Gentrification? Evidence From Chicago

This paper presents research on the distribution of economic benefits from brownfield cleanup and land development. There is growing concern that cleaning up blighted areas, including brownfields, can entrench inequality by disproportionately benefiting some demographic groups more than others. We look for evidence of disproportionate benefits by relating changes in move decisions to land use activity in Chicago using a heterogeneous sorting model. Our research produces two key insights: first, Black and Hispanic households benefit less than White households from brownfield cleanup and vacant land development. Second, owners appear to benefit more than renters from cleanup and development. Overall, these results provide evidence of differences associated with race and housing tenure in who benefits from local land use actions.

NeuriteNet: A convolutional neural network for assessing morphological parameters of neurite growth

BackgroundDuring development or regeneration, neurons extend processes (i.e., neurites) via mechanisms that can be readily analyzed in culture. However, defining the impact of a drug or genetic manipulation on such mechanisms can be challenging due to the complex arborization and heterogeneous patterns of neurite growth in vitro.New Method: NeuriteNet is a Convolutional Neural Network (CNN) sorting model that uses a novel adaptation of the XRAI saliency map overlay, which is a region-based attribution method. NeuriteNet compares neuronal populations based on differences in neurite growth patterns, sorts them into respective groups, and overlays a saliency map indicating which areas differentiated the image for the sorting procedure. ResultsIn this study, we demonstrate that NeuriteNet effectively sorts images corresponding to dissociated neurons into control and treatment groups according to known morphological differences. Furthermore, the saliency map overlay highlights the distinguishing features of the neuron when sorting the images into treatment groups. NeuriteNet also identifies novel morphological differences in neurons cultured from control and genetically modified mouse strains.Comparison with Existing Methods: Unlike other neurite analysis platforms, NeuriteNet does not require manual manipulations, such as segmentation of neurites prior to analysis, and is more accurate than experienced researchers for categorizing neurons according to their pattern of neurite growth. ConclusionsNeuriteNet can be used to effectively screen for morphological differences in a heterogeneous group of neurons and to provide feedback on the key features distinguishing those groups via the saliency map overlay.

Implementation Of Bubble Sort Algorithm On 2 Fruit Models Of Data Selection Using The Java Program Language

There are various kinds of data sorting methods that we know of which are the Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, Shell Sort, Heap Sort, and Radix Sort methods. All of these methods have advantages and disadvantages of each, whose use is determined based on needs. Each method has a different algorithm, where different algorithms affect the execution time. One interesting algorithm to be implemented on 2 variant models of data sorting is the Bubble Sort algorithm, the reason is that this algorithm has a fairly long and detailed process flow to produce an ordered data sequence from a previously unordered data sequence. Two (2) data sorting variant models that will be implemented using the Bubble Sort algorithm are: Ascending data sorting variants moving from left to right, and Descending data sorting variants moving from left to right. And the device used in implementing the Bubble Sort algorithm is the Java programming language.

Deep learning-based image classification for online multi-coal and multi-class sorting

Deep learning is an effective way to improve the classification accuracy of coal images for the machine vision-based coal sorting. However, the related research on deep learning-based mineral image classification has not systematically considered the models for multi-coal and multi-class sorting. Additionally, the universal CNNs model for multi-coal image classification has not been proposed. Given the above problems, combined with deep learning and transfer learning and based on VGG Net, Inception Net, and Res Net, this study builds four CNNs models with different depth and structure for multi-coal and multi-class image classification. Finally, we take anthracite, gas coal, coking coal as the research objects and propose a universal CNNs model suitable for multi-coal and multi-class sorting. Moreover, with the Channel Visualization map, Heatmap, Gard-CAM map, and Guided Backpropagation map, the operational processes of CNNs model in coal image recognition and classification are revealed, and the features that affect the classification weights are analyzed.

Development of a bulk ore sorting model for ore sortability assessment—Part II: Model validation and optimisation

A previous study demonstrated the theoretical development of a model for assessing bulk ore sortability. However, the model has not been validated with many cases. In this study, the developed model was validated using the production data from 232 draw points in a block cave copper mine. For each draw point, sorting results and mine economics were predicted using fractal modelling and compared with results based on actual data analysis. The statistical error was measured for the fractal model predictions of feed grade, mass yield, pre-concentrate grade, metal recovery, and NSR with and without bulk ore sorting. An optimised approach was developed using multifractal modelling; two sets of fractal parameters were determined to characterise the metal distribution in high- and low-grade ranges. The results showed that the multifractal model was more accurate than the fractal model in predicting the sorting results and assessing ore sortability.

Learning non-compensatory sorting models using efficient SAT/MaxSAT formulations

The Non-Compensatory Sorting model aims at assigning alternatives evaluated on multiple criteria to one of the predefined ordered categories. Computing parameters of the Non-Compensatory Sorting model compatible to a set of reference assignments is computationally demanding. To overcome this problem, two formulations based on Boolean satisfiability have recently been proposed to learn the parameters of the Non-Compensatory Sorting model from perfect preference information, i.e. when the set of reference assignments can be completely represented in the model. In this paper, two popular variants of the Non-Compensatory Sorting model are considered, the Non-Compensatory Sorting model with a unique profile and the Non-Compensatory Sorting model with a unique set of sufficient coalitions. For each variant, we start by extending the formulation based on a separation principle to the multiple category case. Moreover, we extend the two formulations to handle inconsistency in the preference information using the Maximum satisfiability problem language. A computational study is proposed to compare the efficiency of both formulations to learn the two Non-Compensatory Sorting models (with a unique profile and with a unique set of sufficient coalitions) from noiseless and noisy preference information.

Optimal Design of Electric Vehicle Parking Lot based on Energy Management Considering Hydrogen Storage System and Demand Side Management

Basically, a system is associated with uncertainty and it is the existence of such a factor which results in an uncertain planning and designing. In today's power system, these conditions are mostly reasoned through the uncertain performance of a number of parameters e.g. price, so the use of uncertainty modeling becomes necessary. In the present work, an optimization model, based on virus search colony, is used for the optimal performance of intelligent parking of electric vehicles in uncertainty situations caused by the price of the upstream grid in the demand response (DRP) program. It has been used to reduce the daily cost of performance by changing various parts of the load between peak and off-peak intervals. Virus Colony Search (VCS) method is a method based on the random-based population, which is also based on specific behaviors of the virus. Chaos theory has been used to develop this algorithm and improve local and global search. Moreover, the proposed multi-objective algorithm is a model based on non-dominated sorting model, variable detection, memory-based strategy selection and fuzzy theory to select the best Pareto from the answer set. Besides, it has a powerful function in solving the above problem. Regarding the mentioned techniques, the possibility of being in local points is reduced and the speed of convergence to the final response is increased. The proposed method was investigated on a sample system including, intelligent parking, local units, thermal and renewable plants in the proposed upstream grid price uncertainty technique. The obtained results show the efficiency of the suggested model. According to the compared results, under the demand response program, the average value of the smart parking cost is reduced about 4%.

Sorting based on urban heritage and income: Evidence from the Amsterdam metropolitan area

We study the relationship between cultural heritage and city structure in a residential sorting model. Contrary to previous research, we find that all income groups attach a large value to the proximity of urban heritage while there are important differences associated with other household characteristics. In line with previous research, we find that all households attach value to the share of high-income households in neighborhoods. Our approach covers both elements and we use the estimated version to investigate the overall impact of cultural heritage on within-city structure. We simulate the housing market equilibrium in a counterfactual situation without spatial differences in the presence of cultural heritage. The results suggest that without highly valued inner city amenities, households’ location choice in European cities, such as Amsterdam, would be much more similar to US cities.

Sorting and Recycling of Lightweight Packaging in Germany \u2014 Climate Impacts and Options for Increasing Circularity Using Tracer-Based-Sorting

This paper elaborates an energy and material flow model for conventional lightweight packaging sorting and recycling in Germany based on literature analysis and information provided by industry experts. The model is used to determine specific energy and material demands of particular lightweight packaging fractions and their respective climate impacts. Fluorescent-based identification techniques, tracer-based-sorting in particular, are assessed as an option to increase material circularity due to improved sorting and recycling accuracy. In comparison to primary production of lightweight packaging, conventional sorting and recycling saves 565 kg CO2-eq./Mg of input sorting. A total of four implementation scenarios are considered, reflecting the percentage of mixed plastics and residuals that can be mechanically processed through improved sorting (10%, 50%, 90% and 100%). Using tracer-based-sorting, these savings increase from 578 up to 1227 kg CO2-eq/Mg depending on implementation scenario. This paper concludes that tracer-based-sorting can contribute to an environmentally benign circular economy by yielding high-quality regranulates, which are capable of substituting more carbon-intensive primary production of lightweight packaging.

Short- and Long-Term Effects of Superhero Media on Young Children's Risk-Taking Behaviors.

Unintentional injuries, the leading cause of death for American children, are caused by a range of psychosocial factors, including risk behavior. One factor that may impact child risk-taking is modeling of superhuman risk-taking from superhero media, both immediately following superhero exposure and based on lifetime exposure and engagement. Fifty-nine 5-year-olds were randomly assigned to view either a 13-min age-appropriate superhero television show or a comparable nonsuperhero show. After the viewing, children engaged in three risk-taking measures: (a) activity room, unsupervised play for 5 min with assortment of apparently dangerous items that might encourage child risk-taking; (b) picture sort, 10 illustrations of children in risk situations, with participant response concerning intended risk-taking in that situation; and (c) vignettes, 10 stories presenting situations with varying degrees of risk, with participant response on intended choice. Parents completed questionnaires concerning children's long-term superhero media exposure and individual superhero engagement (e.g., if child's most recent Halloween costume was of a superhero). Correlations and regressions evaluated effects of immediate superhero exposure, lifetime superhero exposure, and lifetime superhero engagement on children's risk-taking. Mixed results emerged. Lifetime superhero exposure was significantly related to children's risk-taking outcomes in two bivariate (vignettes and picture sort) and one multivariate (picture sort) model. Neither immediate superhero exposure nor lifetime superhero engagement was strongly related to risk-taking. Children's lifetime superhero exposure may influence children's risk-taking. Given American children's substantial media exposure, research should continue to unpack the role of superhero media on children's unintentional injury and other health risk behaviors.