Sort Of Model Research Articles

The SORT model for surgical research collaboration and productivity

The SORT model for surgical research collaboration and productivity

A guided tour of multiple criteria sorting models and methods

A guided tour of multiple criteria sorting models and methods

Retrieving Estimates of the Storm‐Relative Wind Profile From the Vertical Variation of Hydrometeor Size Sorting Signatures

AbstractRaindrop fall speed increases with raindrop size. Because larger raindrops fall through a given layer of the atmosphere more quickly compared to smaller raindrops, they spend less time in the layer and, therefore, have less time to be acted on and advected by the storm‐relative winds. This results in hydrometeor size sorting which impacts the polarimetric radar variables such as differential reflectivity and specific differential phase . Previous work has shown a strong correlation between the mean storm‐relative wind in the sorting layer and the separation between regions of enhanced and at the bottom of the sorting layer. This study leverages this finding, along with a simple size sorting model, to construct radar‐derived estimates of the storm‐relative wind profile. The radar‐derived “pseudo‐hodographs” are well‐correlated with the magnitude and direction of the corresponding storm‐relative wind profiles. Further, these radar‐derived estimates of the storm‐relative winds are used to calculate estimates of shear and storm‐relative helicity, which also exhibit a strong correlation.

Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria

Deriving a representative model using value function-based methods from the perspective of preference disaggregation has emerged as a prominent and growing topic in multi-criteria sorting (MCS) problems. A noteworthy observation is that many existing approaches to learning a representative model for MCS problems traditionally assume the monotonicity of criteria, which may not always align with the complexities found in real-world MCS scenarios. Consequently, this paper proposes some approaches to learning a representative model for MCS problems with non-monotonic criteria through the integration of the threshold-based value-driven sorting procedure. To do so, we first define some transformation functions to map the marginal values and category thresholds into a UTA-like functional space. Subsequently, we construct constraint sets to model non-monotonic criteria in MCS problems and develop optimization models to check and rectify the inconsistency of the decision maker’s assignment example preference information. By simultaneously considering the complexity and discriminative power of the models, two distinct lexicographic optimization-based approaches are developed to derive a representative model for MCS problems with non-monotonic criteria. Eventually, we offer an illustrative example and conduct comprehensive simulation experiments to elaborate the feasibility and validity of the proposed approaches.

Deep learning approaches for classification of copper-containing metal scrap in recycling processes

Separating copper from iron scrap is a critical operation in metal recycling and achieving this with low cost sensoric equipment like RGB cameras instead of XRF/XRT is becoming increasingly attractive. In this article, the groundwork for creating an image classification model to separate copper from iron scrap has been performed. Twenty of the most common and most easily available CNN architectures were trained on 2200 metal scrap specimens and evaluated inline on a sensor-based sorting rig for their prediction accuracy and their inference latency to mimic real circumstances in an industrial setting. Out of these evaluated architectures, DenseNet-201 with 98% accuracy in inline tests is recommended if potent hardware is available. Otherwise AlexNet with 92% accuracy or MobileNet-V2 with 90% accuracy are recommended for further investigation and model creation if hardware restrictions apply. Based on the presented results in this article, the initial cumbersome surveyance of the most suitable network architecture can be substantially reduced and the creation of a sorting model can be streamlined. This article thus provides the basis for creating an inline applicable sorting method for scrap metal that uses low cost sensorics equipment and can provide reasonably high accuracy in its prediction.

Integrating machine learning models to learn potentially non-monotonic preferences for multi-criteria sorting from large-scale assignment examples

Learning preferences from assignment examples has attracted considerable attention in the field of multi-criteria sorting (MCS). However, traditional MCS methods, designed to infer decision makers’ preferences from small-scale assignment examples, encounter limitations when confronted with large-scale data sets. Additionally, the presence of decision makers’ non-monotonic preferences for certain criteria in MCS problems necessitates accounting for potential non-monotonicity when devising preference learning methods. To address this, this paper proposes some new models to learn potentially non-monotonic preferences for MCS problems from large-scale assignment examples by leveraging machine learning models. Specifically, we first introduce the Piecewise-Linear Neural Network (PLNN) model, which leverages the threshold-based value-driven sorting procedure as the underlying sorting model and integrates a perceptron-based model to establish piecewise-linear marginal value functions to approximate real ones. On this basis, we address MCS problems with criteria interactions and extend the PLNN model to develop the Piecewise-Linear Factorization Machine-based Neural Network (PLFMNN) model by incorporating the factorization machine to factorize interaction coefficients. Training these models allows us to learn potentially non-monotonic preferences of decision makers. To illustrate the proposed models, we apply them to a red wine quality classification problem. Furthermore, we assess the performance of the proposed models through computational experiments on both artificial and real-world data sets. Additionally, we conduct statistical tests to ascertain the significance of the performance differences. Experimental results reveal that the proposed models are comparable to the multilayer perceptron model and outperform other baseline models on most data sets, thus affirming their efficacy. Finally, we conduct some sensitivity analysis to assess the impact of certain parameters on the performance of the proposed models and compare them with existing studies from a theoretical perspective, further demonstrating their effectiveness.

A deep learning approach to improve signal quality: spike denoising for reliable sorting using transformer networks

Accurate sorting is critical in neural signal processing. This paper presents a spike denoising method using a transformer network for enhanced spike sorting. Accurate spike sorting involves identifying and isolating signals generated by individual neurons from recordings obtained from multiple neurons. A transformer is a deep learning model that uses self-attention to differentially weight the significance of each part of the input data. Transformer networks consist of two main parts: the Encoder and the Decoder. The Encoder is made up of encoding layers that iteratively map the input to a new space, while the Decoder contains decoding layers that generate a denoised signal from the output of the Encoder. The aim of this paper is to present a supervised sorting model that could isolate the raw spike waveforms to their fired neurons with higher accuracies, leveraging the attention mechanism in the proposed model. The developed approach utilizes the transformer network to increase the accuracy of detecting the main source of spikes. Additionally, this method extracts the main spike source in cases of overlapped spikes. To assess our model’s performance, we tested it on two public datasets. The first dataset was designed to evaluate the model’s ability to handle noise, while the second dataset presented challenges for spike classification, making it a suitable test for our model’s capabilities. Our model outperformed other existing models on both datasets. Our model achieved an average classification accuracy of 99.85% on the simulated dataset and 95.06% on the experimental dataset, outperforming other models. These results suggest that our model is well-suited for handling noise and difficult spike classification scenarios and is superior to other currently available models.

The technological origins of the decline in labor market dynamism

In the last decades, there has been a marked decline in the job flows to and from unemployment and between employment. We ask whether and how technological change can account for his secular decline in labor market dynamism. We propose a theory that focuses on the determinants of technology broadly defined: 1. the complementarity between worker skill and firm productivity; and 2. the volatility in productivity shocks; and 3. search frictions. We derive job flows in a sorting model with search frictions and endogenous search effort both on and off the job, as well as shocks that lead to mismatch. We quantify our model using the US data and find an increase in the complementarity between labor and technology, a decline in the frequency and volatility of productivity shocks, and a decline in the match efficiency as well as an increase in the search costs. The changing nature of these features of the technology contributes to the secular decline in labor market dynamism.

Validation of a PGNAA sensor model for bulk material sorting

Global mineral demand is forecast to increase significantly to achieve the transition to renewable energy. Greater volumes of ore of lower grade will have to be mined to meet demand. Techniques to process large volumes of low-grade ore efficiently are being investigated to reduce the cost and impact of mining. One technique is to use sensor information to sort mined material, allowing waste to be discarded early in mineral processing. Prompt gamma neutron activation analysis (PGNAA) is a sensing technique that can provide information on the multi-elemental composition of a bulk sample which can be used for bulk material sorting. This paper presents the development of a Monte Carlo simulation model of a PGNAA sensor for bulk sorting using the Geant4 toolkit. The GEOSCAN sensor (Scantech Australia) was used as a case-study to demonstrate the application of the model. The sensor responses for a range of pure mineral samples (Fe2O3, SiO2, S, Na2CO3 and MnO2) were measured to validate the developed model. The sensitivity of the simulation results to the hadronic and electromagnetic physics models used was tested. It was determined that the PGNAA sensor model can reproduce measurements obtained from the GEOSCAN sensor. In particular, the model can provide a good reproduction of the overall spectral shape and the locations of distinct characteristic peaks. The differences between simulated and experimental results are within 30% on average. It was found that the Geant4 HP neutron model best reproduces the activation peaks observed in experimental measurements. Additionally, the PGNAA spectrum was found to be insensitive to the choice of electromagnetic model for the photon interactions. The validated sensor model provides a useful tool for investigating PGNAA sensor applications including a bulk sorting strategy for new materials, sensor calibration, improvements in signal analysis and optimised sensor design.

Tiebout sorting in online communities

This paper proposes a stylized, dynamic model to address the issue of sorting online. There are two large homogeneous groups of individuals. Everyone must choose between two online platforms, one of which has superior amenities (akin to having superior local public goods). Each individual enjoys interacting online with those from their own group but dislikes being on the same platform as those in the other group. Unlike a Tiebout model of residential sorting, both platforms have unlimited capacity so there are no constraints on cross-platform migration. It is clear how each group would like to sort themselves but, in the presence of the other type, only the two segregated outcomes are guaranteed to be equilibria. Integration on a platform can be supported in equilibrium as long as the platform is sufficiently desirable. If online integration of the two communities is a desired social outcome, then the optimal policy is clear: make the preferred platform even more desirable. Revitalizing the inferior platform will never lead to integration and even increases the likelihood of segregation. Finally, integration is more elastic in response to an increase in platform amenities than to reductions in intolerance.

Conceptual design and optimization of integrating renewable energy sources with hydrogen energy storage capabilities

Systems are inherently tied to uncertainty, which necessitates the development of designs and schedules that accommodate this unpredictability. Particularly in modern power systems, the variable efficiency of numerous factors, including pricing, underscores the need for uncertainty modeling. In the meantime, the incorporation of EVs (electric vehicles) into the electrical grid is advancing, with these vehicles being recognized for their role in reducing dependency on fossil energies and enhancing the resilience, stability and efficiency of the grid. However, the rapid growth of the EV sector necessitates thoughtful strategies from decision-makers to manage its expansion effectively, particularly considering the environmental implications associated with the life cycle of EVs, including their charging and discharging processes. This research introduces the conceptual design and optimization approach utilizing a hybrid bat algorithm and differential evolution algorithm (BA-DEA) to improve the efficiency and resilience of EV-smart parking lots under the unpredictable conditions caused by grid pricing fluctuations within the DRP (demand response program). The technique effectively modulates daily costs by adjusting loads among peak and off-peak periods. Key features of the proposed approach include a non-dominated sorting model, innovative variable identification, a memory-based selection process, and the application of fuzzy theory to identify optimal Pareto outcomes. This methodology is not only swift in converging to a solution but also demonstrates a high likelihood of reaching the global optimum. Modeling considerations for smart hydrogen storage systems (SHSSs) incorporate significant constraints, notably those associated with electrolyzers, fuel cells, and storage capacities. The algorithm's effectiveness is validated in scenarios involving parking facilities and multiple uncertain resources, demonstrating a robust reduction in specific cost indicators and adjustments in cost dynamics when DRP is considered. Specifically, the algorithm achieves a substantial 42% decrease in cost variability when DRP is excluded, and a 46.9% reduction in cost variability when DRP is included, despite a minor 5% increase in average cost. These outcomes underscore the proposed system's capability to improve resilience and thermal performance under fluctuating conditions, making it a promising solution for future smart energy systems. Further, the results exhibited the complex interplay between cost optimization and operational adjustments in response to demand-side management.

Setting the morphologic quality limits enabling accurate classification of charred archaeological grape seeds

This study investigates the morphological changes in grape pips resulting from various charring conditions. Employing high-resolution scanning combined with morphometric measurements for morphological analysis, we aimed to understand the effects of charring on grape pips. Our morphometric analysis demonstrated significant alterations in seed shape above 250 °C. The length–width ratio and the occurrence of cracks notably changed, providing a basis for assessing charring conditions. In addition, applying a machine learning classification method, we determined that accurate classification of grape varieties by the morphometric analysis method is feasible for seeds charred at up to 250 °C and 8 h. Integrating the morphometric changes and temperature ranges suitable for classification, we developed a sorting model for archaeological seeds. By projecting length–width ratios onto a curve calculated from controlled conditions, we estimated charring temperatures. Approximately 50% of archaeological seeds deviated from the model, indicating drastic charring conditions. This sorting model facilitates a stringent selection of seeds fit for classification, enhancing the accuracy of our machine learning-based methodology. In conclusion, combining machine learning with morphometric sorting enables the identification of charred grape seeds suitable for identification by the morphometric method. This comprehensive approach provides a valuable tool for future research for the identification of charred grape seeds found in archaeological contexts, enhancing our understanding of ancient viticulture practices and grape cultivation.

FRICTIONAL SORTING: THE IMPACTS OF DUAL CONSTRAINTS ON MOBILITY AND HOUSING SUPPLY IN CHINA

AbstractUsing an equilibrium sorting model and microdata from China, we evaluate the impacts of dual constraints on mobility and housing supply on workers’ sorting behavior, quantifying the welfare and distributional consequences. Counterfactual simulations show that lowering migration costs associated with institutional restrictions and migrant‐specific amenities would increase welfare and reduce inequality by moving workers from inland to coastal regions in China. These impacts depend on the housing supply elasticity in coastal regions. Similarly, the impacts of relaxing housing supply restrictions depend on mobility constraints. Results highlight the policy complementarities between reducing the two kinds of frictions.

Decision-Making Model in Information Technology Management of Logistics Companies

The research is devoted to developing an innovative decision-making model for automating the sorting processes of postal item sorting. In today’s environment of rapid development of logistics services and growing volumes of mail, there is a need to improve existing technologies and methods of managing sorting lines. The main goal of this study is to create an effective model that will ensure the optimisation of sorting terminals with loading points. The purpose of the research is to develop decision-making models for managing postal service lines that implement sorting logic for terminals with loading points. The research methodology is based on a systematic approach that allows us to take into account all aspects of sorting lines. The theories of finite predicate algebra and set algebra were used to create mathematical sorting models. Methods of building decision support systems and modern information technologies ensure the integration of the developed algorithms into real postal systems. The scientific novelty lies in defining and implementing a variant of the decision-making model for an automated sorting line for mail. The model implements the specified sorting logic, which makes it possible to significantly improve the efficiency and accuracy of the processes of loading and sorting mail. Conclusions. A variant of implementing the model of the decision support system for an automated sorting line of mail is proposed. The developed model makes it possible to implement the logic of sorting mail for terminals with defined loading points. This helps to increase the efficiency and reliability of postal services, reducing the likelihood of errors and ensuring timely delivery.

Urban heat and within-city residential sorting

This study presents new causal evidence on how urban heat contributes to sorting within a city. We estimate a discrete choice residential sorting model that includes census-tract fixed effects and controls for open space and green coverage to analyze how differences in urban heat at the census-tract level influence the location choices of New York City homeowners given their race, ethnicity, and income. Our results show clear patterns of residential sorting, with whites and high-income households outcompeting other racial/ethnic groups and low-income households for housing in cooler neighborhoods. Our counterfactual exercise, inspired by Cool Neighborhoods NYC, reveals that heat-mitigation policies can make poorer and minority households, on average, worse off. These findings are striking, considering that such programs often aim to enhance welfare in heat-exposed neighborhoods predominantly inhabited by low-income and minority households.

Depth‐Partitioning of Particulate Organic Carbon Composition in the Rising and Falling Stages of the Amazon River

AbstractThe Amazon River mobilizes organic carbon across one of the world's largest terrestrial carbon reservoirs. Quantifying the sources of particulate organic carbon (POC) to this flux is typically challenging in large systems such as the Amazon River due to hydrodynamic sorting of sediments. Here, we analyze the composition of POC collected from multiple total suspended sediment (TSS) profiles in the mainstem at Óbidos, and surface samples from the Madeira, Solimões and Tapajós Rivers. As hypothesized, TSS and POC concentrations in the mainstem increased with depth and fit well to Rouse models for sediment sorting by grain size. Coupling these profiles with Acoustic Doppler Current Profiler discharge data, we estimate a large decrease in POC flux (from 540 to 370 kg per second) between the rising and falling stages of the Amazon River mainstem. The C/N ratio and stable and radiocarbon signatures of bulk POC are less variable within the cross‐section at Óbidos and suggest that riverine POC in the Amazon River is predominantly soil‐derived. However, smaller shifts in these compositional metrics with depth, including leaf wax n‐alkanes and fatty acids, are consistent with the perspective that deeper and larger particles carry fresher, less degraded organic matter sources (i.e., vegetation debris) through the mainstem. Overall, our cross‐sectional surveys at Óbidos highlight the importance of depth‐specific sampling for estimating riverine export fluxes. At the same time, they imply that this approach to sampling is perhaps less essential with respect to characterizing the composition of POC sources exported by the river.

Predicting Risk of Post-Operative Morbidity and Mortality following Gynaecological Oncology Surgery (PROMEGO): A Global Gynaecological Oncology Surgical Outcomes Collaborative Led Study.

The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models (p < 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien-Dindo I-II), major morbidity (Clavien-Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61, p < 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities.

Premature agglomeration?: Two phases of development with spatial sorting

AbstractClusters in the developing world do, to a large extent, attract low‐educated individuals, and these clusters are in some cases, characterized by urbanization without industrialization. This contrasts starkly to clusters in advanced economies that attract high‐skilled individuals and entrepreneurs. In this paper, we develop a model of agglomeration and spatial sorting that is consistent with these two types of different agglomeration processes in developed and developing countries. We show that a poor country that has an agglomeration with low skilled individuals, may get stuck in this equilibrium, but that free mobility of human capital from the outset nevertheless is superior from the perspective of total social welfare.

Research on joint scheduling method of order grading and machine maintenance

AbstractIn the multi‐variety and large‐scale order production mode, enterprises must balance delivery deadlines and maintain customer satisfaction while also considering the health status of machines. Therefore, the authors propose a method for jointly optimising production scheduling and machine maintenance. Before machine processing, an order value grading and sorting model and a machine health‐status group partitioning model are constructed to classify orders into different production value levels and machines into different health‐status groups, respectively. During machine processing, based on the Weibull distribution theory, a ‘health evaluation function value’ constraint machine preventive maintenance (PM) model and PM strategy are proposed to account for the changing health status of machines; these are integrated with the order allocation machine strategy as decision‐making elements in the production schedule. Finally, two case studies are used to verify the effectiveness of this proposed model and method. The results show that compared to general scheduling schemes, the proposed method can reduce total delay and improve customer satisfaction. Additionally, the PM plan proposed in this method can improve production efficiency and line stability compared to periodic maintenance.

Enhancing resource allocation in drought mitigation through a group multicriteria sorting model: a case study of Rio Grande do Norte, Brazil

ABSTRACT Drought is a natural phenomenon that poses a significant threat to water resources in affected regions. The detrimental effects of this extreme weather event, regardless of its type, have had an impact, necessitating concrete resource allocation projects to mitigate drought. From this perspective, our research proposes a group multicriteria sorting model to support resource allocation for drought mitigation. A multicriteria sorting model was developed based on the PROMSORT method to assist the government in evaluating 14 municipalities of the Apodi-Mossoró river basin in the Rio Grande do Norte State in Brazil. This model classifies the municipalities into high, moderate, and low-priority categories, enabling targeted attention and allocation of resources for drought mitigation efforts. The research findings demonstrate that the proposed model can effectively support strategic public policies, allocate resources, and facilitate the implementation of appropriate actions, thereby focusing efforts on the cities most severely affected by drought and alleviating the adverse effects of this natural disaster