Matching Scheme Research Articles

Fairness in online vehicle-cargo matching: An intuitionistic fuzzy set theory and tripartite evolutionary game approach

This paper explores the concept of fairness and equitable matching in an on-line vehicle-cargo matching setting, addressing the varying degrees of satisfaction experienced by shippers and carriers. Relevant indicators for shippers and carriers in the on-line matching process are categorized as attributes, expectations, and reliability, which are subsequent quantified to form satisfaction indicators. Employing the intuitionistic fuzzy set theory, we devise a transformed vehicle-cargo matching optimization model by combining the fuzzy set’s membership, non-membership, and uncertainty information. Through an adaptive interactive algorithm, the matching scheme with fairness concerns is solved using CPLEX. The effectiveness of the proposed matching mechanism in securing high levels of satisfaction is established by comparison with three benchmark methods. To further investigate the impact of considering fairness in vehicle-cargo matching, a shipper-carrier-platform tripartite evolutionary game framework is developed under the waiting response time cost (WRTC) sharing mechanism. Simulation results show that with fairness concerns in vehicle-cargo matching, all stakeholders are better off: The platform achieves positive revenue growth, and shippers and carriers receive positive subsidy. This study offers both theoretical insights and practical guidance for the long-term and stable operation of the on-line freight stowage industry.

Offshore carbon storage from power plants based on real option and multi‐period source‐sink matching: A case study in the eastern coastal China

Carbon capture utilization and storage (CCUS) emerges as a pivotal strategy for CO2 reduction in the power sector, particularly focusing on the overlooked domain of offshore storage along China's east coast. In spite of the potential high costs, irreversible investments, and lengthy development, offshore storage can still be prospective. Considering autonomous decision-making among emission sources, this study pioneers a CO2 offshore storage investment decision model tailored for coal-fired and gas-fired power plants. Innovating an offshore storage source-sink matching model with a real options model and introducing a pipeline network optimization model allows a realistic source-sink matching strategy to be explored under optimal investment timing. According to the results, among 154 large stationary emission sources in Zhejiang Province, offshore storage could reduce CO2 emissions by 4.59 Gt, utilizing the Qiantang, Minjiang, and Fuzhou depressions. It is economically feasible to implement offshore storage with a whole-process unit cost of 368.8 CNY/tCO2, mainly dominated by capture costs. A hybrid carbon tax-subsidy policy promotes carbon reduction and economic benefits, offering a more effective incentive for emission sources to invest in offshore storage than a single policy. At a hybrid policy price of 250 CNY/tCO2, all 27 selected emission sources are projected to invest in offshore storage by 2048, with a preference for the Qiantang depression as the storage site. Practically, this study provides important technical support and guidance for the large-scale deployment of offshore storage.

A collaborative-learning multi-agent reinforcement learning method for distributed hybrid flow shop scheduling problem

As the increasing level of implementation of artificial intelligence technology in solving complex engineering optimization problems, various learning mechanisms, including deep learning (DL) and reinforcement learning (RL), have been developed for manufacturing scheduling. In this paper, a collaborative-learning multi-agent RL method (CL-MARL) is proposed for solving distributed hybrid flow-shop scheduling problem (DHFSP), minimizing both makespan and total energy consumption. First, the DHFSP is formulated as the Markov decision process, the features of machines and jobs are represented as state and observation matrixes according to their characteristics, the candidate operation set is used as action space, and a reward mechanism is designed based on the machine utilization. Next, a set of critic networks and actor networks, consist of recurrent neural networks and fully connected networks, are employed to map the states and observations into the output values. Then, a novel distance matching strategy is designed for each agent to select the most appropriate action at each scheduling step. Finally, the proposed CL-MARL model is trained through multi-agent deep deterministic policy gradient algorithm in collaborative-learning manner. The numerical results prove the effectiveness of the proposed multi-agent system, and the comparisons with existing algorithms demonstrate the high-potential of CL-MARL in solving DHFSP.

Red cell specifications for blood group matching in patients with haemoglobinopathies: An updated systematic review and clinical practice guideline from the International Collaboration for Transfusion Medicine Guidelines.

Red blood cell (RBC) antigen matching beyond ABO and RhD is commonly recommended for patients with sickle cell disease (SCD) and thalassaemia. We present an updated systematic literature review to inform evidence-based guidelines on RBC matching. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool was used to develop recommendations. Six new observational studies (4 prospective, 2 retrospective) were identified. The six studies reported on 583 patients in total, including cross-over designs, with sample sizes from 10 to 343. Studies were heterogeneous, utilising varying degrees of RBC matching and different definitions for 'extended' matching. All reported on alloimmunisation. One study reported on molecular matching. The reported prevalence of alloimmunisation using limited matching was 0%-50% and with extended matching was 0%-24%. Eighty-two patients were alloimmunised before study entry. The risk of bias across studies was moderate to critical. The guideline panel recommends that ABO, RhDCcEe, and K-compatible RBCs are selected for individuals with SCD and thalassaemia, even in the absence of alloantibodies, and that RBCs which are antigen-negative to already existing clinically significant antibodies are chosen. There is a need for comparative research to define the benefit, impact, cost-effectiveness, and feasibility of extended RBC matching strategies to prevent alloimmunisation.

Comparisons of tactical and technical performance factors between winners and non-winners in Para Taekwondo: A comprehensive analysis of the 2020 Tokyo Paralympic Games

ABSTRACT This study aims to analyse the differences in tactical and technical elements between winners and non-winners and identify key strategic and technical elements contributing to performance in Para Taekwondo. Notational analysis was used to achieve these objectives, and 78 Para Taekwondo matches (36 men’s matches and 42 women’s matches) from the 2020 Tokyo Paralympics were analysed. The recorded videos were analysed using Adobe Premiere Pro software (version 24.2) with a frame-by-frame analysis of 0.033 seconds. The main results are: a) winners had significantly higher kick-to-score ratios compared to non-winners (male: 38.3 ± 17.37% vs. 19.4 ± 11.95%, p < .001; female: 32.7 ± 18.17% vs. 11.0 ± 7.72%, p < .001); b) winners had fewer attack attempts but higher scoring efficiency; c) non-winners used the front leg more frequently (male: 43.2% vs. 39.7%, p < .05; female: 47.5% vs. 42.7%, p < .001); d) logistic regression analysis showed that kick-to-score ratio significantly impacted match outcomes for both male (OR = 1.10, p < .001) and female athletes (OR = 1.19, p < .001), while offensive frequency negatively impacted female athletes’ match outcomes (OR = 0.89, p = .002). This study emphasizes the importance of scoring efficiency and strategic defensive actions, consistent with patterns in Olympic Taekwondo. It provides insights for enhancing athlete training and match strategy in combat sports, enriching the understanding of Para Taekwondo.

Simultaneous measurement of temperature and refractive index based on fiber optic Fabry–Pérot cavity in batteries

Abstract A Fabry-Pérot (FP) sensor for simultaneous measurement of refractive index and temperature of an all-fiber synchronous liquid based on the Vernier effect is proposed, special application for internal monitoring of lithium-ion battery electrolytes. The theoretical model of high sensitivity cascade Fabry-Pérot interferometer (PFPI) is established, the sensitivity amplification mechanism of PFPI is analyzed in detail, the perfect matching scheme for measuring the refractive index of the electrolyte inside the battery is designed according to the matching conditions of the optical path, and the corresponding sensors for simultaneous measurement of the temperature and the refractive index are fabricated. The experimental results show that the sensor has a maximum refractive index sensitivity of 15391.5033 nm/RIU, a good linear fit, and can effectively monitor the refractive index change of the electrolyte by 10-4 orders of magnitude during normal charging and discharging of lithium-ion batteries. Compared to traditional epoxy adhesive bonded or laser processed FP sensors, this all-fibre optic structure not only improves temperature and corrosion resistance, but also significantly reduces temperature cross-sensitivity. Its small size and excellent resistance to high temperature and corrosion ensure the sensor's excellent response and broad application prospects in monitoring the refractive index of the electrolyte in 18650 lithium-ion batteries, which provides effective technical support for monitoring the health status of batteries and detecting early safety issues.

Design and matching control strategy of electro-hydraulic load-sensitive hydraulic power unit for legged robots

A hydraulic power unit is the core energy source of hydraulic legged robots, ensuring the robot's maneuverability and carrying capacity in complex terrains. However, its pressure and flow output are usually set to a fixed level under all working conditions, which leads to mismatched pressure and flow requirements of hydraulic drive units. This mismatch reduces the stability and control accuracy of the hydraulic drive units and results in significant energy waste. To address these issues, this paper proposes an electro-hydraulic load-sensitive hydraulic power unit and a matching control strategy to better align outputs with requirements, thereby enhancing energy efficiency. Firstly, with the variable speed servo motor driving axial piston pump as the core, the closed system working principle of an electro-hydraulic load-sensitive hydraulic power unit is proposed, and the mathematical model of key components exhibiting strong nonlinearity and time-varying parameters is derived. After that, a matching control strategy based on flow feedforward and pressure difference compensation feedback is proposed. Finally, simulations and experiments are conducted to verify the feasibility of the unit and its matching control strategy. This paper provides a new solution for the design and control of hydraulic power units, laying the groundwork for achieving high-performance hydraulic systems.

Scene graph fusion and negative sample generation strategy for image-text matching

Scene graph fusion and negative sample generation strategy for image-text matching

Linking household members and defining relational networks using routine health data.

The growth of rich electronic health record (EHR) data and large health databases has introduced new opportunities to link individuals together into households and relational networks. These 'linked relational networks' hold promise for providing family-level care and studying intergenerational epidemiology and clinical outcomes. However, as linked relational networks become more commonly available in EHRs and research databases, it is critical to understand their challenges and limitations. Matching algorithms are being used to create linked relational networks in EHR and health databases. Clinically, these algorithms have been most useful to provide dyadic maternal-newborn care. In research, studies using these algorithms investigate topics ranging from the pharmacoepidemiology of parental drug exposure on childhood health outcomes, to heritability of chronic conditions, to associations between parental and child healthcare access and service delivery. However, ethical and technical challenges continue to limit use of these algorithms. There is also a critical research gap in the external validity of these matching algorithms. Linked relational networks are in widespread use in pediatric clinical care and research. More research is needed to understand the scope, limitations, and biases inherent in existing matching strategies.

The power of one: A single flanker produces compatibility effects in the episodic flanker task

AbstractThe episodic flanker task is an episodic version of the Eriksen and Eriksen (Perception &amp; Psychophysics, 16 (1), 143–149, 1974) perceptual flanker task, showing the same compatibility and distance effects. Subjects are presented with a list followed by a probe display in which one item is cued. The task, to indicate whether the probed letter appeared in the same position in the memory list, requires focusing attention on a single item in memory. The probe display contains flanking items to be ignored. They are same as the memory list or different. Same flankers are compatible with “yes” responses and incompatible with “no” responses. Different flankers are incompatible with “yes” responses and compatible with “no” responses. Previously, we presented multiple flankers in the probe, allowing a global matching strategy. Here, we report two episodic flanker experiments with just one flanker in the probe to encourage focusing sharply on the target. We found flanker compatibility effects in both experiments when a single flanker appeared immediately adjacent to the target. Experiment 2 varied the distance between the flanker and the target in the probe and the memory list and found the compatibility effect in response time only when the flanker was immediately adjacent to the target in both the probe and the memory list. The effect in accuracy also appeared when the flanker was two positions away in both the probe and the memory list. These results show that attention is focused sharply on elements of a memory structure during retrieval, suggesting that memory retrieval is perceptual attention turned inward.

Quantifying Tennis Players’ Performance and Analyzing Momentum Fluctuations Based on Weighted Logistic Regression Model

This paper proposes a dual-model approach to analyze tennis players’ performance and predict match outcomes, which combines a weighted logistic regression model and a decision tree model. By examining key factors such as scoring rate and serving status, the models calculate the scoring probabilities at different time points, quantifying the performance of players throughout the match. Furthermore, the paper constructs a decision tree model to predict shifts in the match situation based on momentum fluctuations. The research findings underscore the importance of momentum in determining match outcomes and provide valuable insights for training and match strategies. This dual-model approach offers a fresh perspective on the impact of momentum in tennis matches, allowing players to better understand and adapt to the dynamics of momentum fluctuations throughout a match. Additionally, the study suggests targeted training, simulated match conditions, psychological training, real-time data tracking, and physical preparation as strategies to enhance performance and adapt to momentum changes. This comprehensive approach provides players with valuable insights to optimize their performance and strategies in tennis matches.

Research on match fluctuation prediction and strategy based on OFS algorithm

The aim of this paper is to construct a model for predicting on-field fluctuations in matches by using OFS algorithm, combined with random forest and lattice point search algorithms, and to analyse the key factors in order to improve the coach's decision-making ability in matches. Cross-validation and hyper-parameter tuning techniques were used during the model building process to find the best hyper-parameter combinations through lattice point search to optimise the model performance and generalisation ability. The study provides a detailed description of the model implementation process, including data processing, feature selection, parameter setting, model building, training and evaluation. Meanwhile, a series of targeted recommendations are proposed to address the variability of match fluctuations, including the analysis of past matches, the focus on recent performance, the assessment of psychological quality, the study of opponent's weaknesses, the flexible response to changes in matches and the maintenance of focus, with a view to helping athletes and coaches to formulate a more effective match strategy and to achieve better match results.

Induced magnetic field and thermal regulation of synovial fluid flow through irregular surfaces with first-order slip and gold nanoparticles

ABSTRACT The current computational exploration develops a poroelastic model of the knee cartilage to elevate its temperature under cyclic sinusoidal loading. A contributing factor to the rise in temperature is this tissue’s viscous dissipation, which converts some of the mechanical energy supplied into heat. The manipulation of cartilage temperature is mostly reliant on the movement of synovial fluid inside the space between joints and within the permeable cartilage. We developed the region flow model in the presence of induced magnetic fields and gold nanoparticles, which enhanced the efficiency of joint lubrication by boosting the load-carrying capacity. We dealt with the problem for two different models. Model 1 assumes that viscosity is exponentially dependent on concentration. The shear thinning index in Model 2 is regarded as a concentration-dependent variable. We introduce these models for a complex wavy surface, where the first-order slip effect occurs. We modified the governing problem by assuming a long wavelength and a low Reynolds number. We followed up with a computational analysis utilizing the Rung–Kutta–Merson approach with Newton iteration in a shooting and matching strategy. We have conducted detailed comparisons between Model 1 and Model 2. The graphical findings have been shown for the distributions of velocity, temperature, and nanoparticle concentration. Additionally, the pressure gradients, streamlines, and axially generated magnetic fields have also been included. These results are presented for numerous physical parameters. The mechanics of trapping are thoroughly examined through the use of streamlines.

Modeling a ride-sourcing market with a third-party platform integrator under batch matching mechanisms

This paper develops a mathematical model to characterize a generalized ride-sourcing market with a third-party integrator and multiple competitive ride-sourcing platforms. Different from earlier works which normally assume instant matching strategies, our model can characterize the integrator’s and platforms’ decisions on choosing flexible matching strategies under batch matching mechanisms and their influences on the market equilibrium. Through theoretical analytics and numerical studies, this study reveals that the introduction of a third-party platform integrator is not always beneficial but may be detrimental to the system in certain situations. For example, an inefficient matching strategy chosen by the integrator may lead to a lose-lose situation where both the customers hailing rides through the integrator or the individual platforms experience longer waiting times. In addition, we show that a ride-sourcing platform with a high market share may refuse to join the integrator since the efficiency gains brought by the integrator do not outweigh the losses caused by its losing market share. The managerial insights obtained from this work can assist both the integrator and individual ride-sourcing platforms in developing more efficient operational strategies in terms of pricing and matching strategies.

Online bilateral matching methodology for anti-epidemic resources based on spatial transmission risk

This paper focuses on the online anti-epidemic resource allocation between demanders and suppliers considering the epidemic spatial spread. The spatial crowdsourcing with sharing platform is an effective way for anti-epidemic resource allocation, and a reasonable online matching strategy can improve the efficiency of resource utilization. The paper proposes online matching heuristic strategy (HSTRF-LRLUF strategy) and designs an online batch bilateral matching algorithm for anti-epidemic resources, which considers the impact of grid spatial aggregation and diffusion risk of emerging infectious diseases. The population distribution within grids and the commuting patterns between grids can provide decision support for selecting online matching strategies of anti-epidemic resources. A larger matching time window focusing on the spatial transmission risk (TR) of the epidemic can obtain better matching results. However, with a smaller matching time window, the decision makers can focus on spatial agglomeration risk (OR) or spatial diffusion risk (IR). The paper effectively combines the spatial crowdsourcing model with the anti-epidemic resource allocation to achieve the allocation of emergency resources to individuals. A combined anti-epidemic resource online matching heuristic strategies is designed from the spatial agglomeration risk and the spatial diffusion risk. Decision makers can dynamically adjust the online matching strategies of anti-epidemic resources by evaluating the spatial agglomeration risk, the spatial diffusion risk, and the overall spatial transmission risk based on the real-time spread of the epidemic and the supply of anti-epidemic resources.

An adaptive synthetic method for long sequence radar mode recognition

AbstractRadar work mode recognition is crucial to analyse radar behaviour and intention. There are some challenges limiting the recognition of long sequences with multiple mode classes. First, the performance of recognition method relies on precise segregation of intercepted sequence, which is often unfeasible in reality. Second, the states at the boundaries of adjacent modes may create extraneous mode samples that intervenes the recognition. Third, current methods fail to deal with the scenarios where multiple modes share the same state sequence. To address these problems, a novel forward matching method (FMM) is proposed, comprising a shortest path method (SPM) for intra‐mode recognition, a matching strategy, and an adjustment mechanism. SPM is to provide potential recognition for short fragments of the given long sequence. The matching strategy is to assess the availability of current recognition. The adjustment mechanism tunes the segregation and improves the subsequent recognition. FMM offers several distinct advantages. First, the model can explicitly characterise the mode transition probability and is totally interpretable. Second, FMM can distinguish intentional ambiguities, alleviate mosaic ambiguity and probability deviation associated with inter‐mode recognition. Third, FMM is extendable to integrate with other intro‐mode recognition methods to cater to various scenarios.

Decoupling Online Ride-Hailing Services: A Privacy Protection Scheme Based on Decentralized Identity

Online ride-hailing services have become a vital component of urban transportation worldwide due to their convenience and flexibility. However, the expansion of their user base has dramatically heightened the risks of user privacy information leakage. Among these risks, the privacy leakage problem caused by the direct correlation between user (driver and passenger) identity information and location-based ride information is of particular concern. This paper proposes a novel privacy protection scheme for ride-hailing services. In this scheme, decentralized identities are employed for user authentication, separating the identity registration service from the ride-hailing platform, thereby preventing the platform from obtaining user privacy data. The scheme also employs a fuzzy matching strategy based on location Points of Interest (POI) and a ciphertext-policy attribute-based hybrid encryption algorithm to hide the user’s precise location and restrict access to location information. Crucially, the scheme achieves the complete decoupling of identity registration services and location-based ride services on the ride-hailing platform, ensuring that users’ real identities and ride data cannot be directly associated, effectively protecting user privacy. Within the decoupled architecture, regulatory authorities are established to handle emergencies within ride-hailing services. Through simulation experiments and security analysis, this scheme is demonstrated to be both feasible and practical, providing a new privacy protection solution for the ride-hailing industry.

Matching Tabular Data to Knowledge Graph with Effective Core Column Set Discovery.

Matching tabular data to a knowledge graph (KG) is critical for understanding the semantic column types, column relationships, and entities of a table. Existing matching approaches rely heavily on core columns that represent primary subject entities on which other columns in the table depend. However, discovering these core columns before understanding the table’s semantics is challenging. Most prior works use heuristic rules, such as the leftmost column, to discover a single core column, while an insightful discovery of the core column set that accurately captures the dependencies between columns is often overlooked. To address these challenges, we introduce Dependency-aware Core Column Set Discovery ( DaCo ), an iterative method that uses a novel rough matching strategy to identify both inter-column dependencies and the core column set. Additionally, DaCo can be seamlessly integrated with pre-trained language models, as proposed in the optimization module. Unlike other methods, DaCo does not require labeled data or contextual information, making it suitable for real-world scenarios. In addition, it can identify multiple core columns within a table, which is common in real-world tables. We conduct experiments on six datasets, including five datasets with single core columns and one dataset with multiple core columns. Our experimental results show that DaCo outperforms existing core column set detection methods, further improving the effectiveness of table understanding tasks.

Lattice matching strategy in Cu-based oxides for large-scale and long-term thermochemical energy storage

Redox-active metal oxides, particularly Cu-based oxide, are noteworthy for their economic feasibility and potential as a recyclable, zero-carbon energy source. These materials are poised to serve as a sustainable solution for large-scale and long-term thermochemical energy storage (TCES), thereby mitigating the intermittency challenges inherent in renewable energy systems. However, a significant impediment to their performance is the materials sintering at elevated temperatures, which precipitate a decline in cyclic reversibility, often manifesting even within the initial cycle of operation. To counteract this limitation, we proposed an innovative approach that leverages the concept of lattice matching, augmented by the incorporation of cigarette butts in the synthesis process to fabricate a Cu-Ce heterogeneous interface. This matched lattice preserved the integrity of the TCES material's porous architecture. Additionally, the lattice oxygen within this composite exhibits a transferability. Even after a prolonged period of two years under ambient air conditions, the TCES material retains the capacity to discharge a remarkable 99.4 % of its adsorbed energy. Furthermore, over the course of 600 cycles, the system's stability is remarkably preserved at 98–100 %, and reversible loss of pure CuO is ∼40 % within the initial cycle. Given these attributes, this TCES material emerges as a promising candidate for industrial applications.

Aerosol optical depth retrieval using scaled digital number (DN) values of multi-spectral satellite and a generating adversarial model based on deep learning application

ABSTRACT The current quantitative retrieval of Aerosol Optical Depth (AOD) typically uses Top-of-atmosphere (TOA) reflectance data obtained by the scale and offset terms of radiometric calibration. Errors can be introduced during the conversion of Digital Number (DN) values to TOA reflectance, restraining the accurate retrieval of AOD. Particularly when surface reflectance is high, conversion errors can significantly distort the retrieval of Aerosol Optical Depth (AOD), given the minimal impact of aerosols on the radiation detected by satellite sensors. To counteract this, we introduce an innovative retrieval algorithm that harnesses a Generative Adversarial Network (GANN), a deep learning (DL) model, to accurately derive AOD from the scaled digital number (DN) values of multi-spectral satellite imagery. The DL technology enables the use of scaled DN values for AOD retrieval, bypassing the conversion errors associated with TOA reflectance since it relies on a sample learning approach instead of radiative transfer calculations. To ensure the number and representativeness of training samples, a total of 37,103 samples from different underlying surfaces from 2014 to 2018 were obtained using a space-time matching strategy. Our K-cross validation demonstrates that employing DN values for AOD retrieval promises to yield higher accuracy compared to using TOA reflectance. Independent 2019 data were used to estimate GANN, MCD19A2 and MOD04_3K aerosol products. Our model showed superior performance compared to the other products in terms of a higher correlation (R = 0.8184) with AERONET AOD and obtaining more reliable retrievals (7601). The sensitivity experiment suggests that GANN exhibits limitations in areas characterized by high aerosol loads and heterogeneity. Our study can give a novel insight into AOD retrieval based on multi-spectral satellite and DL models.