Matching Scheme Research Articles

High-confidence structural annotation of substances via multi-layer molecular network reveals the system-wide constituent alternations in milk interfered with diphenylolpropane

The spectral database-based mass spectrometry (MS) matching strategy is versatile for structural annotating in ingredient fluctuation profiling mediated by external interferences. However, the systematic variability of MS pool attributable to aliasing peaks and inadequacy of present spectral database resulted in a substantial metabolic feature depletion. An amended procedure termed multiple-charges overlap peaks extraction algorithm (MCOP) was proposed involving identifying collision-trigged dissociation precursor ions through iteratively matching mass features of fragmentations to expand the spectral reference library. We showcased the versatility and utility of established strategy in an investigation centered on the stimulation of milk mediated by diphenylolpropane (BPA). MCOP enabled efficient unknown annotations at metabolite-lipid-protein level, which elevated the accuracy of substance annotation to 85.3% after manual validation. Arginase and α-amylase (|r| > 0.75, p < 0.05) were first identified as the crucial issues via graph neural network-based virtual screening in the abnormal metabolism of urea triggered by BPA, resulting in the accumulation of arginine (original: 1.7 μg kg−1 1.7 times) and maltodextrin (original: 6.9 μg kg−1 2.9 times) and thus, exciting the potential dietary risks. Conclusively, MCOP demonstrated generalisation and scalability and substantially advanced the discovery of unknown metabolites for complex matrix samples, thus deciphering dark matter in multi-omics.

Blockchain-based cooperative game bilateral matching architecture for shared storage

The development of IPFS (InterPlanetary File System) and blockchain-based distributed storage projects has brought new possibilities to the field of storage. This paper proposes a blockchain-based cooperative game bilateral matching architecture as a novel approach for shared storage networks. In traditional competitive (non-cooperative) game models, the allocation of storage resources is centered around pricing, leading to a scenario where node providers often engage in price competition to obtain greater rewards, resulting in an imbalance in resource allocation for both buyers and sellers. In contrast, a distributed storage model based on cooperative game theory can better facilitate cooperation and resource sharing among node providers. This paper designs a storage resource allocation algorithm based on the stable marriage matching algorithm, demonstrating the stability of this algorithm as a matching solution. The paper also analyzes the differences between cooperative and non-cooperative game models in the market, and explores an equilibrium pricing mechanism guided by supply and demand. Furthermore, the paper introduces a trading mechanism for storage resources, including publication standards and matching schemes, ensuring efficient and trustworthy interaction between storage suppliers and demanders in a decentralized network centered around storage resources, thus enabling the circulation of the value of storage resources. A prototype of a blockchain-based shared storage trading system is implemented in this paper, utilizing bilateral matching for tradings. System evaluation and experimental testing are conducted, with results showing that the average utility value of the matching trading mechanism proposed in this paper outperforms the Double Auction-based matching model under any Poisson distribution (λ = 0.1, 0.2, 0.3, …, 0.9) conditions set in the experiments. Additionally, compared to the traditional approach of directly storing complete data content on the chain, the design proposed in this paper effectively reduces on-chain storage consumption by approximately 27.06%.

Determination of phenytoin at trace levels in domestic wastewater and synthetic urine samples by gas chromatography-mass spectrometry after its preconcentration by simple liquid-phase microextraction.

This work presents a sensitive and accurate analytical method for the determination of phenytoin at trace levels in domestic wastewater and synthetic urine samples by gas chromatography-mass spectrometry (GC-MS) after the metal sieve-linked double syringe liquid-phase microextraction (MSLDS-LPME) method. A metal sieve was produced in our laboratory in order to disperse water-immiscible extraction solvents into aqueous media. Univariate optimization studies for the selection of proper extraction solvent, extraction solvent volume, mixing cycle, and initial sample volume were carried out. Under the optimum MSLDS-LPME conditions, mass-based dynamic range, limit of quantitation (LOQ), limit of detection (LOD), and percent relative standard deviation (%RSD) for the lowest concentration in calibration plot were figured out to be 100.5-10964.2 μg kg-1, 150.6 μg kg-1, 45.2 μg kg-1, and 9.4%, respectively. Detection power was improved as 187.7-folds by the developed MSLDS-LPME-GC-MS system while enhancement in calibration sensitivity was recorded as 188.0-folds. In the final step of this study, the accuracy and applicability of the proposed system were tested by matrix matching calibration strategy. Percent recovery results for domestic wastewater and synthetic urine samples were calculated as 95.6-110.3% and 91.7-106.6%, respectively. These results proved the accuracy and applicability of the proposed preconcentration method, and the obtained analytical results showed the efficiency of the lab-made metal sieve apparatus.

Multi‐target detection and tracking of shallow marine organisms based on improved YOLO v5 and DeepSORT

AbstractIn order to solve the related problems of detection and tracking of shallow marine organisms, this paper designs a YOLO v5 multi‐target detection and tracking algorithm with attention mechanism. When working underwater, the authors usually encounter many difficulties. Different luminosity and complex coral background usually affect the detection of marine organisms. At the same time, the unrestricted movement of marine organisms, the ability to hide behind rocks and algae, and their mutual occlusion while swimming pose additional challenges to this task. Considering the characteristics of shallow marine organisms activity environment, the attention mechanism is added to the feature extraction network of YOLO v5 to reduce redundant information and improve the detection accuracy of shallow marine organism targets in complex environment. The improved algorithm improves the average detection accuracy of marine organisms target detection by 3.2%. Aiming at the problem of shallow marine organisms target tracking, a shallow marine organisms multi‐target tracking algorithm based on improved Deep Simple Online And Realtime Tracking (SORT) is designed. The improved YOLO v5 algorithm is used to replace Faster R‐CNN (Faster Region‐Convolutional Neural Networks) as the detector of DeepSORT tracking algorithm, and the cascade matching strategy is adopted to solve the problem that the target cannot be tracked continuously when it is occluded for a long time. The experimental results show that the shallow marine organisms multi‐target tracking algorithm based on improved DeepSORT reduces the number of id transformation of marine biological target tracking in shallow sea environment, and improves the accuracy of shallow marine organisms multi‐target tracking.

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

In response to the challenges of dynamic adaptability, real-time interactivity, and dynamic optimization posed by the application of existing deep reinforcement learning algorithms in solving complex scheduling problems, this study proposes a novel approach using graph neural networks and deep reinforcement learning to complete the task of job shop scheduling. A distributed multi-agent scheduling architecture (DMASA) is constructed to maximize global rewards, modeling the intelligent manufacturing job shop scheduling problem as a sequential decision problem represented by graphs and using a Graph Embedding–Heterogeneous Graph Neural Network (GE-HetGNN) to encode state nodes and map them to the optimal scheduling strategy, including machine matching and process selection strategies. Finally, an actor–critic architecture-based multi-agent proximal policy optimization algorithm is employed to train the network and optimize the decision-making process. Experimental results demonstrate that the proposed framework exhibits generalizability, outperforms commonly used scheduling rules and RL-based scheduling methods on benchmarks, shows better stability than single-agent scheduling architectures, and breaks through the instance-size constraint, making it suitable for large-scale problems. We verified the feasibility of our proposed method in a specific experimental environment. The experimental results demonstrate that our research can achieve formal modeling and mapping with specific physical processing workshops, which aligns more closely with real-world green scheduling issues and makes it easier for subsequent researchers to integrate algorithms with actual environments.

Research on Tennis Match Strategies Based on Machine Learning and Markov Chain Modeling

This paper mainly studies the influence of athletes' performance on the results of sports competitions, and uses advanced data analysis and machine learning technology to predict the players' scoring patterns in tennis matches. Firstly, the Markov chain model is used to capture match points and game flow. In order to quantify player performance, the concept of momentum is introduced. The random forest model based on factor analysis optimization studies the factors affecting player performance. Secondly, the random forest model optimized by firefly algorithm is used to select the factors that may affect the competition, identify the important factors and data that affect the fluctuation of the competition, and realize the prediction. This study provides an advanced perspective on sports analytics, providing actionable insights for athletes and coaches to refine their strategies and gain a competitive edge, while also providing lessons for the development of sports analytics and data-driven decision making.

Maximizing matching, equity and survival in kidney transplantation using molecular HLA immunogenicity quantitation

HLA matching improves long-term outcomes of kidney transplantation, yet implementation challenges persist, particularly within the African American (Black) patient demographic due to donor scarcity. Consequently, kidney survival rates among Black patients significantly lag behind those of other racial groups. A refined matching scheme holds promise for improving kidney survival, with prioritized matching for Black patients potentially bolstering rates of HLA-matched transplants.To facilitate quantity, quality and equity in kidney transplants, we propose two matching algorithms based on quantification of HLA immunogenicity using the hydrophobic mismatch score (HMS) for prospective transplants. We mined the national transplant patient database (SRTR) for a diverse group of donors and recipients with known racial backgrounds. Additionally, we use novel methods to infer survival assessment in the simulated transplants generated by our matching algorithms, in the absence of actual target outcomes, utilizing modified unsupervised clustering techniques.Our allocation algorithms demonstrated the ability to match 87.7% of Black and 86.1% of White recipients under the HLA immunogenicity threshold of 10. Notably, at the lowest HMS threshold of 0, 4.4% of Black and 12.1% of White recipients were matched, a marked increase from the 1.8% and 6.6% matched under the prevailing allocation scheme. Furthermore, our allocation algorithms yielded similar or improved survival rates, as illustrated by Kaplan–Meier (KM) curves, and enhanced survival prediction accuracy, evidenced by C-indices and Integrated Brier Scores.

A RANDOM FOREST-BASED MODEL OF SCORE FLUCTUATIONS IN PROFESSIONAL TENNIS MATCHES

In tennis matches, the victory and turning points of the game are often influenced by various factors. To explore the factors that affect match fluctuations (changes in the flow of scoring) and to provide suggestions for athletes match strategies, this paper first identifies general indicators through a literature review and uses logistic regression to determine the effectiveness of the chosen model. Secondly, it employs the Fourier function fitting to identify turning points in the match. Considering the scarcity of turning points in the game, this paper uses the SMOTE method to expand the dataset. Subsequently, it tests with a random forest classification model, achieving an accuracy of 93.433%. To improve the models accuracy, several indicators were added to the original model, resulting in a correct rate of 98.51%. Finally, to verify the models results and applicability, the model was applied to other matches with good results. A sensitivity analysis was conducted, revealing good model stability. The model results indicate that the main factors affecting the appearance of turning points include the players movement distance during the match, whether there are changes in the depth and width of the return, score differences, and the maximum number of consecutive wins. When tested in other types of matches, we found that the importance of these factors may change to some extent, but the results remain satisfactory. KEYWORDS: Volatility prediction, Random Forest, Logistic regression, Sensitivity analysis.

An adjustment method of shared private idle parking spaces matching scheme considering demanders cancel reservations

An adjustment method for the initial matching scheme between private idle parking spaces and demanders is proposed in this paper. We consider a shared parking platform to determine the initial matching scheme. However, the demanders may cancel reservations due to the change of destinations or parking time before the matching scheme is executed. Meanwhile, new demanders make reservations. We develop a multi-objective optimization model satisfies adjustment principles to obtain the adjusted matching scheme that mitigates the negative impacts of cancellation reservation. The objective is to maximize the satisfaction of demanders, the satisfaction of private idle parking spaces, and the platform profit. The improved nondominated sorting genetic algorithm II (INSGA-II) is then developed to solve the model and determine the adjusted matching scheme. Our results suggest that the performance of the adjusted matching scheme outperforms that of non-adjusted matching scheme for various experimental sizes.

Pay more attention to consumers: exploring customer acquisition strategies of large third-party sellers on e-B2C market

PurposeThis study develops an empirical framework to address how large third-party sellers (TPSs) can apply customer acquisition strategies to improve their performance in consumers’ person-goods matching process and how the platform firm’s similar strategies moderate the effects of TPSs’ strategies.Design/methodology/approachUsing data collected from the top ten TPSs from a Chinese e-commerce platform, the fixed effect model is used to validate the conceptual model and hypotheses.FindingsThe study results show that both market detection strategy and matching optimization strategy can help large TPSs improve their sales performance. Moreover, the similar market detection strategy applied by the platform firm weakens the effect of large TPSs’ customer acquisition strategies, while the similar matching optimization strategy applied by the platform firm strengthens the effect of large TPSs’ customer acquisition strategies.Originality/valueThis study provides firsthand evidence on the performance of large TPSs’ and the platform firm’s strategies. It demonstrates the effectiveness of large TPSs’ market detection strategy and matching optimization strategy, which can be adopted to meet consumers’ search and evaluation motivations in their person-goods matching process respectively. Moreover, it identifies the role of platform firms by showing the moderating effect of similar strategies adopted by the platform firm on the effect of large TPSs’ customer acquisition strategies.

Impact of neoadjuvant chemotherapy on short-term outcomes after simple and complex minimally invasive minor hepatectomy for colorectal liver metastases: A propensity-score matched and coarsened exact matched study

BackgroundIn the last three decades, minimally invasive liver resection has been replacing conventional open approach in liver surgery. More recently, developments in neoadjuvant chemotherapy have led to increased multidisciplinary management of colorectal liver metastases with both medical and surgical treatment modalities. However, the impact of neoadjuvant chemotherapy on the surgical outcomes of minimally invasive liver resections remains poorly understood. MethodsA multicenter, international, database of 4998 minimally invasive minor hepatectomy for colorectal liver metastases was used to compare surgical outcomes in patients who received neoadjuvant chemotherapy with surgery alone. To correct for baseline imbalance, propensity score matching, coarsened exact matching and inverse probability treatment weighting were performed. Results2546 patients met the inclusion criteria. After propensity score matching there were 759 patients in both groups and 383 patients in both groups after coarsened exact matching. Baseline characteristics were equal after both matching strategies. Neoadjuvant chemotherapy was not associated with statistically significant worse surgical outcomes of minimally invasive minor hepatectomy. ConclusionNeoadjuvant chemotherapy had no statistically significant impact on short-term surgical outcomes after simple and complex minimally invasive minor hepatectomy for colorectal liver metastases.

Image enhancement in palmprint recognition: a novel approach for improved biometric authentication

Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8×7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.

DIML: Deep Interpretable Metric Learning via Structural Matching.

In this paper, we present a new framework named DIML to achieve more interpretable deep metric learning. Unlike traditional deep metric learning method that simply produces a global similarity given two images, DIML computes the overall similarity through the weighted sum of multiple local part-wise similarities, making it easier for human to understand the mechanism of how the model distinguish two images. Specifically, we propose a structural matching strategy that explicitly aligns the spatial embeddings by computing an optimal matching flow between feature maps of the two images. We also devise a multi-scale matching strategy, which considers both global and local similarities and can significantly reduce the computational costs in the application of image retrieval. To handle the view variance in some complicated scenarios, we propose to use cross-correlation as the marginal distribution of the optimal transport to leverage semantic information to locate the important region in the images. Our framework is model-agnostic, which can be applied to off-the-shelf backbone networks and metric learning methods. To extend our DIML to more advanced architectures like vision Transformers (ViTs), we further propose truncated attention rollout and partial similarity to overcome the lack of locality in ViTs. We evaluate our method on three major benchmarks of deep metric learning including CUB200-2011, Cars196, and Stanford Online Products, and achieve substantial improvements over popular metric learning methods with better interpretability.

A Ka-Band Frequency Doubler With a Broadband Matching Scheme for Efficiency Optimization

A Ka-Band Frequency Doubler With a Broadband Matching Scheme for Efficiency Optimization

Game theory-based switch migration strategy for satellite networks

Software-Defined Networking (SDN) separates the control plane and data plane of the network, abstracting the underlying infrastructure of applications and network services and handing it over to the SDN controller for unified management. The introduction of SDN technology into satellite networks effectively addresses control challenges posed by dynamic topologies and intermittent inter-satellite links. Due to factors such as geography and usage patterns, the distribution of remote sensing data traffic is uneven in both space and time. Some remote sensing satellites may acquire large volumes of data within a short period and transmit them back to the ground through intermediate nodes, leading to a sudden surge in load on certain controllers. This situation adversely impacts the performance of remote sensing data transmission. To mitigate this, switch migration operations are required to alleviate the load on overloaded controllers and to achieve the load balance among all controllers. In this paper, a switch migration scheme based on game theory is proposed under a three-layer satellite network architecture, which is designed based on the management costs and ensures timely triggering of the migration operations. Moreover, a game theory-based matching optimization model is formulated in the scheme to address the matching problem between target controllers and migration candidate switches. We further propose a distributed algorithm to seek the Nash equilibrium within the game. Experimental results demonstrate that, compared to other relevant algorithms, this approach can find the near-optimal matching strategy for each target controller and reduce the average latency of these controllers.

A Unified Search Framework for Data Augmentation and Neural Architecture on Small-Scale Image Data Sets

Data augmentation is an effective technique to enrich the training data’s diversity and reduce the risk of overfitting. However, different datasets have distinct preferences on various augmentation techniques. Recently, automated data augmentation (auto-augmentation), which could engineer augmentation policy automatically, drew a growing interest. Previous auto-augmentation methods usually utilize a Density Matching strategy, which highly depends on a large data scale to ensure a precise policy evaluation. When facing small-scale datasets, it usually achieves an inferior performance. To address the problem, an improved method named Augmented Density Matching is proposed by augmenting the train data with policies uniformly sampled from a prior distribution, making the policy evaluation more precise. Moreover, we propose a unified search framework for data augmentation and neural architecture (USAA) by formulating the search processes with one formulation. As a result, both optimal augmentation policy and neural architecture could be obtained within one round of search process. Extensive experiments have been conducted on a bag of medical datasets with general small scales, and the results show that the proposed Augmented Density Matching and USAA can outperform the state-of-the-art auto-augmentation and AutoML approaches, respectively.

Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking.

Recently, template-based trackers have become the leading tracking algorithms with promising performance in terms of efficiency and accuracy. However, the correlation operation between query feature and the given template only achieves accurate target localization, but is prone to state estimation error, especially when the target suffers from severe deformation. To address this issue, segmentation-based trackers are proposed that use per-pixel matching to improve the tracking performance of deformable objects effectively. However, most of the existing trackers only match with the target features of the initial frame, thereby lacking the discrimination for handling a variety of challenging factors, e.g., similar distractors, background clutter, and appearance change. To this end, we propose a dynamic compact memory embedding technique to enhance the discrimination of the segmentation-based visual tracking method that can well tell the target from the background. Specifically, we initialize a memory embedding with the target features in the first frame. During the tracking process, the current target features that have certain correlation with the existing memory are updated to the memory embedding online. To further improve the tracking accuracy for deformable objects, we use a weighted point-to-global matching strategy to measure the correlation between the pixelwise query feature and the whole template, so as to capture more detailed deformation information. Extensive evaluations on six challenging tracking benchmarks including VOT2016, VOT2018, VOT2019, GOT-10K, TrackingNet, and LaSOT demonstrate the superiority of our method over recent remarkable trackers. Besides, our tracker outperforms the excellent segmentation-based trackers, i.e., D3S and SiamMask on the DAVIS2017 benchmark. The code is available at https://github.com/peace-love243/CMEDFL.

Estimating the Effect of Engagement in Community-Based Going-Out Program on Incidence Disability in Older Adults

ObjectivesAlthough going out has been reported to be associated with the incidence of disability, few studies have investigated the effect of community-based programs to promote going out on the incidence of disability. This study aimed to estimate the effects of a program fostering going-out on the incidence of disability in community-dwelling older adults. DesignLongitudinal, observational study. Setting and ParticipantsNonengaged (n = 1086) and engaged older adults (n = 1086) enrolled in the National Center for Geriatrics and Gerontology Study of Geriatric Syndrome by using a one-to-one nearest neighbor propensity score–matching scheme. MethodsAfter the baseline assessments, participants in the community-based going-out program received a specialized physical activity tracker, monitored their daily physical activity, and received personalized feedback on going out to community facilities with a system for reading the device for 12 months. Disability onset was defined as a new case of long-term care under the public insurance certification in Japan within 48 months of program completion. The absolute risk reduction and the number needed to treat for the incidence of disability were calculated for the nonengaged and engaged groups. Cox proportional hazard regression analysis, using inverse probability weighting was used to obtain the hazard ratio. ResultsDisabilities occurred in 112 individuals in the matched nonengaged group and 51 individuals in the engaged group. The absolute risk reduction was 5.67% (95% CI 3.46%-7.88%). The number needed to treat was 18 (95% CI 13-29). The hazard ratio, with the nonengaged group as the reference, was 0.49 (95% CI 0.36-0.67). Conclusions and ImplicationsThis longitudinal observational study suggested that a community-based program could prevent 1 disability in every 18 participants. This program does not require a professional instructor, only the distribution of devices and system installation, and it could be beneficial as a population-based approach to preventing disabilities.

Electromagnetic wave absorption mechanism and wear corrosion characteristics of Ti3SiC2 MAX phase-doped Ni/AlN composite coating with core-shell structure based on laser cladding-induced pitaya-like multi-level heterogeneous interface

To satisfy the service requirements of wave-absorbing materials in harsh environments, it is important to design and manufacture corrosion-resistant composite-absorbing materials. According to the strategy of impedance matching and the dissipation behaviour of nickel-based alloys, AlN ceramics, and MAX-phase Ti3SiC2 multicomponent composite-regulated materials were studied. As an additive manufacturing technology, multilevel heterointerface composites with core–shell structures were obtained by laser cladding. The construction of a unique core–shell structure of a metal–ceramic and ceramic (core)–ceramic (shell) multi-level heterogeneous interface increased the interfacial relaxation and multiple dissipation and improved the impedance matching. The results enabled the N20M3 cladding coating to exhibit excellent microwave absorption characteristics, with a reflection loss of −32.84 dB at the ultra-thin thickness of 1.8 mm. Additionally, the composites exhibited excellent corrosion resistance, high hardness, and relatively good resistance to erosion wear. This work promotes the durability of wave-absorbing materials under severe conditions, as well as the integration of the efficient manufacturing of wave-absorbing materials.

Enhancing action recognition from low-quality skeleton data via part-level knowledge distillation

Skeleton-based action recognition is vital for comprehending human-centric videos and has applications in diverse domains. One of the challenges of skeleton-based action recognition is dealing with low-quality data, such as skeletons that have missing or inaccurate joints. This paper addresses the issue of enhancing action recognition using low-quality skeletons through a general knowledge distillation framework. The proposed framework employs a teacher-student model setup, where a teacher model trained on high-quality skeletons guides the learning of a student model that handles low-quality skeletons. To bridge the gap between heterogeneous high-quality and low-quality skeletons, we present a novel part-based skeleton matching strategy, which exploits shared body parts to facilitate local action pattern learning. An action-specific part matrix is developed to emphasize critical parts for different actions, enabling the student model to distill discriminative part-level knowledge. A novel part-level multi-sample contrastive loss achieves knowledge transfer from multiple high-quality skeletons to low-quality ones, which enables the proposed knowledge distillation framework to include training low-quality skeletons that lack corresponding high-quality matches. Comprehensive experiments conducted on the NTU-RGB+D, Penn Action, and SYSU 3D HOI datasets demonstrate the effectiveness of the proposed knowledge distillation framework.