Matching Scheme Research Articles

An efficient methodology for simulating multivariate non-Gaussian stochastic processes

Simulation of multivariate non-Gaussian stochastic processes is still a challenging task, where the efficiency and accuracy may not be balanced. In this paper, an efficient methodology is presented to address this challenge. This methodology makes use of a probability weighted moments-based Hermite polynomial model and a wavenumber–frequency spectrum density-based spectral representation approach. First, a probability weighted moments-based Hermite polynomial model is employed to establish a transformation relationship between Gaussian and non-Gaussian samples. Importantly, this model only requires the solution of a solvable system of equations. Second, the target non-Gaussian wavenumber–frequency spectrum density can be promptly transformed into the Gaussian one, where a fast root matching strategy and fast Fourier transform are utilized. Finally, the underlying Gaussian samples are generated by the spectral representation method, and the non-Gaussian samples are obtained via the proposed transformation model. The computational results from numerical examples demonstrate that the proposed methodology is considered to be an accurate, applicable, and efficient approach for simulating multivariate non-Gaussian stochastic processes.

Ending child labour: does conditional cash transfer matter? Evidence from Indonesia

Purpose This study aims to investigate the extent to which the Indonesian conditional cash transfer (CCT), known as the Family Hope Program (FHP), impacts the probability of children engaging in labour activities.Design/methodology/approach This study utilizes data from the Indonesian Family Life Survey in 2014, focussing on periods following the implementation of the FHP. To estimate the impact of FHP on child labour in Indonesia, the authors employ a propensity score matching strategy to balance the characteristics observed between the participant and non-participant groups.Findings The estimates show that FHP has no statistical impact on child labour across all matching techniques. This implies that receiving the CCT does not always help poor households decrease the probability of stopping their children from participating in labour activities.Social implications The conditions applied to the beneficiaries, which only require children to attend school without requiring them to stop working, may not effectively address the issue of child labour. The current structure and design of the FHP need to be re-evaluated and improved to effectively combat child labour.Originality/value Despite numerous studies examining the impact of CCT on child labour which remains inconclusive in Indonesia, this study contributes to the existing literature by considering children participating in labour activities across all types of work without focussing on specific education levels or regions.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0580

Iterative ensemble smoothing scheme for reservoir history matching with unknown observation error covariance

Ensemble-based smoothers are well-known data assimilation tools in reservoir applications, including history matching and geophysical inversion. They entail the knowledge of the covariance of observation errors, which, however, is often poorly known in real applications and commonly estimated subjectively on the basis of a diagonal structure. This neglects the correlations within the observation errors, which may deteriorate the quality of the model estimates. Herein, we relaxed the independent observational error assumption by considering a nondiagonal structure for the covariance. Given that all the elements in the covariance matrix could not be estimated given a limited amount of data, which is typically the case in practice, we reduced the number of degrees of freedom by parameterizing this covariance with two scalar parameters, one for error variance and the other for error correlation length–scale. We then estimated these parameters together with the model variables using a hybrid algorithm that combined a variational Bayesian approach with particle filters and an ensemble-based smoother. The proposed approach was validated with a linear Gaussian model and a nonlinear reservoir flow model. The results clearly demonstrate the potential of the proposed method in effectively addressing the uncertainty of observations in the history-matching process.

Imperfect premise matching finite-time filter design for continuous-time Takagi–Sugeno fuzzy systems

An input–output finite-time filtering design problem for a class of delayed Takagi–Sugeno fuzzy systems with external disturbance input is discussed in this paper. Specifically, the filter design uses the imperfect premise matching scheme that relaxes the requirement that the filter and system membership functions be the same. This method can improve the flexibility of the filter design by incorporating membership function information into the input–output finite-time stability analysis. Using the system and filter states, an augmented filtering system is obtained. The required stability criteria are then derived using the Lyapunov–Krasovskii stability theorem and a few recent inequality approaches. Following that, the desired filter gain matrices are determined. The developed filtering design method is validated through some demonstrative examples.

Radiation-Variation Insensitive Coarse-to-Fine Image Registration for Infrared and Visible Remote Sensing Based on Zero-Shot Learning

Infrared and visible remote sensing image registration is significant for utilizing remote sensing images to obtain scene information. However, it is difficult to establish a large number of correct matches due to the difficulty in obtaining similarity metrics due to the presence of radiation variation between heterogeneous sensors, which is caused by different imaging principles. In addition, the existence of sparse textures in infrared images as well as in some scenes and the small number of relevant trainable datasets also hinder the development of this field. Therefore, we combined data-driven and knowledge-driven methods to propose a Radiation-variation Insensitive, Zero-shot learning-based Registration (RIZER). First, RIZER, as a whole, adopts a detector-free coarse-to-fine registration framework, and the data-driven methods use a Transformer based on zero-shot learning. Next, the knowledge-driven methods are embodied in the coarse-level matches, where we adopt the strategy of seeking reliability by introducing the HNSW algorithm and employing a priori knowledge of local geometric soft constraints. Then, we simulate the matching strategy of the human eye to transform the matching problem into a model-fitting problem and employ a multi-constrained incremental matching approach. Finally, after fine-level coordinate fine tuning, we propose an outlier culling algorithm that only requires very few iterations. Meanwhile, we propose a multi-scene infrared and visible remote sensing image registration dataset. After testing, RIZER achieved a correct matching rate of 99.55% with an RMSE of 1.36 and had an advantage in the number of correct matches, as well as a good generalization ability for other multimodal images, achieving the best results when compared to some traditional and state-of-the-art multimodal registration algorithms.

Numerical study on novel parabolic trough solar receiver-reactors with double-channel structure catalyst particle packed beds by developing actual three-dimensional catalyst porosity distributions

Flow channel structure and operation strategy play important role in fixed packed bed based parabolic trough solar receiver-reactors (PTSRRs) for efficient hydrogen production. A horizontal separated double-channel structure with the countercurrent control strategy (H-DCS-CCS) and a vertical separated double-channel structure with the flow matching strategy (V-DCS-FMS) were proposed for better coupling of multiple physical fields in PTSRRs. A three-dimensional comprehensive numerical model was developed to simulate the complex optical-thermal-chemical process and real spatial pore structure characteristics of novel PTSRRs, based on a proposed coupling calculation procedure of actual three-dimensional catalyst porosity distributions. After validation, the effect mechanism of novel double-channel structures and operation strategies were further investigated. It was revealed that the H-DCS-CCS can significantly reduce the PTSRR maximum temperature, increasing the upper limit of the methanol conversion rate by 7.15%. The V-DCS-FMS can effectively improve the temperature uniformity and the reaction performance within tuned flow rate ratio ranges. This proposed novel concept could provide guidance for similar solar-driven thermochemical hydrogen production systems.

Tanfīż Istirātījiyyah Muṭābiqah Biṭāqah Fihris (Index Card Match) liṭullāb al-Ma’had

This study aims to explain increased student learning outcomes and activeness by applying the Index Card Match strategy to the Nahwu lessons. This research is called Classroom Action Research. This research was carried out twice with four steps: planning, preparation for teaching in class, implementation of the teaching process in class, observation of students' conditions, and reflection by researchers to analyze the results of observations. The research location is the Al-Khair Ponorogo Modern Islamic Boarding School in class 2, with 20 students. In collecting data, researchers interviewed school principals and teachers to obtain information regarding student and school conditions. In the observation, the researcher recorded the conditions before and after implementation. In tests prepared by teachers related to nahwu. The data analysis used is quantitative and qualitative. The results of this study show that student learning outcomes increased by 20%. And increase student activity with a ratio of 12.5% ​​increase in student activity.

An accurate and sensitive determination of selected pesticides in mixed fruit juice samples using the combination of a simple and efficient microextraction method and GC-MS with a matrix matching calibration strategy.

Pesticides have been used on several fruits, vegetables and cereals to control harmful organisms in order to increase the quality of products; however, these substances cause serious health effects. Therefore, an accurate and sensitive analytical method should be developed for the determination of pesticides to evaluate their toxicity. In this study, an efficient microextraction strategy was applied to preconcentrate eight different selected pesticides from mixed fruit juice samples prior to gas chromatography-mass spectrometry detection. All significant parameters such as spraying number, extraction solvent type, sample volume and mixing type/period belonging to the developed extraction method were elaborately optimized to get low detection limits. After the optimization studies, system analytical performance studies were carried out and limit of detection (LOD) values varied from 0.04 μg /kg-1 to 1.99 μg kg-1 (mass based) for the selected analytes. Under the optimum experimental conditions, spiking recovery experiments were performed in the mixed fruit juice samples to evaluate the applicability and accuracy of the proposed method. The recovery results were recorded in the range of 81.4-123.5% with acceptable standard deviations by applying a matrix matching calibration strategy. The proposed analytical method can be used for the qualitative and quantitative determination of selected pesticides in the mixed fruit juice samples and can also be applied to other fruit juice samples using a matrix matching calibration strategy.

Dynamic coordinated strategy for parking guidance in a mixed driving parking lot involving human-driven and autonomous vehicles

<abstract> <p>The advent of autonomous vehicles (AVs) poses challenges to parking guidance in mixed driving scenarios involving human-driven vehicles (HVs) and AVs. This study introduced a dynamic and coordinated strategy (DCS) to optimize parking space allocation and path guidance within a mixed driving parking lot, aiming to enhance parking-cruising efficiency. DCS considers the distinctive characteristics of HVs and AVs and dynamically formulates parking guiding schemes based on real-time conditions. The strategy encompasses four main steps: Triggering scheme formulation, identifying preoccupied parking spaces, updating the parking lot traffic network and optimizing the vehicle-path-space matching scheme. A programming model was established to minimize the total remaining cruising time, and iterative optimization was conducted with vehicle loading test based on timing. To elevate computational efficiency, the concept of parking-cruising path tree (PCPT) and its updating method were introduced based on the dynamic shortest path tree algorithm. Comparative analysis of cases and simulations demonstrated the efficacy of DCS in mitigating parking-cruising duration of different types of vehicles and minimizing forced delays arising from lane blocking. Notably, the optimization effect is particularly significant for vehicles with extended cruising durations or in parking lots with low AV penetration rates and high saturation, with an achievable optimization rate reaching up to 18%. This study addressed challenges related to drivers' noncompliance with guidance and lane blocking, thereby improving overall operational efficiency in mixed driving parking lots.</p> </abstract>

Extracting Systemic Anticancer Treatment Lines from the Danish National Patient Registry for Solid Tumour Patients Treated in the North Denmark Region Between 2009 and 2019.

Reconstructing patient treatment trajectories is important to generate real-world evidence for epidemiological studies. The Danish National Patient Registry (DNPR) contains information about drug prescriptions and could therefore be used to reconstruct treatment trajectories. We aimed to evaluate and enhance two existing methods to reconstruct systemic anticancer treatment trajectories. This study was based on data from 8738 consecutive patients with solid tumors treated in the North Denmark Region between 2009 and 2019. Two approaches found in the literature as well as two new approaches were applied to the DNPR data. All methods relied on time intervals between two consecutive drug administrations to determine if they belonged to the same treatment line. MedOnc, a local dataset from the Department of Oncology, Aalborg University Hospital was used as a reference. To evaluate the performance of each method, F1-scores were calculated after matching the lines identified in both datasets. We used three different matching strategies: stringent matching, loose matching, and matching based on line numbers, controlling for overfitting. Overall, the two new approaches outperformed the simpler and best performing of the two existing methods, with F1-scores of 0.47 and 0.45 vs 0.44 for stringent matching and 0.84 and 0.83 vs 0.82 for loose matching. Nevertheless, only one of the new methods outperformed the existing simpler method when matching on the number of lines (0.73 vs 0.72). Large differences were seen by cancer site, especially for the stringent and line number matchings. Performances were relatively stable by calendar year. The high F1-scores for the new methods confirm that they should be generally preferred to reconstruct systemic anticancer treatment trajectories using the DNPR.

BPMTrack: Multi-Object Tracking with Detection Box Application Pattern Mining.

The key to multi-object tracking is its stability and the retention of identity information. A common problem with most detection-based approaches is trusting and using all the detector outputs for the association. However, some settings of detectors can affect stable long-range tracking. Based on the principle of reducing the association noise in the detection processing step, we propose a new framework, the Box application Pattern Mining Tracker (BPMTrack), to address this issue. Specifically, we worked on three main aspects: output threshold, association strategy, and motion model. Due to the problem of inconsistency between classification scores and localization accuracy, we propose the Box Quality Estimation Network (BQENet) to predict the localization quality scores of all detections in the current frame, reserving high-quality boxes for the tracker. In addition, based on observations of intensive scenarios, we propose a simple and effective data association method, the Non-Maximum Suppression Integration (NMSI) matching strategy. It recovers the Non-Maximum Suppression (NMS) detection, inputs them into BQENet, and then performs hierarchical matching with reasonable control of box priority to alleviate the problem of absent objects caused by occlusion. Finally, we propose an improved Measurement Correct and Noise Scale (MCNS) Kalman algorithm to improve the prediction accuracy of object positions and, thus, the association quality. We performed an extensive ablation evaluation of the proposed framework to prove its effectiveness. Moreover, the three tracking benchmarks show our method's accuracy and long-distance performance.

Align While Fusion: A Generalized Nonaligned Multiview Multilabel Classification Method.

In the task of multiview multilabel (MVML) classification, each object is described by several heterogeneous view features and annotated with multiple relevant labels. Existing MVML methods usually assume that these heterogeneous features are strictly view-aligned, and they directly conduct cross-view information fusion to train a multilabel prediction model. However, in real-world scenarios, such strict view-aligned requirement can be hardly satisfied due to the recurrent spatiotemporal asynchronism when collecting MVML data, which would cause inaccurate multiview fusion results and degrade the classification performance. To address this issue, we propose a generalized nonaligned MVML (GNAM) classification method, which achieves multiview information fusion while aligning cross-view features and accordingly learns a desired multilabel classifier. Specifically, we first introduce a multiorder matching alignment strategy to achieve cross-view feature alignments, where both first-order feature correspondence and second-order structure correspondence are jointly integrated to guarantee the compactness of the view-alignment results. Afterward, a commonality-and individuality-based multiview fusion structure is formulated on the aligned-view features to excavate the consistencies and complementarities across different views, which leads all relevant multiview semantic labels, especially rare labels, to be characterized more comprehensively. Finally, we embed adaptive global label correlations to multilabel classification model to further enhance its semantic expression integrity and develop an alternative algorithm to optimize the whole model. Extensive experimental results have verified that GNAM is significantly superior to other state-of-the-art methods.

Research on the optimization strategy of matching the structure of vocational education and industrial development in Hubei under the background of big data

Abstract There is a certain gap between the current situation of matching vocational education graduates and regional industrial development letters in Hubei Province, which needs to be dealt with by the efforts of vocational education institutions, the government, industrial frontiers, and graduates themselves. Aiming at the current problems of Hubei Province that the match between vocational education and industrial development structure is not high enough and the level of cooperation between industry and education needs to be improved, this paper puts forward the optimization strategy of matching between industry and education in vocational education institutions that contains five suggestions. After designing the hierarchical analysis and fuzzy comprehensive evaluation method to construct the evaluation index system, the C1 vocational education institutions that carried out the optimization reform of industry-industry matching according to the strategy of this paper were compared with the remaining two ordinary vocational education institutions. It is found that the overall level of industry-education matching of C1 institutions, 87.489, is the highest among the three institutions and is excellent, and the scores of the first-level indexes are 87.095, 90.216, and 85.371, respectively. The satisfaction levels of faculty, curriculum construction, and enterprise benefit are 0.91, 0.9, and 0.95, thanks to the fact that C1 colleges and universities have introduced off-campus industrial mentors under the guidance of the strategy of optimizing the matching of production and education increased the number of applied skills courses, expanding school-enterprise cooperation and other initiatives have had obvious effects. The employment rate of graduates in their specialty counterparts has steadily increased from 69.4% in 2018 to 88.5% in 2023, and the degree of structural matching between vocational education and industrial development has significantly increased. This paper makes useful thinking and innovative strategies for achieving better matching between vocational education and industrial structure, and promoting industrial development and economic transformation and upgrading in Hubei Province.

Injecting Text Clues for Improving Anomalous Event Detection From Weakly Labeled Videos.

Video anomaly detection (VAD) aims at localizing the snippets containing anomalous events in long unconstrained videos. The weakly supervised (WS) setting, where solely video-level labels are available during training, has attracted considerable attention, owing to its satisfactory trade-off between the detection performance and annotation cost. However, due to lack of snippet-level dense labels, the existing WS-VAD methods still get easily stuck on the detection errors, caused by false alarms and incomplete localization. To address this dilemma, in this paper, we propose to inject text clues of anomaly-event categories for improving WS-VAD, via a dedicated dual-branch framework. For suppressing the response of confusing normal contexts, we first present a text-guided anomaly discovering (TAG) branch based on a hierarchical matching scheme, which utilizes the label-text queries to search the discriminative anomalous snippets in a global-to-local fashion. To facilitate the completeness of anomaly-instance localization, an anomaly-conditioned text completion (ATC) branch is further designed to perform an auxiliary generative task, which intrinsically forces the model to gather sufficient event semantics from all the relevant anomalous snippets for completely reconstructing the masked description sentence. Furthermore, to encourage the cross-branch knowledge sharing, a mutual learning strategy is introduced by imposing a consistency constraint on the anomaly scores of these two branches. Extensive experimental results on two public benchmarks validate that the proposed method achieves superior performance over the competing methods.

Abnormal User Behavior Detection for Industry Big Data Analysis

Abstract The security of user information and the precision of user services are paramount, necessitating effective detection of abnormal user behavior in vast datasets. This study introduces the QGAN-BDE algorithm, which leverages a quantum generative adversarial network combined with a novel approach for detecting and evaluating abnormal user behavior. Through a feature matching strategy, the algorithm ensures close data alignment between the generator and discriminator. At the same time, the integration of a classical convolutional neural network within the BDE network assesses user behavior abnormalities. Setting distinct thresholds for abnormal behavior and threats enables the differentiation between normal and abnormal activities. Utilizing a dataset and financial stock log data for simulation, the proposed method achieves an AUC value of approximately 0.912 with small negative data samples. Additionally, it records generator and discriminator loss values within the ranges of [1.05,1.55] and [0.49,0.61], respectively, and demonstrates over 80% accuracy in detecting financial stock log anomalies. This method’s reliance on comprehensive big data allows for an in-depth analysis of user behavior, facilitating the timely identification and management of abnormalities.

Graph alignment exploiting the spatial organization improves the similarity of brain networks.

Every brain is unique, having its structural and functional organization shaped by both genetic and environmental factors over the course of its development. Brain image studies tend to produce results by averaging across a group of subjects, under the common assumption that it is possible to subdivide the cortex into homogeneous areas while maintaining a correspondence across subjects. We investigate this assumption: can the structural properties of a specific region of an atlas be assumed to be the same across subjects? This question is addressed by looking at the network representation of the brain, with nodes corresponding to brain regions and edges to their structural relationships. Using an unsupervised graph matching strategy, we align the structural connectomes of a set of healthy subjects, considering parcellations of different granularity, to understand the connectivity misalignment between regions. First, we compare the obtained permutations with four different algorithm initializations: Spatial Adjacency, Identity, Barycenter, and Random. Our results suggest that applying an alignment strategy improves the similarity across subjects when the number of parcels is above 100 and when using Spatial Adjacency and Identity initialization (the most plausible priors). Second, we characterize the obtained permutations, revealing that the majority of permutations happens between neighbors parcels. Lastly, we study the spatial distribution of the permutations. By visualizing the results on the cortex, we observe no clear spatial patterns on the permutations and all the regions across the context are mostly permuted with first and second order neighbors.

DualRC: A Dual-Resolution Learning Framework With Neighbourhood Consensus for Visual Correspondences.

We address the problem of establishing accurate correspondences between two images. We present a flexible framework that can easily adapt to both geometric and semantic matching. Our contribution consists of three parts. Firstly, we propose an end-to-end trainable framework that uses the coarse-to-fine matching strategy to accurately find the correspondences. We generate feature maps in two levels of resolution, enforce the neighbourhood consensus constraint on the coarse feature maps by 4D convolutions and use the resulting correlation map to regulate the matches from the fine feature maps. Secondly, we present three variants of the model with different focuses. Namely, a universal correspondence model named DualRC that is suitable for both geometric and semantic matching, an efficient model named DualRC-L tailored for geometric matching with a lightweight neighbourhood consensus module that significantly accelerates the pipeline for high-resolution input images, and the DualRC-D model in which we propose a novel dynamically adaptive neighbourhood consensus module (DyANC) that dynamically selects the most suitable non-isotropic 4D convolutional kernels with the proper neighbourhood size to account for the scale variation. Last, we thoroughly experiment on public benchmarks for both geometric and semantic matching, showing superior performance in both cases.

Theoretical investigation of synergistically boosting the anchoring and electrochemical performance of lithiophilic/sulfiphilic transition metal carbides for lithium-sulfur batteries.

Lithium-sulfur (Li-S) battery is one of the most promising next-generation energy-storage systems with a high energy density and low cost. However, their commercial applications face several challenges, such as the shuttle effect caused by the soluble lithium polysulfide (LiPSs) intermediates and the sluggish sulfur redox reaction. In this article, we systematically investigated the anchoring and electrochemical performance of a series of transition metal carbides (TMCs: TiC, VC, ZrC, NbC, HfC, TaC) as cathode materials for Li-S batteries by theoretical calculations. The lithiophilic/sulfiphilic non-polar (001) surfaces of TMCs can offer moderate binding strength with LiPS intermediates, ensuring good performance of sulfur immobilization. These TMCs can also facilitate lithium diffusion, indicating the good rate performance of Li-S batteries. We also demonstrated that the studied TMCs can be classified into two classes according to their catalytic activity for Li2S decomposition which originated from their different electronic structural features. Furthermore, TiC, ZrC, and HfC exhibited excellent bifunctional electrochemical activity through reducing the Gibbs free energy for sulfur reduction reactions (SRRs) and lowering the barrier for Li2S decomposition which facilitates accelerating electrode kinetics and elevating utilization of sulfur. Our results offer a systematic approach to designing and screening non-polar materials for high-performance Li-S batteries, based on the rational electronic structure and lattice match strategy.

A Hybrid DRL-Based Adaptive Traffic Matching Strategy for Transmitting and Computing in MEC-Enabled IIoT

A Hybrid DRL-Based Adaptive Traffic Matching Strategy for Transmitting and Computing in MEC-Enabled IIoT

PIRCHE-II Risk and Acceptable Mismatch Profile Analysis in Solid Organ Transplantation.

To optimize outcomes in solid organ transplantation, the HLA genes are regularly compared and matched between the donor and recipient. However, in many cases a transplant cannot be fully matched, due to widespread variation across populations and the hyperpolymorphism of HLA alleles. Mismatches of the HLA molecules in transplanted tissue can be recognized by immune cells of the recipient, leading to immune response and possibly organ rejection. These adverse outcomes are reduced by analysis using epitope-focused models that consider the immune relevance of the mismatched HLA.PIRCHE, an acronym for Predicted Indirectly ReCognizable HLAEpitopes, aims to categorize and quantify HLA mismatches in a patient-donor pair by predicting HLA-derived T cell epitopes. Specifically, the algorithm predicts and counts the HLA-derived peptides that can be presented by the host HLA, known as indirectly-presented T cell epitopes. Looking at the immune-relevant epitopes within HLA allows a more biologically relevant understanding of immune response, and provides an expanded donor pool for a more refined matching strategy compared with allele-level matching. This PIRCHE algorithm is available for analysis of single transplantations, as well as bulk analysis for population studies and statistical analysis for comparison of probability of organ availability and risk profiles.