Matching Scheme Research Articles

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

How to achieve effective matching donations: the impact of matching strategies on corporate social responsibility performance

How to achieve effective matching donations: the impact of matching strategies on corporate social responsibility performance

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.

Multifunctional phase-change composites for green electromagnetic interference shielding and thermal response prepared under the guidance of an impedance matching strategy.

With the advent of the information age, electromagnetic hazards are becoming more serious. In view of environmental protection, green electromagnetic interference (EMI) shielding materials with little or no secondary reflection have become the ideal choice. In this paper, by freeze-drying, high-temperature carbonization, coating and impregnation backfilling, we prepared carbonized Ni-MOF/reduced graphene oxide/silver nanowire-polyimide@polyethylene glycol composites (Ni@C/r-GO/AgNW-PI@PEG) with gradient conductivity based on impedance matching. The impedance matching layer Ni@C/r-GO-300 reduces the reflection of electromagnetic waves from the surface of the material, the dissipation layer Ni@C/r-GO-600 provides excellent electromagnetic wave dissipation capability, and the reflection layer AgNW-PI ensures that the electromagnetic waves are reflected back into the material. Meanwhile, the EMI shielding performance value of Ni@C/r-GO/AgNW-PI@PEG reaches 62.3 dB with an ultra-low reflectivity (R) of 0.04. In CST simulations, the intrinsic mechanism of electromagnetic energy loss within the material is revealed by energy loss density cloud maps. In addition, heat from high-temperature objects is transferred through the highly thermally conductive AgNW-PI membrane to the long-channel Ni@C/r-GO backbone. Therefore, the composites prepared on the basis of impedance matching will accelerate the use of EMI shielding materials for the thermal management of portable electronic devices and battery heat dissipation packaging.

TransMatch: A Transformer-Based Multilevel Dual-Stream Feature Matching Network for Unsupervised Deformable Image Registration.

Feature matching, which refers to establishing the correspondence of regions between two images (usually voxel features), is a crucial prerequisite of feature-based registration. For deformable image registration tasks, traditional feature-based registration methods typically use an iterative matching strategy for interest region matching, where feature selection and matching are explicit, but specific feature selection schemes are often useful in solving application-specific problems and require several minutes for each registration. In the past few years, the feasibility of learning-based methods, such as VoxelMorph and TransMorph, has been proven, and their performance has been shown to be competitive compared to traditional methods. However, these methods are usually single-stream, where the two images to be registered are concatenated into a 2-channel whole, and then the deformation field is output directly. The transformation of image features into interimage matching relationships is implicit. In this paper, we propose a novel end-to-end dual-stream unsupervised framework, named TransMatch, where each image is fed into a separate stream branch, and each branch performs feature extraction independently. Then, we implement explicit multilevel feature matching between image pairs via the query-key matching idea of the self-attention mechanism in the Transformer model. Comprehensive experiments are conducted on three 3D brain MR datasets, LPBA40, IXI, and OASIS, and the results show that the proposed method achieves state-of-the-art performance in several evaluation metrics compared to the commonly utilized registration methods, including SyN, NiftyReg, VoxelMorph, CycleMorph, ViT-V-Net, and TransMorph, demonstrating the effectiveness of our model in deformable medical image registration.

Flexible Iris Matching Based on Spatial Feature Reconstruction

In an iris recognition-at-a-distance (IAAD) system used in surveillance scenarios, the camera usually captures a large number of low-quality images. These images exhibit partial occlusions due to eyelids and eyelashes, specular reflections, and severe deformations caused by pupil dilations and contractions. Recognizing these low-quality images is a challenging yet dominant problem in IAAD. To mitigate this issue, current iris recognition systems mostly filter out low-quality images by using strict criteria based on image quality evaluation. This strategy, however, wastes device capabilities and produces low throughput of subjects. Other systems require highly cooperative users. In this work, we propose a novel occlusion-robust, deformation-robust, and alignment-free framework for low-quality iris matching, which integrates the merits of deep features and sparse representation in an end-to-end learning process known as iris spatial feature reconstruction (ISFR). Here each probe image can be sparsely reconstructed on the basis of appropriate feature maps from gallery high-quality images. ISFR uses the error from robust reconstruction over spatial pyramid features to measure similarities between two iris images, which naturally avoids the time-consuming alignment step. In summary, the distinctiveness of deep features, the robustness of sparse reconstruction, and the flexibility of multiscale matching strategy are unified in a general framework to attain more accurate and reasonable iris matching. Extensive experimental results on four public iris image databases demonstrate that the proposed method significantly outperforms both traditional and deep learning-based iris recognition methods.

Comparative study of positioning and technical-tactical indicators between teams of different performance levels in the Qatar 2022 FIFA world cup

The aim of the study was to identify the technical-tactical indicators and differentiate collective positioning between the qualified teams and teams non-qualified for the final phase of the FIFA World Cup Qatar 2022, considering effective playing time. The aim was also to understand the interaction of variables that significantly increased the likelihood of being qualified in the analysed championship. We conducted a comparative analysis that covered all matches played (N=64), evaluating 93 technical-tactical indicators, 24 collective positional indicators and six hybrid indicators. The absolute technical-tactical indicators were normalised based on the effective playing time of each team in each match. We used t-tests and binary logistic regression (R2 Nagelkerke = .738 – AUC = .955) to analyse differences and determine their statistical significance (p<.05). Our analysis revealed significant differences in 33 indicators, suggesting that certain technical-tactical aspects played a crucial role in teams’ performance. Furthermore, through multivariate analysis, we were able to identify that offensive efficiency in set pieces, the height of the defensive line during the offensive phase, and the ability to reduce the available playing space for the opposing team during the defensive phase emerged as the main indicators that allowed us to classify the teams’ performance. These findings enable coaches to use the identified key indicators as performance predictors to devise match strategies aimed at enhancing the effectiveness of their teams.

CPBA-CLIM: An entity-relation extraction model for ontology-based knowledge graph construction in hazardous chemical incident management.

In recent years, hazardous chemical incidents have occurred frequently, resulting in significant human casualties, property damage, and environmental pollution due to human or natural factors. Accurately mining the lessons learned from accumulating incident reports and constructing the knowledge graph for hazardous chemical incident management can assist managers in identifying patterns and analyzing common attributes, thereby preventing the recurrence of similar incidents. This article addresses the challenges of dispersed textual information, specialized vocabulary, and data formats in hazardous chemical incidents. We propose a novel entity-relation extraction model called CPBA-CLIM (content-position-based attention-cross-label intersect matching) to provide an accurate data foundation for constructing the hazardous chemical incident knowledge graph. The content-position-based attention module, based on content-position attention, incorporates contextual semantic information into the combined encoding of bidirectional encoder representations from the transformer's content and position to obtain dynamic word vectors that align with the thematic context of the text. Additionally, the cross-label intersect matching strategy evaluates the rationality of entity-relation interactions in sets containing potential overlaps, reducing the impact of entity-relation overlap on triplet extraction accuracy. Comparative experimental results on public datasets demonstrate the model's outstanding performance in overlapping triplets. Qualitative experiments on a self-constructed dataset integrate our model with ontology construction techniques, successfully establishing a knowledge graph for managing hazardous chemical incidents. This research effectively enhances the degree of automation and efficiency in knowledge graph construction, thus offering support and decision-making foundations for hazardous chemical safety management.

Polyvinyl alcohol/chitosan based nanocomposite organohydrogel flexible wearable strain sensors for sports monitoring and underwater communication rescue

Hydrogel-based flexible wearable sensors have garnered significant attention in recent years. However, the use of hydrogel, a biomaterial known for its high toughness, environmental friendliness, and frost resistance, poses a considerable challenge. In this study, we propose a stepwise construction and multiple non-covalent interaction matching strategy to successfully prepare dynamically physically crosslinked multifunctional conductive hydrogels. These hydrogels self-assembled to form a rigid crosslinked network through intermolecular hydrogen bonding and metal ion coordination chelation. Furthermore, the freeze-thawing process promoted the formation of poly(vinyl alcohol) microcrystalline domains within the amorphous hydrogel network system, resulting in exceptional mechanical properties, including a tensile strength (2.09 ± 0.01 MPa) and elongation at break of 562 ± 12 %. It can lift 10,000 times its own weight. Additionally, these hydrogels exhibit excellent resistance to swelling and maintain good toughness even at temperatures as low as −60 °C. As a wearable strain sensor with remarkable sensing ability (GF = 1.46), it can be effectively utilized in water and underwater environments. Moreover, it demonstrates excellent antimicrobial properties against Escherichia coli (Gram-negative bacteria). Leveraging its impressive sensing ability, we combine signal recognition with a deep learning model by incorporating Morse code for encryption and decryption, enabling information transmission.

SSTM: Semantic Segmentation-based Template Matching method for heterogenous images

Global navigation satellite systems (GNSS) have disadvantages such as signal interference, loss, and spoofing. This has led to the development of new methods to complement or replace satellite navigation. Absolute visual localization is one of the main methods of vision-based localization. The goal is to localize the current view of the UAV in a reference satellite map or in a georeferenced image of a previous flight. During nighttime, due to poor lighting conditions, infrared images are used as a substitute for visible light images. However, the significant differences between infrared and visible light images pose challenges in matching them. Therefore, this paper proposes a Semantic Segmentation-based Template Matching (SSTM) method for heterogeneous images. The method proposed in this paper breaks the general idea of using a single feature extraction algorithm for heterogeneous image matching. In this paper, the matching of UAV infrared images with visible light satellite remote sensing images is realized by unifying two different image feature extraction modules of the feature description language and the matching scheme of this feature description language. The advantages of this method are demonstrated through comparisons with other advanced techniques. With the development of semantic segmentation techniques, it is believed that this modular “translation” method can achieve matching between arbitrary sensors.

Theoretical research on the application of impedance calculation in the design of high-intensity low-frequency acoustic generator

The impedance matching is essential in producing high-intensity acoustic waves. This paper studies the impedance calculation methods of modulators and double-chamber Helmholtz resonators based on fluid mechanics, aeroacoustics, and electro-acoustic analogy principles. The results: ① The impedance expression of the resonators is derived. ② The impedance variation law of the chamber volume, the stator size, the air supply pressure, the air supply flow, and the working frequency is calculated. ③ The impedance-matching design scheme of the two-chamber Helmholtz low frequency and high acoustic intensity generator is given. Based on the experimental results of the high acoustic intensity generating device that the author has manufactured in the closed space, the correctness of the impedance calculation and matching design scheme in this paper is verified. This method has guiding significance for the design of high-intensity acoustic generating devices in confined space.

Subject Matter Complexity and Disclosure Channel Richness

ABSTRACT Despite the increase in and diversity of disclosure channels available, our understanding of how managers incorporate channel features into their disclosure decisions remains incomplete. I provide evidence that managers choose relatively rich channels that offer multiple cues, opportunities for interaction, and linguistic diversity (i.e., the earnings call, as compared to the press release) to communicate complex information. The positive relation between disclosure channel richness and subject matter complexity persists in both a document-level analysis and a small sample test examining disclosure channel choice from all possible disclosure channels. I provide some evidence that deviating from the complexity/richness matching strategy is associated with a muted market response to firms’ quarterly disclosures. The results are consistent with managers choosing disclosure channels to reduce investors’ information processing costs. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G14; M41.

Alcohol for seizure induction in the epilepsy monitoring unit

Rationale: Seizure induction techniques are used in the epilepsy monitoring unit (EMU) to increase diagnostic yield and reduce length of stay. There are insufficient data on the efficacy of alcohol as an induction technique. MethodsWe performed a retrospective cohort study using six years of EMU data at our institution. We compared cases who received alcohol for seizure induction to matched controls who did not. The groups were matched on the following variables: age, reason for admission, length of stay, number of antiseizure medications (ASM) at admission, whether ASMs were tapered during admission, and presence of interictal epileptiform discharges. We used both propensity score and exact matching strategies. We compared the likelihood of epileptic seizures and nonepileptic events in cases versus controls using Kaplan-Meier time-to-event analysis, as well as odds ratios for these outcomes occurring at any time during the admission. ResultsWe analyzed 256 cases who received alcohol (median dose 2.5 standard drinks) and 256 propensity score-matched controls. Cases who received alcohol were no more likely than controls to have an epileptic seizure (X2(1) = 0.01, p = 0.93) or nonepileptic event (X2(1) = 2.1, p = 0.14) in the first 48 h after alcohol administration. For the admission overall, cases were no more likely to have an epileptic seizure (OR 0.89, 95 % CI 0.61–1.28, p = 0.58), nonepileptic event (OR 0.97, CI 0.62–1.53, p = 1.00), nor require rescue benzodiazepine (OR 0.63, CI 0.35–1.12, p = 0.15). Stratified analyses revealed no increased risk of epileptic seizure in any subgroups. Sensitivity analysis using exact matching showed that results were robust to matching strategy. ConclusionsAlcohol was not an effective induction technique in the EMU. This finding has implications for counseling patients with epilepsy about the risks of drinking alcohol in moderation in their daily lives.

Powertrain structure analysis of extended range electric vehicles

Since its invention, the internal combustion engine has greatly contributed to the development of human civilization and is also an important symbol of the progress of human civilization. The energy and environmental problems brought by traditional automobiles have seriously restricted the development of today's automotive industry. Due to the bottleneck of pure electric vehicle power battery technology, its short range and short battery life, the extended range electric vehicle is a smooth transition model of the pure electric vehicle, with its high efficiency, small battery capacity, long driving range and other advantages have received widespread attention. In this paper, the key technical issues, including the component selection, parameter matching, and control strategy of the extended-range electric vehicle drive system, are analyzed and discussed in detail. Several issues worthy of attention in future research are pointed out in the paper and provide insights for further development of extended-range electric vehicles.

Pregnancy Weight Gain After Gastric Bypass or Sleeve Gastrectomy

Pregnancy weight gain may affect the association of bariatric surgery with postsurgery pregnancy outcomes. However, the association of pregnancy weight gain with bariatric surgery is unclear. To compare pregnancy weight gain among women with a history of bariatric surgery vs those without and to investigate whether pregnancy weight gain differs by surgical procedure, surgery-to-conception interval, and/or surgery-to-conception weight loss. This nationwide, population-based matched cohort study was conducted in Sweden from 2014 to 2021. Singleton pregnancies with a history of bariatric surgery were propensity score matched (1:1) to pregnancies without such a history according to early-pregnancy body mass index (BMI), prepregnancy diabetes, prepregnancy hypertension, maternal age, smoking status, education level, height, country of birth, and delivery year. In addition, post-gastric bypass pregnancies were matched to post-sleeve gastrectomy pregnancies using the same matching strategy. Data analysis was performed from November 2022 to May 2023. History of bariatric surgery. Pregnancy weight gain was standardized by gestational age into early-pregnancy BMI-specific z scores. This study included 12 776 pregnancies, of which 6388 had a history of bariatric surgery and 6388 were matched controls. The mean (SD) age was 31.6 (4.9) years for the surgery group and 31.4 (5.2) for the matched controls, with an early-pregnancy mean (SD) BMI of 29.4 (5.2) in both groups. Across all early-pregnancy BMI strata, women with a history of bariatric surgery had lower pregnancy weight gain than matched controls. The differences in pregnancy weight gain z score values between the 2 groups were -0.33 (95% CI, -0.43 to -0.23) for normal weight, -0.33 (95% CI, -0.40 to -0.27) for overweight, -0.21 (95% CI, -0.29 to -0.13) for obese class I, -0.16 (95% CI, -0.29 to -0.03) for obese class II, and -0.08 (95% CI, -0.28 to 0.13) for obese class III. Pregnancy weight gain did not differ by surgical procedure. A shorter surgery-to-conception interval (particularly within 1 year) or lower surgery-to-conception weight loss was associated with lower pregnancy weight gain. In this nationwide matched cohort study, women with a history of bariatric surgery had lower pregnancy weight gain than matched controls with similar early-pregnancy characteristics. Pregnancy weight gain was lower in those with a shorter surgery-to-conception interval or lower surgery-to-conception weight loss, but did not differ by surgical procedure.

Linking physical violence to women’s mobility in Chile

Despite increased global attention on violence against women, understanding the factors that lead to women becoming victims remains a critical challenge. Notably, the impact of domestic violence on women’s mobility—a critical determinant of their social and economic independence—has remained largely unexplored. This study bridges this gap, employing police records to quantify physical and psychological domestic violence, while leveraging mobile phone data to proxy women’s mobility. Our analyses reveal a negative correlation between physical violence and female mobility, an association that withstands robustness checks, including controls for economic independence variables like education, employment, and occupational segregation, bootstrapping of the data set, and applying a generalized propensity score matching identification strategy. The study emphasizes the potential causal role of physical violence on decreased female mobility, asserting the value of interdisciplinary research in exploring such multifaceted social phenomena to open avenues for preventive measures. The implications of this research extend into the realm of public policy and intervention development, offering new strategies to combat and ultimately eradicate domestic violence against women, thereby contributing to wider efforts toward gender equity.