Multimodal image matching is fundamental in computer vision. However, existing methods often struggle to achieve effective cross-modal feature fusion, especially under scale variations and complex scenarios. To this end, we propose PMCMatcher, a Parallel Multi-Scale Cascaded Transformer-Based Network for multimodal feature matching. The core module, the Parallel Multi-Scale Cascaded Transformer, achieves deep interaction and progressive multi-scale fusion through the Selective Multi-Head Linear Attention module and the Cascaded Fusion mechanism. Additionally, the Dynamic Local Feature Enhancement module significantly strengthens the extraction of details by adaptively adjusting convolution kernel weights. To further improve matching accuracy, the Refinement Layer is incorporated to gradually optimize the matching process, enhancing the model’s accuracy and robustness in cross-modal scenarios. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on four representative multimodal image matching datasets, highlighting its superior generalization ability and precise matching accuracy.

OBJECTIVES: We sought to create a computational pipeline for attaching geomarkers, contextual or geographic measures that influence or predict health, to electronic health records at scale, including developing a tool for matching addresses to parcels to assess the impact of housing characteristics on pediatric health. MATERIALS AND METHODS: We created a geomarker pipeline to link residential addresses from hospital admissions at Cincinnati Children's Hospital Medical Center (CCHMC) between July 2016 and June 2022 to place-based data. Linkage methods included by date of admission, geocoding to census tract, street range geocoding, and probabilistic address matching. We assessed 4 methods for probabilistic address matching. RESULTS: We characterized 124 244 hospitalizations experienced by 69 842 children admitted to CCHMC. Of the 55 684 hospitalizations with residential addresses in Hamilton County, Ohio, all were matched to 7 temporal geomarkers, 97% were matched to 79 census tract-level geomarkers and 13 point-level geomarkers, and 75% were matched to 16 parcel-level geomarkers. Parcel-level geomarkers were linked using our exact address matching tool developed using the best-performing linkage method. DISCUSSION: Our multimodal geomarker pipeline provides a reproducible framework for attaching place-based data to health data while maintaining data privacy. This framework can be applied to other populations and in other regions. We also created a tool for address matching that democratizes parcel-level data to advance precision population health efforts. CONCLUSION: We created an open framework for multimodal geomarker assessment by harmonizing and linking a set of over 100 geomarkers to hospitalization data, enabling assessment of links between geomarkers and hospital admissions.

Abstract Copy–move forgery poses a significant threat to social life and has aroused much attention in recent years. Although many copy‐move forgery detection (CMFD) methods have been proposed, the most existing CMFD methods are short of adaptability in detecting images, which leads to the limitation on detection effects. To solve this problem, the paper proposes a novel keypoint‐based CMFD method: second‐keypoint matching and double adaptive filtering (SMDAF). Motivated by image matching based on keypoint, the second‐keypoint matching method is designed to match keypoints extracted from copy–move forgery images, which can be used for both the single‐CMFD and the multiple‐CMFD. Then, a double adaptive filter (DAF) based on the AdaLAM algorithm and the KANN‐DBSCAN clustering algorithm to filter wrong keypoint matches adaptively are proposed, according to the distinct distribution of keypoints in each image. Finally, the forgery regions are presented by finding their convex hulls and padding them. Compared with existing methods, extensive experiments show that the SMDAF method significantly provides more efficiency in detecting images under simulated real‐world conditions, has better robustness when facing images with different post‐treatment attacks, and is more effective in distinguishing images that look copy–move forged but are real.

There are many problems such as strong subjectivity, complex calculations, and lack of intelligence in most of the current energy system evaluation models, so a design of an evaluation system for urban smart energy systems based on an improved genetic algorithm-back propagation (BP) neural network is proposed. First of all, the hierarchical structure of the indicator evaluation system for energy system was established, analytic hierarchy process (AHP) was used to assign weights to each indicator, and data samples were classified. Then, SeqGAN was used to expand the data set, which solved the difficult problem of data acquisition. Finally, the genetic algorithm was used to determine the best initial values of the weights and thresholds of the BP neural network structure parameters designed for this research. The simulation experiment results showed that the method in this paper has higher classification accuracy than the traditional method, and comprehensive evaluation model proposed can effectively evaluate the urban smart energy system.

This paper proposes a virtual transformer operation of a three-stage solid state transformer (SST) used in the distribution network. By tracking the AC voltage at medium voltage AC (MVAC) input side as the reference of low voltage AC (LVAC) output stage, voltage relationship between AC buses can be established, and the mutual voltage support can be formed through the SST. The employed control method can make the SST operate as an ideal AC transformer which has a constant transformation ratio. Circuit parameters design and control parameters calculation are given in this work. Simulation results based on this SST topology and control method prove that this virtual transformer operation based on a three-stage SST is feasible through the software Matlab/Simulink.

Microservice Architecture is a new development paradigm that transforms the traditional business-oriented information management system into the collaborative work of microservices. Microservices are usually distributed in a loosely coupled manner in the network. In order to coordinate tasks, microservices must coordinate their executions through message interactions with each other. Therefore, modeling and analyzing the interaction between microservices becomes a key issue. The choreography defines the interaction contract between services, and the realizability analysis is the key task to ensure the correct implementation of the microservices interaction contract. The choreography is realizable if interaction contract satisfies choreography specification. This paper use CSP# to analyze the realizability of microservice choreography under synchronous communication and bounded asynchronous communication, and a solution is proposed to repair the unrealizable microservice choreography and made it realizable.

Domain-specific modelling is used to construct and realise the different application models upon the same specific domain for software reuse. The paper integrates domain-specific modelling and web service techniques with model-driven development, and proposes a unified approach named SODSMI (Service Oriented executable Domain-Specific Modelling and Implementation) to build the executable domain-specific model so as to achieve the target of model-driven development and reuse. The approach is organised by domain space, which is employed as the elementary unit of the domain-specific modelling and implementation framework. It makes software reuse at the domain level, realises the reuse of domain knowledge, and openly extends the range and scale of domain-specific model and its implementation.

The paper proposes a Fully Flexible Power Transmission & Distribution System (FPTDS) for the next generation power grid, the FPTDS structure used the brand new flexible electric equipment: Energy-Router, Energy-Switcher and Energy-Hub. And then from the Cyber Physical Systems(CPS), the paper gives a Power-CPS (P-CPS) interconnect reference model, and thereby, focuses on designing the fully flexible next generation equipment for Fully Flexible Power Distribution System (FPDS): Energy-Switcher architecture in detail, and researches the next generation distribution grid patterns with Energy-Switchers, presents the grid patterns for all kinds of practical application scenes, finally simulates the Energy-Switcher AC & DC power supply pattern under computer condition, the simulation results demonstrate the systems are feasible and valid.

Power electronic transformer (PET) is an emerging new type of power converter in recent years. It has not only the basic functions of power transformation and isolation, but also the extra functions of power quality control. A novel power electronics transformer for distribution system named flexible power distribution unit is proposed in this paper, and the energy exchange mechanism between the network and load is revealed. Finally, a 100kW 600Vac/220Vac/110Vdc medium frequency isolated prototype is developed and demonstrated in laboratory. The experimental results show that the proposed structure and control strategy are feasible.

Solid State Power electronic transformer (PET) is an emerging new type of power converter in recent years. It has not only the basic functions of power transformation and isolation, but also the extra functions of power quality control. This paper presents the key control strategies of solid state PET for electrical distribution system application, especially under voltage disturbance conditions. Several critical grid voltage disturbances are generated by a disturbance voltage source based on a three phase PWM inverter. And the solid state PET prototype is tested and passed voltage disturbance ride through function. The experimental results verify the PET power quality control abilities.

BACKGROUND: Imputation of individual level genotypes at untyped markers using an external reference panel of genotyped or sequenced individuals has become standard practice in genetic association studies. Direct imputation of summary statistics can also be valuable, for example in meta-analyses where individual level genotype data are not available. Two methods (DIST and ImpG-Summary/LD), that assume a multivariate Gaussian distribution for the association summary statistics, have been proposed for imputing association summary statistics. However, both methods assume that the correlations between association summary statistics are the same as the correlations between the corresponding genotypes. This assumption can be violated in the presence of confounding covariates. METHODS: We analytically show that in the absence of covariates, correlation among association summary statistics is indeed the same as that among the corresponding genotypes, thus serving as a theoretical justification for the recently proposed methods. We continue to prove that in the presence of covariates, correlation among association summary statistics becomes the partial correlation of the corresponding genotypes controlling for covariates. We therefore develop direct imputation of summary statistics allowing covariates (DISSCO). RESULTS: We consider two real-life scenarios where the correlation and partial correlation likely make practical difference: (i) association studies in admixed populations; (ii) association studies in presence of other confounding covariate(s). Application of DISSCO to real datasets under both scenarios shows at least comparable, if not better, performance compared with existing correlation-based methods, particularly for lower frequency variants. For example, DISSCO can reduce the absolute deviation from the truth by 3.9-15.2% for variants with minor allele frequency <5%.

We present a high-performance and real-time production scheduling algorithm for digital print production based on a dynamic incremental evolutionary algorithm. The optimization objective is to prioritize the dispatching sequence of orders and balance resource utilization. The scheduler is scalable for realistic problem instances and it provides solutions quickly for diverse print products that require complex fulfillment procedures. Furthermore, it dynamically ingests the transient state of the factory, such as process information and resource failure probability in print production; therefore, it minimizes the management-production mismatch. Discrete-event simulation results show that the production scheduler leads to a higher and more stable order on-time delivery ratio compared to a rule-based heuristic. Its beneficial attributes collectively contribute to the reduction or elimination of the shortcomings that are inherent in today's digital printing environment and help to enhance a print factory's productivity and profitability.

Accurate predictions of both process-execution time and process status are crucial for the development of an intelligent enterprise information system (EIS). We have developed new automated learning-based process-execution time-prediction and process status-prediction methods that can be embedded into an EIS. Process-execution time prediction is a regression problem and state-of-the-art (baseline) time-prediction methods use a machine-learning regression model. Process status prediction is a binary classification problem in which a class labeled “completed” or “in-progress” is assigned to a process with respect to an arbitrary predictive horizon (i.e., the future time given by the method user). The methods proposed in this paper integrate statistical methods with support-vector regression. Comparison results obtained from the real data of a digital-print enterprise show that the proposed time-prediction method reduces both the relative mean error and the root-mean-squared error of the regression model. Furthermore, the proposed status-prediction method not only achieves higher classification accuracy than state-of-the-art methods, it also estimates the probability of the predicted status. In addition, algorithm development and training phases of the proposed methods do not rely on any arbitrary predictive horizon. Therefore, a single time-prediction model as proposed is sufficient for status prediction as opposed to a baseline status-prediction method that requires classification models for all potential predictive horizons.

An enterprise service-level performance time series is a sequence of data points that quantify demand, throughput, average order-delivery time, quality of service, or end-to-end cost. Analytical and predictive models of such time series can be embedded into an enterprise information system (EIS) in order to provide meaningful insights into potential business problems and generate guidance for appropriate solutions. Time-series analysis includes periodicity detection, decomposition, and correlation analysis. Time-series prediction can be modeled as a regression problem to forecast a sequence of future time-series datapoints based on the given time series. The state-of-the-art (baseline) methods employed in time-series prediction generally apply advanced machine-learning algorithms. In this article, we propose a new univariate method for dealing with midterm time-series prediction. The proposed method first analyzes the hierarchical periodic structure in one time series and decomposes it into trend, season, and noise components. By discarding the noise component, the proposed method only focuses on predicting repetitive season and smoothed trend components. As a result, this method significantly improves upon the performance of baseline methods in midterm time-series prediction. Moreover, we propose a new multivariate method for dealing with short-term time-series prediction. The proposed method utilizes cross-correlation information derived from multiple time series. The amount of data taken from each time series for training the regression model is determined by results from hierarchical cross-correlation analysis. Such a data-filtering strategy leads to improved algorithm efficiency and prediction accuracy. By combining statistical methods with advanced machine-learning algorithms, we have achieved a significantly superior performance in both short-term and midterm time-series predictions compared to state-of-the-art (baseline) methods.

On-demand digital print service is an example of a real-time embedded enterprise system. It offers mass customization and exemplifies personalized manufacturing services. Once a print order is submitted to the print factory by a client, the print service provider (PSP) needs to make a real-time decision on whether to accept or refuse this order. Based on the print factory's current capacity and the order's properties and requirements, an order is refused if its acceptance is not profitable for the PSP. The order is accepted with the most appropriate due date in order to maximize the profit that can result from this order. We have developed an automated learning-based order admission framework that can be embedded into an enterprise environment to provide real-time admission decisions for new orders. The framework consists of three classifiers: Support Vector Machine (SVM), Decision Tree (DT), and Bayesian Probabilistic Model (BPM). The classifiers are trained by history orders and used to predict completion status for new orders. A decision integration technique is implemented to combine the results of the classifiers and predict due dates. Experimental results derived using real factory data from a leading print service provider and Weka open-source software show that the order completion status prediction accuracy is significantly improved by the decision integration strategy. The proposed multiclassifier model also outperforms a standalone regression model.

SUMMARY: Although the 1000 Genomes haplotypes are the most commonly used reference panel for imputation, medical sequencing projects are generating large alternate sets of sequenced samples. Imputation in African Americans using 3384 haplotypes from the Exome Sequencing Project, compared with 2184 haplotypes from 1000 Genomes Project, increased effective sample size by 8.3-11.4% for coding variants with minor allele frequency <1%. No loss of imputation quality was observed using a panel built from phenotypic extremes. We recommend using haplotypes from Exome Sequencing Project alone or concatenation of the two panels over quality score-based post-imputation selection or IMPUTE2's two-panel combination. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Genotype imputation has become an indispensible step in genome-wide association studies (GWAS). Imputation accuracy, directly influencing downstream analysis, has shown to be improved using re-sequencing-based reference panels; however, this comes at the cost of high computational burden due to the huge number of potentially imputable markers (tens of millions) discovered through sequencing a large number of individuals. Therefore, there is an increasing need for access to imputation quality information without actually conducting imputation. To facilitate this process, we have established a publicly available SNP and indel imputability database, aiming to provide direct access to imputation accuracy information for markers identified by the 1000 Genomes Project across four major populations and covering multiple GWAS genotyping platforms. RESULTS: SNP and indel imputability information can be retrieved through a user-friendly interface by providing the ID(s) of the desired variant(s) or by specifying the desired genomic region. The query results can be refined by selecting relevant GWAS genotyping platform(s). This is the first database providing variant imputability information specific to each continental group and to each genotyping platform. In Filipino individuals from the Cebu Longitudinal Health and Nutrition Survey, our database can achieve an area under the receiver-operating characteristic curve of 0.97, 0.91, 0.88 and 0.79 for markers with minor allele frequency >5%, 3-5%, 1-3% and 0.5-1%, respectively. Specifically, by filtering out 48.6% of markers (corresponding to a reduction of up to 48.6% in computational costs for actual imputation) based on the imputability information in our database, we can remove 77%, 58%, 51% and 42% of the poorly imputed markers at the cost of only 0.3%, 0.8%, 1.5% and 4.6% of the well-imputed markers with minor allele frequency >5%, 3-5%, 1-3% and 0.5-1%, respectively. AVAILABILITY: http://www.unc.edu/∼yunmli/imputability.html

This paper analyzes two of the most significant circuit reliability degradation phenomena in current CMOS technology, namely, temporal Negative Bias Temperature Instability (NBTI) for PMOS transistors and spatial Random Dopant Fluctuation (RDF) for both NMOS and PMOS nano-scale transistors. Among many models, Reaction-Diffusion model used for analyzing NBTI and width dependant model proposed for RDF simulations can currently get closest results to real cases. However, in most of the previous work, the causes and effects of NBTI and RDF were investigated individually. In this paper, we try to exploit how they act together to influence threshold voltage (Fth) and, hence, circuit performance. The simulation result shows that if a PMOS transistor suffers from severe RDF effect, NBTI effect inflicts more damage to it in terms of device performance. At the end, some possible methods of weakening the negative effects of NBTI and RDF are proposed and evaluated.

The usage of decision models has been given attention by decision makers for many years. How to realise public-use and reuse of model, and gain the adequate information not only limited to local departments in the wide enterprise environment is one of the most concerned issues of decision-makers. The idea of multiple-tier framework put forward by distributed system provides a helpful approach to this issue. This paper proposes a practical approach to model manipulation in a distributed decision support system based on network technology and component technology. It implements the separation among the clients, application and data level, accordingly allows the data and model source distributed storage and sharing for multi-departments. The method based on COM+ component technology makes the model distributed in the host computer all over the Internet/Intranet. Consequently, widespread models can be supplied to the client by application servers.