MOTIVATION: The Sequence Kernel Association Test (SKAT) and its extensions are the most popular methods for studying the association between phenotypes and a set of single nucleotide polymorphisms. Their practical application is very wide, but most of these methods are designed for continuous and binary phenotypes. Ordered categorical phenotypes are also very common in practice, so there is an urgent need to develop SKAT-type tests for proportional odds model. RESULTS: To accommodate ordered categorical phenotypes, we propose a test named the Sequence Kernel Association Test for the Proportional Odds Model (POM-SKAT). It constructs a score test for the variance of the coefficients of interest using a quasi-likelihood and the P-value is evaluated by approximating the asymptotic distribution of the test statistic with the Pearson Type III distribution. Simulation studies demonstrate that our method performs well and achieves high power in detecting gene-phenotype associations. We apply POM-SKAT to rheumatoid arthritis data provided by Genetic Analysis Workshop 16, identifying multiple relevant gene variants. AVAILABILITY AND IMPLEMENTATION: Code is available at GitHub (https://github.com/amss-stat/POM-SKAT).

Wind power gains more and more attention from all over the world as a clean and renewable energy resource, and accurate prediction of wind speed has become a hot issue. This paper presents a novel incremental forecaster based on phase space reconstruction and broad learning network (BLN) for short-term prediction. First, time delay and embedding dimension which take an essential part in phase space reconstruction are determined by the C–C algorithm. Then, these optimal parameters are input to the BLN trained incrementally. Afterward, forecasting values are given by the output layer of BLN. Data collected from a wind farm is adopted for verifying the efficacy of this proposed model. Furthermore, five commonly used assessment indicators are applied to evaluate predictive performance of different models. Results show that the proposed model has the smallest prediction error, which performs better than the other models at one-step to three-step ahead forecasting, and this strength is attributed to address the problem of local optimum. Furthermore, the proposed model consumes less training time than the other models. Therefore, the proposed model tends to be promising for wind speed prediction of the big data era.

In the current research of recommending the workers to the demanders, most scholars only model and analyze task information and workers preferences while ignoring demander preferences. To solve this problem, this paper proposed an improved deep matrix factorization algorithm considering both workers preference and demanders preference, and then applied it to the workers recommendation of the crowdsourcing platform. First, this algorithm constructs user preferences based on tags, using ratings of tags as the initial weights. Second, this algorithm uses Word2vec to get tag vectors, multiply it with the weight, and then average all tag vectors to get the user vector. Third, this algorithm uses the cosine similarity to calculate the user similarity matrix. Last, the user similarity matrix is incorporated into the deep matrix factorization algorithm as auxiliary information. The experimental dataset comes from the Zhubajie.com. The results show that this algorithm is better than the baseline models in both MAE and RMSE.

Summary Array‐unit dual‐usage register is a kind of register resource that can be read or written as a whole or individually. It is mainly configured in processors with SIMD processing units and provides register‐level speed data transfer between the scalar and vector processing units. To improve the efficiency of algorithms by using an array‐unit dual‐usage register, we investigate in this article the problem of adapting register allocation to code containing array‐unit dual‐usage register names. We propose a corresponding global register allocation method by combining the allocation of regular registers with array‐unit dual‐usage register, ensuring that the names of array‐unit dual‐usage register can be used in the input code of register allocation. Moreover, we present the processing framework of this method and the specific algorithms of some related vital aspects and demonstrate the working principles of the algorithms by an example. Experimental studies were conducted on a platform based on the FT‐M7002 DSP core, and showing that our register allocation method can effectively handle codes containing array‐unit dual‐usage register names and support relevant application algorithms to improve their data transfer scheme. For some typical algorithms with input matrix, substantial performance improvements of twofold or higher are achieved.

Remotely sensed nighttime light (NTL) data provides an opportunity to observe human economic activities at night. However, existing NTL remote sensing data does not sufficiently reflect human activities at a fine resolution. In this study, we obtain a set of NTL data comprising volunteered passenger aircraft nighttime remote sensing (VPAN-RS) imagery by adopting a passenger plane as the remote sensing platform and using low-cost portable photography equipment as sensors. This method demonstrates the advantages of VPAN-RS data in high spatial resolution, frequent revisits, and low cost, which can supplement existing NTL data. The dataset acquired in this study covers 16 cities in China and one city in Japan, with a spatial resolution up to meter level. The data acquisition method and processing workflow are introduced in this article. Quality assessment shows that the reprojection error of the dataset falls within the range from 5–10 pixels, and the geometric error can be within 15 m. Via a comparison to reference data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor of the Suomi-National Polar-Orbiting Partnership (NPP) polar-orbiting satellite, we found that the VPAN-RS data provides more details of city lighting. Additionally, the radiance of the VPAN-RS data suitably fits that of monthly composite NPP/VIIRS data. We further outline an application example of VPAN-RS nighttime imagery, examine the unique challenges associated with VPAN-RS data with a focus on the influence of the image acquisition method on the data quality, and provide an outlook for the future of VPAN-RS data. We conclude that VPAN-RS NTL images are an effective data source for nighttime earth observations and have the potential for various applications.

Service evolution is the process of maintaining and evolving existing Web services to cater for new requirements and technological changes. In this paper, a service evolution model is proposed to analyze service dependencies, identify changes on services and estimate impact on consumers that will use new versions of these services. Based on the proposed service evolution model, four service evolution patterns are described: compatibility, transition, split-map, and merge-map. These proposed patterns provide reusable templates to encourage well-defined service evolution while minimizing issues that arise otherwise. They can be applied in the service evolution scenario where a single service is used by many, possibly unknown, consumers' applications. In such a scenario, providers evolve their services independently from consumers, which might cause unexpected errors and incur unpredicted impact on the dependent consumers' applications. Therefore, providers can use these patterns to estimate the impact that changes to be introduced to their services may cause on their consumers, and to allow consumers smoothly migrate to the newest version of the service.

Service-oriented architecture (SOA) has become a widely accepted approach that provides a flexible IT infrastructure in order to deal with the increasing pace of business changes and global competition. However, the evolution of SOA challenges traditional research methodologies and motivates us to explore new approaches for analyzing and evaluating the effects of change. In order to identify the basic principles that analyze the SOA evolution, this work will utilize dependency analysis and information entropy. Dependency analysis is applied in order to derive information from service dependencies to measure the relative importance of the service in a service-oriented system. In addition, we combine information entropy with dependency analysis to measure the service and system entropy. Consequently, we provide a quantificational approach based on dependency analysis and information entropy to estimate the change effects on a service-oriented system.

As many software systems have been turned as Web services, the evolutionary changes of Web services are becoming an important issue. To understand the way in which the change affects the services, we must ascertain parts of the system that will be effected by the change and examine them for additional impacts. In this paper, we propose an impact analysis model based on service dependency. In particular, the service dependency graph model, service dependency and the relation matrix are examined. Based on the shift and calculation of the matrix, the dependency and impact of the service evolution can be analyzed and its quantity can be ascertained. Furthermore, we also represent an approach for service change annotation and for service evolution process. Overall, these works provide a foundation for the automatic management, control, and evaluation of service evolution.

The resource-constrained project scheduling problem (RCPSP) is an extensively explored area. The existing RCPSP approaches tend to focus on single project scheduling problems without practical support to address the multiple project schedule coordination which involves constraints defined across projects. This paper extends RCPSP by involving time and resource constraints and proposes a practical dynamic task network scheduling algorithm. This algorithm takes time constraints, resource constraints, and particularly the dynamic task execution status into consideration. Dynamic scheduling through a partial task network is considered a unique feature of this algorithm. The proposed algorithm is fully implemented and tested in a web-based aircraft inspection maintenance management system.

OWLS (Web Ontology Language for Services) is being widely concerned of composing a Web service based workflow. However, a complete workflow should include both internal and external processes for their strong correlation that can be expressed by the logic power of the Web Ontology Language. Moreover, agent based workflow, from another point of view, provides a flexible mechanism for service discovery and dynamic workflow coordination at run time. This paper presents an agent based workflow ontology model for the purpose of semantic workflow building, reasoning, and process reconfiguration.

The dynamical analysis of pulse variability gives new insight into researches of cardiovascular system's dynamics. Firstly, long-term pulse variability analysis for the researches on cardiovascular system was proposed. Secondly, approximate entropy was applied to analyze three groups of long-term pulse waveform variabilities and we found that the pulses' approximate entropies of patients with cardiovascular disease preferred to smaller value and less irregularity. What more, the pulse variability of the patient with pacemaker newly implanted was also studied. Finally, the pulse variability's clinical value for cardiovascular system was concluded.