Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices. including cell phones and other sensing devices in Location-based Social Network (LB-SN). It can help travelling enterprises intelligently predict users' interests and preferences, provide them with scientific tourism paths and increase the enterprises income. Thus, successive Point-of-Interest (POI) recommendation has become hot research topic in Augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the Recurrent Neural Network (RNN)-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The Graph Neural Network (GNN)-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose an Interaction-enhanced and Time-aware Graph Convolution Network (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The Enterprise Management Systems (EMS) can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN bring better results compared to the existing methods.

Summary With the ever‐increasing popularity of web application programming interfaces (APIs) sharing communities, it is becoming a promising way for software developers to design and create their interesting Apps through composing a set of selected web APIs that can collectively fulfill the App functions expected by the App developer. However, the App developer's web APIs selection decision‐makings are often nontrivial due to the massive candidate APIs as well as their diverse functions. Furthermore, it is difficult to guarantee that the selected web APIs are compatible enough. Moreover, traditional web APIs recommendation approaches only return a recommended APIs list, which are often not sufficient to accommodate the App developer's undetermined and fuzzy personalized preferences. Considering the above challenges, a novel keywords‐driven web APIs recommendation approach called keywords‐driven and compatibility‐aware multiple API group recommendation is proposed in this article for green and compatible software, which cannot only satisfy the App developer's functional requirements, but also return a group of web APIs recommended lists. Each returned list includes a set of compatible web APIs. Finally, we design a series of experiments based on a real‐world web APIs dataset, that is, PW dataset crawled from www.programmableWeb.com. Experimental reports compared with other competitive approaches in existing literatures indicate the effectiveness and efficiency of our proposal in this work.

Background: Acute lymphoblastic leukemia (ALL) arises from an imbalanced proliferation and differentiation of lymphoid progenitors due to special chromosomal and epigenetic abnormalities affecting cell cycle regulation. The cyclin-dependent kinase inhibitor (CDKI) family has crucial functions in G1 progression and G1 to S entry regulation. Among CDKIs, P21 and P27 are able to exert remarkable effects on all CDKs. Hence, we investigated the expression levels of P21 and P27 in ALL patients to determine whether or not their expression had been altered. Materials and Methods: In the present study, we evaluated P21 and P27 expression in bone marrow and peripheral blood samples of 52 newly diagnosed ALL patients (30 males, 22 females) and 13 healthy normal controls (5 males, 8 females) using quantitative real-time PCR. Data were analyzed via SPSS (version 16) software and P<0.05 was assigned as the statistical significance level. Results: Our findings demonstrated lower expression levels of P21 and P27 in ALL patients compared with normal controls (8.33-and 1.69-fold change, respectively). P21 and P27 expression was significantly different between T-cell acute lymphoblastic leukemia (T-ALL) and B-cell acute lymphoblastic leukemia (B-ALL) patients (P= 0.03). Conclusion: Since P21 and P27 are able to influence the activity of both cyclins and CDKs, it is postulated that decreased expression of these genes reduces P21-and P27-mediated suppressive effects on cyclins and CDKs. Therefore, these events facilitate the activation of cyclins and CDKs which may result in cancer progression in ALL patients. Abstract

This paper investigates the most appropriate application programming interface (API) that best accelerates the flow-based applications on the wireless sensor networks (WSNs). Each WSN include many sensor nodes which have limited resources. These sensor nodes are connected together using base stations. The base stations are commonly network systems with conventional processors which are responsible for handling a large amount of communicated data in flows of network packets. For this purpose, classification of the communicated packets is considered the primary process in such systems. With the advent of high-performance multicore processors, developers in the network industry have considered these processors as a striking choice for implementing a wide range of flow-based wireless sensor networking applications. The main challenge in this field is choosing and exploiting an API which best allows multithreading; i.e. one which maximally hides the latency of performing complex operations by threads and increases the overall efficiency of the cores. This paper assesses the efficiency of Thread, Open Multiprocessing, and Threading Building Blocks (TBB) libraries in multithread implementation of set-pruning and grid-of-tries packet classification algorithms on dual-core and quad-core processors. In all cases, the speed and throughput of all parallel versions of the classification algorithms are much more than the corresponding serial versions. Moreover, for parallel classification of a sufficiently large number of packets by both classification algorithms, TBB library results in higher throughput and performance than the other libraries due to its automatic scheduling and internal task stealing mechanism.