Relation extraction, a crucial task in understanding the intricate relationships between entities in biomedical domains, has predominantly focused on binary relations within single sentences. However, in practical biomedical scenarios, relationships often extend across multiple sentences, leading to extraction errors with potential impacts on clinical decision-making and medical diagnosis. To overcome this limitation, we present a novel cross-sentence relation extraction framework that integrates and enhances coreference resolution and relation extraction models. Coreference resolution serves as the foundation, breaking sentence boundaries and linking entities across sentences. Our framework incorporates pre-trained deep language representations and leverages graph LSTMs to effectively model cross-sentence entity mentions. The use of a self-attentive Transformer architecture and external semantic information further enhances the modeling of intricate relationships. Comprehensive experiments conducted on two standard datasets, namely the BioNLP dataset and THYME dataset, demonstrate the state-of-the-art performance of our proposed approach.

5G customer premise equipment (5G-CPE) is an IoT gateway technology that integrates 5G and Wi-Fi and therefore can provide Wi-Fi connection for IoT devices and meanwhile benefit from the advantages of 5G. With the increasing number of IoT devices, transmission collisions and hidden/exposed terminal problems on the Wi-Fi connection side become more and more serious. Conventional mechanisms cannot solve these problems well. In this paper, we propose a Wi-Fi sector (Wi-FiS) design, which is compatible with Wi-Fi, to solve them fundamentally. Wi-FiS divides the whole coverage area of Wi-Fi into multiple sectors and utilizes beamforming technology and sector-based scheduling to improve system performance of Wi-Fi dense networks. For a single-cell network, Wi-FiS differentiates uplink and downlink operations and totally excludes collision in downlink. For a multicell network, Wi-FiS can avoid hidden and exposed terminal problems, while enabling parallel transmissions among multiple cells. We then develop a theoretical model to analyze Wi-FiS’s throughput. Extensive simulations verify that our theoretical model is very accurate and Wi-FiS can improve system throughput of Wi-Fi dense networks significantly.

Radio frequency identification (RFID) networks planning (RNP) is a challenging task on how to deploy RFID readers under certain constraints. Existing RNP models are usually derived from the flat and centralized-processing framework identified by vertical integration within a set of objectives which couple different types of control variables. This paper proposes a two-level RNP model based on the hierarchical decoupling principle to reduce computational complexity, in which the costefficient planning at the top levels is modeled with a set of discrete control variables (i.e., switch states of readers), and the quality of service objectives at the bottom level are modeled with a set of continuous control variables (i.e., physical coordinate and radiate power). The model of the objectives at the two levels is essentially a multiobjective problem. In order to optimize this model, this paper proposes a specific multiobjective artificial bee colony optimizer called H-MOABC, which is based on performance indicators with reinforcement learning and orthogonal Latin squares approach. The proposed algorithm proves to be competitive in dealing with two-objective and three-objective optimization problems in comparison with state-of-the-art algorithms. In the experiments, H-MOABC is employed to solve the two scalable real-world RNP instances in the hierarchical decoupling manner. Computational results shows that the proposed H-MOABC is very effective and efficient in RFID networks optimization.