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This paper presents a high isolation four-port MIMO slot antenna for sub-6G applications. Four antenna elements are placed vertically to form vertical polarization. In addition, etching trapezoidal grooves on the four sides of the ground can not only improve the low-frequency isolation, but also increase the bandwidth. The measured results show that the antenna can work at 1.95–6.7GHz (109.8%), and the isolation between antenna elements is below −18 dB. The envelope coefficient (ECC) parameter of the antenna is less than 0.004, the peak gain is 7 dB, and the radiation efficiency is 90%. The results show that the antenna has high isolation and good performance, and can be applied to wireless applications such as N41/N77/N78/N7 (5G-NR) sub-6 GHz/Wi-Fi-5/V2X/DSRC. [ABSTRACT FROM AUTHOR]

Traditionally, recommender systems modelled the physical and cyber contextual influence on people’s moving, querying, and browsing behaviors in isolation. Yet, searching, querying, and moving behaviors are intricately linked, especially indoors. Here, we introduce a tripartite location-query-browse graph (LQB) for nuanced contextual recommendations. The LQB graph consists of three kinds of nodes: locations, queries, and Web domains. Directed connections only between heterogeneous nodes represent the contextual influences, while connections of homogeneous nodes are inferred from the contextual influences of the other nodes. This tripartite LQB graph is more reliable than any monopartite or bipartite graph in contextual location, query, and Web content recommendations. We validate this LQB graph in an indoor retail scenario with extensive dataset of three logs collected from over 120,000 anonymized, opt-in users over a 1-year period in a large inner-city mall in Sydney, Australia. We characterize the contextual influences that correspond to the arcs in the LQB graph, and evaluate the usefulness of the LQB graph for location, query, and Web content recommendations. The experimental results show that the LQB graph successfully captures the contextual influence and significantly outperforms the state of the art in these applications. [ABSTRACT FROM PUBLISHER]

• Deformation behaviors of large diameter steel tubes from a floating offshore wind tube (FOWT) against impact loads were investigated by experiments and numerical simulations. • Global motions of the tubes in the experiments were accounted for by a single-degree-of-freedom model for the rigid body motions of the FOWT. • Impact force-deformation curves, tube deformation patterns and energy dissipation were compared and discussed. • A nondimensional force-deformation curve was recommended for the design of large diameter steel tubes under impact loads. Large diameter steel tubes are widely used in bottom fixed and floating offshore wind turbines. Offshore wind turbines (OWTs) operating in the oceans are exposed to the risk of collisions when ships pass and dock at turbines. Thus, it is extremely important to investigate the impact mechanics of ship-OWT collisions and propose practical designs for methods to protect OWTs from collision loads. This paper presents a series of experimental and numerical studies on the deformation behaviors of large diameter steel tubes from a NREL 5 MW spar-type floating offshore wind turbine (FOWT) under lateral impact loads. These studies consider the effects of different impact velocities, attached masses, diameters and thicknesses of the tubes on their response to impact loading. In these experiments, a rigid indenter was mounted on a pendulum system and accelerated to strike the tubes; the scale was 1:30. The dimensions of the indenter head were much smaller than the tube diameter in order to concentrate the impact load. Global motions of the impacted tubes were modeled by springs introduced at the boundaries, and their stiffnesses were determined according to an equivalent single-degree-of-freedom (SDOF) model. Numerical simulations of the experiments were conducted using the nonlinear finite element (FE) software LS-DYNA. The experimental and numerical results were compared and discussed with respect to force-deformation curves, deformation modes and energy dissipation. Existing theoretical solutions for the lateral indentation resistance of tubes were also compared to the experimental and numerical simulation data. The results indicated needs for new solutions when the impact loads become concentrated. [ABSTRACT FROM AUTHOR]

We analyze 18- million rows of Wi- Fi access logs collected over a 1-year period from over 120,000 anonymized users at an inner city shopping mall. The anonymized data set gathered from an opt-in system provides users' approximate physical location as well as web browsing and some search history. Such data provide a unique opportunity to analyze the interaction between people's behavior in physical retail spaces and their web behavior, serving as a proxy to their information needs. We found that (a) there is a weekly periodicity in users' visits to the mall; (b) people tend to visit similar mall locations and web content during their repeated visits to the mall; (c) around 60% of registered Wi- Fi users actively browse the web, and around 10% of them use Wi- Fi for accessing web search engines; (d) people are likely to spend a relatively constant amount of time browsing the web while the duration of their visit may vary; (e) the physical spatial context has a small, but significant, influence on the web content that indoor users browse; and (f) accompanying users tend to access resources from the same web domains. [ABSTRACT FROM AUTHOR]

• The collision between a spar-type floating offshore wind turbine (FOWT) and ship is numerically investigated by the nonlinear finite element (FEM) method. • Model verification of finite element model is performed for both FOWT and ship. • The collision forces, displacements and accelerations of FOWT with different impact velocities are obtained. • The incurred structural damage of FOWT and ship with different impact velocities is evaluated. • The energy conversion ratio between FOWT and ship subjected to collision is obtained. This paper investigated the dynamic responses and the incurred structural damage of a 5 MW spar-type floating offshore wind turbine (FOWT) and a ship when they collide. Ansys/LS-DYNA was used to establish the finite element models (FEMs) of the FOWT and the ship and to subsequently conduct the collision analysis. The global dynamics of the FOWT are captured well by the FEM comparing with FAST code and a simplified 8-degree of freedom (8-DOF) model established in MATLAB. The validity of the ship FEM was guaranteed by the fact that the model outputs agree well with empirical formulas. A series of simulations were then carried out using the FEMs to analyze the impact forces, displacements, accelerations, damages and energy dissipation during the ship collision for various impact velocities. The results show that the peak collision force, peak spar platform displacement and peak tower top displacement increase almost linearly with the initial velocity of the ship. The maximum tower top acceleration during the collision consistently exceeds 0.2 g, severely affecting the normal operation of the FOWT. The collision causes dents on the spar platform rather than the collapse of the whole wind turbine. After the collision, the initial kinetic energy of the ship is mainly transformed into the plastic deformation energy of the FOWT and the energy dissipated in the water. In addition, assuming a rigid ship bow results in accurate FOWT dynamic responses but fails to provide a good evaluation of the spar platform deformation. [ABSTRACT FROM AUTHOR]

Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N movie recommendations. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N movie recommendations in terms of accuracy. [ABSTRACT FROM AUTHOR]

Abstract: The Information Bottleneck principle provides a systematic method to extract relevant features from complex data sets, and it models features extraction as data compression and quantifies the relevance of extracted feature by how much information it preserved about a specified feature. How to construct an optimal solution to IB remains a problem. The current Information Bottleneck (IB) algorithms only utilize the information between element pairs, and ignore the information among the neighborhood of elements. This is one of the major reasons for most IB algorithms’ failure to preserve as much relative information as possible, which further limits IB applicability in many areas. In this paper, we present the concept of density connectivity component, by which the information loss among the neighbors of an element, rather than the information loss between paired elements, can be considered. Then, we introduce this concept into the current agglomerative IB algorithm (aIB) and sequential IB algorithm (sIB), and propose two density-based IB algorithms, DaIB and DsIB. The experiment results on the benchmark data sets indicate that the DaIB and DsIB algorithm can preserve more relevant information and achieve higher precision than the aIB and sIB algorithm, respectively. [ABSTRACT FROM AUTHOR]

The travelling officer problem (TOP) is a graph-based orienteering problem for modelling the patrolling routines of a parking officer monitoring an area. Recently, a spatiotemporal probabilistic model was built for TOP to estimate the leaving probability of parking cars, and relevant algorithms were applied to search for the optimal path for a parking officer to maximize the collection of parking fines from cars in violation. However, there are often multiple parking officers on duty during business hours in the central business district, which provides us with the opportunities to introduce cooperation among officers for efficient car-parking management. The multiple travelling officers problem (MTOP) is a more complex problem than the TOP because multiple officers are involved simultaneously in paths construction. In this study, the MTOP is formulated and new components are established for solving the problem. One essential component called the leader-based random-keys encoding scheme (LERK) is developed for the representation of possible solutions. Then, cuckoo search (CS), genetic algorithm (GA) and particle swarm optimization (PSO) are implemented using the proposed components and compared with other state-of-the-art GA and PSO using other solution encoding schemes to solve MTOP. In addition, two greedy selection algorithms are adopted as baselines. The performance of the algorithms is evaluated with real parking sensors data and different metrics. The experimental results show that the performance of CS and GA using LERK is considerably improved in comparison with that of other implemented algorithms. [ABSTRACT FROM AUTHOR]

The ever increasing size of graphs makes them difficult to query and store. In this paper, we present Shrink , a compression method that reduces the size of the graph while preserving the distances between the nodes. The compression is based on the iterative merging of the nodes. During each merging, a system of linear equations is solved to define new edge weights in a way that the new weights have the least effect on the distances. Merging nodes continues until the desired size for the compressed graph is reached. The compressed graph, also known as the coarse graph, can be queried without decompression. As the complexity of distance-based queries such as shortest path queries is highly dependent on the size of the graph, Shrink improves the performance in terms of time and storage. Shrink not only provides the length of the shortest path but also identifies the nodes on the path. The approach has been applied to both weighted and unweighted graphs including road network, friendship network, collaboration network, web graph and social network. In the experiment, a road network with more than 2.5 million nodes is reduced to fifth while the average relative error is less than 1%. [ABSTRACT FROM AUTHOR]

The bus transit system is promising to enable electric and autonomous vehicles for massive urban mobility, which relies on high-level automation and efficient resource management. Besides the on-road automation, the in-depot automated scheduling for battery recharging has not been adequately studied yet. This paper presents an integrated in-depot routing and recharging scheduling (IDRRS) problem, which is modeled as a constraint programming (CP) problem with Boolean satisfiability conditions (SAT). The model is converted to a flexible job-shop problem (FJSP) and is feasible to be solved by a CP-SAT solver for the optimal solution or feasible solutions with acceptable performance. This paper also presents a case study in Shanghai and compares the results from the FJSP model and the first-come first-serve (FCFS) method. The result demonstrates the allocation of routes and chargers under multiple scenarios with different numbers of chargers. The results show that the FJSP model shortens the delay and increases the time conservation for future rounds of operation than FCFS, while FCFS presents the simplicity of programming and better computational efficiency. The multiple random input test suggests that the proposed approach can decide the minimum number of chargers for stochastic charging requests. The proposed method can conserve the investment by increasing the utilization of automated recharging devices, improving vehicles' in-depot efficiency. [ABSTRACT FROM AUTHOR]

This paper investigates how social images and change detection techniques can be applied to identify the damage caused by natural disasters for disaster assessment. We propose a multi-step image matching-based framework (MSIM), which takes advantages of fast clustering, near duplicate image detection and robust boundary matching for the change detection in disasters. We first model the social images by exploiting their tags and locations. After that, we propose a recursive 2-means algorithm over the new data model, and refine the near duplicate detection by local interest point-based matching over image pairs in neighboring clusters. Finally, we propose a novel boundary representation model called relative position annulus (RPA), which is robust to boundary rotation, location shift and editing operations. A new RPA matching method is proposed by extending dynamic time wrapping (DTW) from time to position annulus. We have done extensive experiments to evaluate the high effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]

• High-resolution FE models are developed and verified to reveal the fluid-structure interaction of ship-bridge collisions. • The influences of impact angles, impact positions, ship drafts and impact velocities on the hydrodynamic effect are explored. • Limitations in the current codes are identified for ship-bridge collisions considering the hydrodynamic effect. • An improved formula considering the hydrodynamic effect and impact position is proposed to estimate absorbed energy in oblique collisions. The hydrodynamic effect in ship-bridge collisions is usually considered by the constant added mass (CAM) method in current studies. However, no research to date has examined its applicability, particularly for oblique collisions. To this end, high-fidelity physics-based finite-element (FE) models are developed in this study to investigate the fluid-structure interaction (FSI) behavior during ship-bridge collisions with different impact angles. The FSI modeling method based on the LS-DYNA code is examined and verified by the potential flow solver WADAM. The hydrodynamic effect predicted by the developed FSI model is consistent with the WADAM result. The verified FSI models are used to assess the influences of several key parameters (e.g., impact angles, impact positions, ship drafts and impact velocities) on the hydrodynamic effect for different ship-bridge collision scenarios. It is found that the ship-bridge collision process obtained from the FSI simulation is different from that of the CAM method for oblique collision scenarios. The CAM method is unsuitable for oblique collisions because it is unable to capture the hydrodynamic effect properly. Also, the FSI simulations indicate that the current design specifications underestimate the impact force and the dissipated energy in oblique collisions. An analytical formula is improved to estimate the energy absorbed by either the ship or the bridge in oblique collisions. Good agreements are achieved between the FSI analysis results and the improved formula. [ABSTRACT FROM AUTHOR]

Digital Assistants (DAs) can support workers in the workplace and beyond. However, target user needs are not fully understood, and the functions that workers would ideally want a DA to support require further study. A richer understanding of worker needs could help inform the design of future DAs. We investigate user needs of future workplace DAs using data from a user study of 40 workers over a four-week period. Our qualitative analysis confirms existing research and generates new insight on the role of DAs in managing people's time, tasks, and information. Placing these insights in relation to quantitative analysis of self-reported task data, we highlight how different occupation roles require DAs to take varied approaches to these domains and the effect of task characteristics on the imagined features. Our findings have implications for the design of future DAs in work settings and we offer some recommendations for reduction to practice. • This paper aims to better understand the need for Digital Assistants (DAs) to support daily task management in work-related settings. • Demonstrating the perceived utility of Digital Assistants that support user time, tasks, and information by target users. • Different occupation roles require varied support domains from Digital Assistant • Task characteristics affect the Digital Assistant's features that users desire. [ABSTRACT FROM AUTHOR]

Recommender systems attempt to suggest information that is of potential interest to users helping them to quickly find information relevant to them. In addition to historical user–item interaction data, such as users' ratings on items, social recommendation methods use social relationships between users to improve the accuracy of recommendations. However, the available social relationships are often extremely sparse. Therefore, incorporating implicit relationships into the recommendation process can be effective to improve the performance of social recommender systems, especially for those users whose explicit relationships are insufficient to make accurate recommendations. The existing approaches have not considered reliability of the implicit relationships. In this paper, a social recommender system is proposed based on reliable implicit relationships. To this end, Dempster–Shafer theory is used as a powerful mathematical tool to calculate the implicit relationships. Moreover, a new measure is introduced to evaluate the reliability of predictions, where unreliable predictions are recalculated using a neighborhood improvement mechanism. This mechanism uses a confidence measure between the users to identify ineffective users in the neighborhood set of a target user. Finally, new reliable ratings are calculated by removing the identified ineffective neighbors. Extensive experiments are conducted on three well-known datasets, and the results demonstrate that our approach achieves superior performance to the state-of-the-art recommendation methods. [ABSTRACT FROM AUTHOR]