A novel gateway node reconfiguration method of IOT based on hierarchical coding particle swarm optimization

In order to overcome the problems of low channel utilization, low transmission success rate and high data transmission delay in current gateway node reconfiguration methods of IOT, this paper proposes a novel gateway node reconfiguration method of IOT based on hierarchical coding particle swarm optimization. Based on the IOT network model, this paper analyzes the delay characteristics of the IOT, and constructs the object function of the gateway node reconfiguration of IOT. By monotone decreasing inertia weight strategy, the coding particle swarm optimization is optimized, and the reconfiguration objective function of the gateway node of IOT by using the optimized particle swarm optimization algorithm is solved. Experimental results show that the channel utilization ratio of the proposed method is higher than 90%, the success rate of information transmission is more than 80%, and the data transmission delay is less than 0.5 s, which indicates that the proposed method has high channel utilization, high transmission success rate and low data transmission delay.

What matters most to ultra-high-net-worth individuals? Exploring the UHNWI luxury customer experience (ULCX)

Purpose This study aims to explore the customer experiences (CXs) of an under-researched luxury client segment, the ultra-high-net-worth individuals (UHNWI) in three settings, yacht-made clothing services, chartering a yacht and art collection. Design/methodology/approach The author conducted 13 interviews with UHNWI, enquiring about their experiences with different services. The author collected and analyzed the data using a recommended three-step approach: in-depth interviews using soft-laddering; coding and purifying data through a systematic approach and hierarchical coding; and using the emerging consensus technique to scrutinize and validate the emerging themes. Findings This study revealed UHNWI drivers or purchasing and repurchasing behavior as (mis)managing expectations, personal relationships with personnel and achieving convenience-driven time savings. The corresponding conceptual framework is the UHNWI luxury CX. Practical implications This study reveals how über luxury brand managers need to carefully manage the UHNWI clientele expectations, focusing their investment on their brand personnel and the way they can save their clients’ valuable time. Originality/value This study is the first to explore UHNWI perceptions of their experience with über luxury providers across multiple contexts. This study highlights that the luxury experience, not the acquisition and owning of luxury goods, drives the UHNWI decision-making and purchase behavior.

Hybrid Particle Swarm Optimization Algorithm for Process Planning

Process planning is a typical combinatorial optimization problem. When the scale of the problem increases, combinatorial explosion occurs, which makes it difficult for traditional precise algorithms to solve the problem. A hybrid particle swarm optimization (HPSO) algorithm is proposed in this paper to solve problems of process planning. A hierarchical coding method including operation layer, machine layer and logic layer is designed in this algorithm. Each layer of coding corresponds to the decision of a sub-problem of process planning. Several genetic operators of the genetic algorithm are designed to replace the update formula of particle position and velocity in the particle swarm optimization algorithm. The results of the benchmark example in case study show that the algorithm proposed in this paper has better performance.

A structured open dataset of government interventions in response to COVID-19

In response to the COVID-19 pandemic, governments have implemented a wide range of nonpharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset on a weekly basis until the end of December 2020.