A study of talent management in the context of Chinese private-owned enterprises

Talent Management (TM) is still a new research field in China’s context. TM like other management disciplines is often embedded in a certain institutional context and influenced by certain social norms, cultural factors and government policies. TM is therefore closely related to research context. The Chinese cultural context is influential in shaping TM concepts and practices. However, how TM practices are used to retain talents in the context of Chinese private-owned enterprises (POEs) is still a research gap. TM is a holistic strategy for an organisation. It is therefore necessary to explore Chinese POEs’ TM practices through a holistic lens, which covers the processes of defining talents, attracting talents, developing talents and retaining talents. Previous TM studies mainly focused on exploring TM concepts and there is a lack of empirical investigations on TM practices, especially in the context of Chinese POEs. Linked to the characteristics of Chinese POEs and Chinese cultural context, this study explores talent retention from a holistic perspective of TM.This research adopted an interpretivist perspective and inductive approach. Based on a case study research method, primary and secondary data were collected from three case companies, and analysed qualitatively.The theoretical framework used in this research is largely based on TM literature. Talent attraction, talent development, and talent retention are used as a lens to view the case companies and to explore what TM activities drive talent retention in the context of Chinese POEs. Organisational commitment is the main theory employed by this research to explore talented individuals’ turnover intention.Findings show that Chinese guanxi is an important perspective to define talented employees in the context of Chinese POEs. Competence, position, and guanxi are holistically considered to define a talented employee. It was found that guanxi is an important factor influencing the entire TM process, including attracting talents, developing talents and retaining talents. Career development, rewards, and guanxi were significant factors in retaining talented employees in the context of Chinese POEs. Guanxi as a new TM perspective not only makes a theoretical contribution to talent definition but also contributes to talent development and talent retention theories. The research offers practical talent retention suggestions to TM practitioners. A significant practical contribution may be adopting TM practices to develop talented employees’ guanxi ties to increase their organisational commitment and to reduce turnover intention

Resource allocation, user association and placement for uav-assisted communications

In the past few years, unmanned aerial vehicle (UAV)-assisted heterogeneous network has attracted significant attention due to its wide range of applications, such as disaster rescue and recovery, ground macro base station (MBS) traffic offloading, communications for temporary events, and data collection for further processing in Internet of Things (IoT). A UAV can act as a flying base station (BS) to quickly recover the communication coverage in the disaster area when the regular terrestrial infrastructure is malfunctioned. The UAV-assisted heterogeneous network can effectively provision line of sight (LoS) communication links and therefore can mitigate potential signal shadowing and blockage. The regulation relaxation and cost reduction of UAVs as well as communication equipment miniaturization make the practical deployment of highly mobile wireless relays more feasible than before. In fact, the 3GPP Rel-16 has included UAV-enabled wireless communications in the new radio standard, aiming to boost capacity and coverage of fifth generation (5G) wireless networks. However, the performance of UAV-assisted communications is greatly affected by the resource allocation scheme, user association policy and the UAV placement strategy. Also, the limited on-board energy and flight time of the UAV poses a great challenge on designing a robust and reliable UAV-enabled IoT network. To maximize the throughput in the UAV-assisted mobile access network, an optimization problem which determines the 3D UAV deployment and resource allocation in a given hotspot area under the constraints of user Quality of Service (QoS) requirements and total available resources is formulated. First, the primal problem is decomposed into two subproblems, i.e., the 3D UAV placement problem and the resource allocation problem. Second, a cyclic iterative algorithm which solves the two sub-problems separately and uses the output of one as the input of the other is proposed. An optimization problem that aims to minimize the average latency ratio of all users is formulated by determining the 3D location of the UAV, the user association and the bandwidth allocation policy between the MBS and the drone base station (DBS) with the constraint of each user’s QoS requirement and total available bandwidth. The formulated problem is a mixed integer non-convex optimization problem, a very challenging and difficult problem. To make formulated problem tractable, it is decomposed into two subproblems, i.e., the user association and bandwidth allocation problem and the 3D DBS placement problem. These two subproblems are alternatively optimized until no performance improvement can be further achieved. To address the challenge of limited on-board battery capacity and flight time, a tethered UAV (TUAV)-assisted heterogeneous network where the aerial UAV is connected with a ground charging station (GCS) through a tether is proposed. The objective of the formulated problem is to maximize the sum rate of all users by jointly optimizing the user association, resource allocation and placement of the GCSs and the aerial UAVs, constrained by each user’s QoS requirement and the total available resource. Since the primal problem is highly non-convex and non-linear and thus challenging to solve, it is decomposed into three subproblems, i.e., the TUAV placement problem, the resource allocation problem and the user association problem. Then, the three sub-problems are alternately and iteratively optimized by using the outputs of the first two as the input for the third. The future work comprises two parts. First, IoT devices usually are generally deployed at remote areas with limited battery capacities and computing power. Therefore, the generated data needs to be offloaded to a more powerful computing server for further processing. Unfortunately, the trajectory design in UAV data collection is generally NP-hard and difficult to obtain the optimal solution. Advances of machine learning (ML) provide a promising alternative approach to solve such problems that cannot be solved by traditional optimization methods. Hence, deep reinforcement learning (DRL) is proposed to be explored to obtain a near optimal solution. Second, the low earth orbit (LEO) satellite networks will revolutionize traditional communication networks with their promising benefits of service continuity, wide-area coverage, and availability for critical communications and emerging applications. However, the integration of LEO satellite networks and terrestrial networks will be another future research endeavor

Versatile Fabrication Methods for Wearable Thermo-electrochemical Cells

Emerging markets for wearable electronics have stimulated a rapidly growing demand for the commercialisation of flexible and reliable energy storage and conversion units, which includes batteries, supercapacitors, and thermo-electrochemical cells (thermocell). Among them, thermocell have attracted significant research attention in recent years owing to their ability to continuously convert body heat into electrical energy. The commercial viability of wearable thermocells has long been limited by their low power output and complex fabrication methods. Great progress has been made in developing flexible electrode materials, gel electrolytes and encapsulation materials. However, it is still a main challenge to develop flexible electrodes on a various-scale and in a cost-effective manner. At present, carbon electrodes are commonly fabricated using a vacuum filtration method. However, fine carbon nanoparticles can be drawn through the filter paper pores via suction force, resulting in blockage of the filtrate and wastage of the ink materials. Meanwhile, this fabrication method is time consuming and reduces the overall fabrication efficiency of devices. Therefore, additional fabrication methods exert pressure on developing simple and scalable techniques, such as laser-etching, 3D printing, and drop coating. The main goal for this study is to fabricate high performance electrodes for wearable thermocell devices in simple and scalable ways. A wearable poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) (PEDOT/PSS or PEDOT: PSS) based film was firstly prepared via a laser-etching method and assembled to be wearable all-solid-state thermocell. For applications of this wearable thermocell, a flexible watch-strap shaped thermocell that could harvest body heat, charge supercapacitors, and light a green LED. This systematic investigation and optimization of 3D structured laser-etched electrode, gel electrolyte, and device architectures introduced has great significance for the realization of body heat harvesting and application in real self-powered wearable electronics

Transformed Schatten-1 Iterative Thresholding Algorithms for Low Rank Matrix Completion

We study a non-convex low-rank promoting penalty function, the transformed Schatten-1 (TS1), and its applications in matrix completion. The TS1 penalty, as a matrix quasi-norm defined on its singular values, interpolates the rank and the nuclear norm through a nonnegative parameter a. We consider the unconstrained TS1 regularized low-rank matrix recovery problem and develop a fixed point representation for its global minimizer. The TS1 thresholding functions are in closed analytical form for all parameter values. The TS1 threshold values differ in subcritical (supercritical) parameter regime where the TS1 threshold functions are continuous (discontinuous). We propose TS1 iterative thresholding algorithms and compare them with some state-of-the-art algorithms on matrix completion test problems. For problems with known rank, a fully adaptive TS1 iterative thresholding algorithm consistently performs the best under different conditions with ground truth matrix being multivariate Gaussian at varying covariance. For problems with unknown rank, TS1 algorithms with an additional rank estimation procedure approach the level of IRucL-q which is an iterative reweighted algorithm, non-convex in nature and best in performance