5,872 research outputs found

    Boundary Spanner Corruption in Business Relationships

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    Boundary spanner corruption—voluntary collaborative behaviour between individuals representing different organisations that violates their organisations’ norms—is a serious problem in business relationships. Drawing on insights from the literatures on general corruption perspectives, the dark side of business relationships and deviance in sales and service organisations, this dissertation identifies boundary spanner corruption as a potential dark side complication inherent in close business relationships It builds research questions from these literature streams and proposes a research structure based upon commonly used methods in corruption research to address this new concept. In the first study, using an exploratory survey of boundary spanner practitioners, the dissertation finds that the nature of boundary spanner corruption is broad and encompasses severe and non-severe types. The survey also finds that these deviance types are prevalent in a widespread of geographies and industries. This prevalence is particularly noticeable for less-severe corruption types, which may be an under-researched phenomenon in general corruption research. The consequences of boundary spanner corruption can be serious for both individuals and organisations. Indeed, even less-severe types can generate long-term negative consequences. A second interview-based study found that multi-level trust factors could also motivate the emergence of boundary spanner corruption. This was integrated into a theoretical model that illustrates how trust at the interpersonal, intraorganisational, and interorganisational levels enables corrupt behaviours by allowing deviance-inducing factors stemming from the task environment or from the individual boundary spanner to manifest in boundary spanner corruption. Interpersonal trust between representatives of different organisations, interorganisational trust between these organisations, and intraorganisational agency trust of management in their representatives foster the development of a boundary-spanning social cocoon—a mechanism that can inculcate deviant norms leading to corrupt behaviour. This conceptualisation and model of boundary spanner corruption highlights intriguing directions for future research to support practitioners engaged in a difficult problem in business relationships

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Resilience and food security in a food systems context

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    This open access book compiles a series of chapters written by internationally recognized experts known for their in-depth but critical views on questions of resilience and food security. The book assesses rigorously and critically the contribution of the concept of resilience in advancing our understanding and ability to design and implement development interventions in relation to food security and humanitarian crises. For this, the book departs from the narrow beaten tracks of agriculture and trade, which have influenced the mainstream debate on food security for nearly 60 years, and adopts instead a wider, more holistic perspective, framed around food systems. The foundation for this new approach is the recognition that in the current post-globalization era, the food and nutritional security of the world’s population no longer depends just on the performance of agriculture and policies on trade, but rather on the capacity of the entire (food) system to produce, process, transport and distribute safe, affordable and nutritious food for all, in ways that remain environmentally sustainable. In that context, adopting a food system perspective provides a more appropriate frame as it incites to broaden the conventional thinking and to acknowledge the systemic nature of the different processes and actors involved. This book is written for a large audience, from academics to policymakers, students to practitioners

    Academic integrity : a call to research and action

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    Originally published in French:L'urgence de l'intĂ©gritĂ© acadĂ©mique, Éditions EMS, Management & société, Caen, 2021 (ISBN 978-2-37687-472-0).The urgency of doing complements the urgency of knowing. Urgency here is not the inconsequential injunction of irrational immediacy. It arises in various contexts for good reasons, when there is a threat to the human existence and harms to others. Today, our knowledge based civilization is at risk both by new production models of knowledge and by the shamelessness of knowledge delinquents, exposing the greatest number to important risks. Swiftly, the editors respond to the diagnostic by setting up a reference tool for academic integrity. Across multiple dialogues between the twenty-five chapters and five major themes, the ethical response shapes pragmatic horizons for action, on a range of disciplinary competencies: from science to international diplomacy. An interdisciplinary work indispensable for teachers, students and university researchers and administrators

    Two-Timescale Design for RIS-Aided Massive MIMO Systems

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    The emerging technology, reconfigurable intelligent surface (RIS), could support high data rate while maintaining low costs and energy consumption. Besides, it can constructively reflect the signal from the base station (BS) to users which helps solve the blockage problem in the urban area. Due to these benefits, RIS could be an energy-efficient and cost-effective complement to conventional massive multiple-input multiple-output (MIMO) systems. Focusing on the underload network in far-field outdoor scenarios with fixed users, this thesis investigates the theoretical performance and optimisation design of uplink RIS-aided massive MIMO systems under different detectors and different channel state information (CSI). A novel two-timescale transmission scheme is exploited where the BS detectors and RIS phase shifts are designed based on fast-changing instantaneous CSI and slow-changing statistical CSI, respectively, which achieves a good trade-off between the system performance and the channel estimation overhead. First, this thesis analyses the RIS-aided massive MIMO system with low-complexity maximal-ratio combination (MRC) detectors under the general Rician fading channel model. Closed-form expressions for the achievable rate are derived with blocked and unblocked direct links, based on which the power scaling laws, the rate scaling orders, and the impact of Rician factors are revealed, respectively. A genetic algorithm (GA)-based method is proposed for the design of the RIS phase shifts relying only on the statistical CSI. Simulation results demonstrate the benefit of integrating the RIS into conventional massive MIMO systems. Second, the RIS-aided massive MIMO system is investigated in the presence of the channel estimation error. Following the two-timescale strategy, a low-overhead channel estimation method is proposed to estimate the instantaneous aggregated CSI, whose quality and properties are analysed to shed light on the benefit brought by the RIS. With MRC detectors and the channel estimation results, the achievable rate is derived and a comprehensive framework for the power scaling laws with respect to the number of BS antennas and RIS elements is given. The superiority of the proposed two-timescale scheme over the instantaneous-CSI scheme is validated. Third, the more general scenario in the presence of spatial correlation and electromagnetic interference (EMI) is studied. The channel estimation result is revisited which shows that the RIS could play more roles with spatial correlation. Then, the closed-form expression of the achievable rate is derived and the negative impact of the EMI is analysed. To maximise the minimum user rate, the phase shifts of the RIS are designed based on an accelerated gradient ascent method, which has low computational complexity and relies only on the statistical CSI. Fourth, to solve the severe multi-user interference issue, a zero-forcing (ZF) detector-based design is considered for the RIS-aided massive MIMO system. After tackling the challenging matrix inversion operator, the closed-form ergodic rate expression is derived. Then, the promising properties of introducing ZF detectors into RIS-aided massive MIMO systems are revealed. Fifth and last, the RIS-aided massive MIMO system with ZF detectors and imperfect CSI is analysed. A minimum mean-squared error (MMSE) channel estimator is proposed and analysed. The closed-form expression of the ergodic rate is derived and two insightful upper and lower bounds are proposed, which unveil the rate scaling orders and prove that the considered structure is promising for enhanced mobile broadband, green communications, and the Internet of Things. Besides, both the sum user rate maximisation and the minimum user rate maximisation problems are solved based on the low-complexity majorization-minimisation (MM) algorithms

    2023-2024 Lynn University Academic Catalog

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    The 2023-2024 Academic Catalog initially published as a web-only document. The Department of Marketing and Communication created a PDF version, which is available for download here.https://spiral.lynn.edu/accatalogs/1052/thumbnail.jp

    Machine learning enabled millimeter wave cellular system and beyond

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    Millimeter-wave (mmWave) communication with advantages of abundant bandwidth and immunity to interference has been deemed a promising technology for the next generation network and beyond. With the help of mmWave, the requirements envisioned of the future mobile network could be met, such as addressing the massive growth required in coverage, capacity as well as traffic, providing a better quality of service and experience to users, supporting ultra-high data rates and reliability, and ensuring ultra-low latency. However, due to the characteristics of mmWave, such as short transmission distance, high sensitivity to the blockage, and large propagation path loss, there are some challenges for mmWave cellular network design. In this context, to enjoy the benefits from the mmWave networks, the architecture of next generation cellular network will be more complex. With a more complex network, it comes more complex problems. The plethora of possibilities makes planning and managing a complex network system more difficult. Specifically, to provide better Quality of Service and Quality of Experience for users in the such network, how to provide efficient and effective handover for mobile users is important. The probability of handover trigger will significantly increase in the next generation network, due to the dense small cell deployment. Since the resources in the base station (BS) is limited, the handover management will be a great challenge. Further, to generate the maximum transmission rate for the users, Line-of-sight (LOS) channel would be the main transmission channel. However, due to the characteristics of mmWave and the complexity of the environment, LOS channel is not feasible always. Non-line-of-sight channel should be explored and used as the backup link to serve the users. With all the problems trending to be complex and nonlinear, and the data traffic dramatically increasing, the conventional method is not effective and efficiency any more. In this case, how to solve the problems in the most efficient manner becomes important. Therefore, some new concepts, as well as novel technologies, require to be explored. Among them, one promising solution is the utilization of machine learning (ML) in the mmWave cellular network. On the one hand, with the aid of ML approaches, the network could learn from the mobile data and it allows the system to use adaptable strategies while avoiding unnecessary human intervention. On the other hand, when ML is integrated in the network, the complexity and workload could be reduced, meanwhile, the huge number of devices and data could be efficiently managed. Therefore, in this thesis, different ML techniques that assist in optimizing different areas in the mmWave cellular network are explored, in terms of non-line-of-sight (NLOS) beam tracking, handover management, and beam management. To be specific, first of all, a procedure to predict the angle of arrival (AOA) and angle of departure (AOD) both in azimuth and elevation in non-line-of-sight mmWave communications based on a deep neural network is proposed. Moreover, along with the AOA and AOD prediction, a trajectory prediction is employed based on the dynamic window approach (DWA). The simulation scenario is built with ray tracing technology and generate data. Based on the generated data, there are two deep neural networks (DNNs) to predict AOA/AOD in the azimuth (AAOA/AAOD) and AOA/AOD in the elevation (EAOA/EAOD). Furthermore, under an assumption that the UE mobility and the precise location is unknown, UE trajectory is predicted and input into the trained DNNs as a parameter to predict the AAOA/AAOD and EAOA/EAOD to show the performance under a realistic assumption. The robustness of both procedures is evaluated in the presence of errors and conclude that DNN is a promising tool to predict AOA and AOD in a NLOS scenario. Second, a novel handover scheme is designed aiming to optimize the overall system throughput and the total system delay while guaranteeing the quality of service (QoS) of each user equipment (UE). Specifically, the proposed handover scheme called O-MAPPO integrates the reinforcement learning (RL) algorithm and optimization theory. An RL algorithm known as multi-agent proximal policy optimization (MAPPO) plays a role in determining handover trigger conditions. Further, an optimization problem is proposed in conjunction with MAPPO to select the target base station and determine beam selection. It aims to evaluate and optimize the system performance of total throughput and delay while guaranteeing the QoS of each UE after the handover decision is made. Third, a multi-agent RL-based beam management scheme is proposed, where multiagent deep deterministic policy gradient (MADDPG) is applied on each small-cell base station (SCBS) to maximize the system throughput while guaranteeing the quality of service. With MADDPG, smart beam management methods can serve the UEs more efficiently and accurately. Specifically, the mobility of UEs causes the dynamic changes of the network environment, the MADDPG algorithm learns the experience of these changes. Based on that, the beam management in the SCBS is optimized according the reward or penalty when severing different UEs. The approach could improve the overall system throughput and delay performance compared with traditional beam management methods. The works presented in this thesis demonstrate the potentiality of ML when addressing the problem from the mmWave cellular network. Moreover, it provides specific solutions for optimizing NLOS beam tracking, handover management and beam management. For NLOS beam tracking part, simulation results show that the prediction errors of the AOA and AOD can be maintained within an acceptable range of ±2. Further, when it comes to the handover optimization part, the numerical results show the system throughput and delay are improved by 10% and 25%, respectively, when compared with two typical RL algorithms, Deep Deterministic Policy Gradient (DDPG) and Deep Q-learning (DQL). Lastly, when it considers the intelligent beam management part, numerical results reveal the convergence performance of the MADDPG and the superiority in improving the system throughput compared with other typical RL algorithms and the traditional beam management method
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