Showing papers by "Northumbria University published in 2020"

Multicell MIMO Communications Relying on Intelligent Reflecting Surfaces

Abstract: Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS’s power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the non-convexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance.

Partial least squares structural equation modeling in HRM research

Abstract: Partial least squares structural equation modeling (PLS-SEM) has become a key multivariate analysis technique that human resource management (HRM) researchers frequently use. While most disciplines...

Dealing with sleep problems during home confinement due to the COVID-19 outbreak: Practical recommendations from a task force of the European CBT-I Academy.

Abstract: In the current global home confinement situation due to the COVID-19 outbreak, most individuals are exposed to an unprecedented stressful situation of unknown duration. This may not only increase daytime stress, anxiety and depression levels, but also disrupt sleep. Importantly, because of the fundamental role that sleep plays in emotion regulation, sleep disturbance can have direct consequences upon next day emotional functioning. In this paper, we summarize what is known about the stress-sleep link and confinement as well as effective insomnia treatment. We discuss those effects of the current home confinement situation that can disrupt sleep but also those that could benefit sleep quality. We suggest adaptions of cognitive behavioural therapy elements that are feasible to implement for those facing changed work schedules and requirements, those with health anxiety and those handling childcare and home-schooling, whilst also recognizing the general limitations imposed on physical exercise and social interaction. Managing sleep problems as best as possible during home confinement can limit stress and possibly prevent disruptions of social relationships.

Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet

Abstract: The Antarctic ice sheet has been losing mass over past decades through the accelerated flow of its glaciers, conditioned by ocean temperature and bed topography. Glaciers retreating along retrograde slopes (that is, the bed elevation drops in the inland direction) are potentially unstable, while subglacial ridges slow down the glacial retreat. Despite major advances in the mapping of subglacial bed topography, significant sectors of Antarctica remain poorly resolved and critical spatial details are missing. Here we present a novel, high-resolution and physically based description of Antarctic bed topography using mass conservation. Our results reveal previously unknown basal features with major implications for glacier response to climate change. For example, glaciers flowing across the Transantarctic Mountains are protected by broad, stabilizing ridges. Conversely, in the marine basin of Wilkes Land, East Antarctica, we find retrograde slopes along Ninnis and Denman glaciers, with stabilizing slopes beneath Moscow University, Totten and Lambert glacier system, despite corrections in bed elevation of up to 1 km for the latter. This transformative description of bed topography redefines the high- and lower-risk sectors for rapid sea level rise from Antarctica; it will also significantly impact model projections of sea level rise from Antarctica in the coming centuries.

A Framework of Robust Transmission Design for IRS-Aided MISO Communications With Imperfect Cascaded Channels

Abstract: Intelligent reflection surface (IRS) has recently been recognized as a promising technique to enhance the performance of wireless systems due to its ability of reconfiguring the signal propagation environment. However, the perfect channel state information (CSI) is challenging to obtain at the base station (BS) due to the lack of radio frequency (RF) chains at the IRS. Since most of the existing channel estimation methods were developed to acquire the cascaded BS-IRS-user channels, this paper is the first work to study the robust beamforming based on the imperfect cascaded BS-IRS-user channels at the transmitter (CBIUT). Specifically, the transmit power minimization problems are formulated subject to the worst-case rate constraints under the bounded CSI error model, and the rate outage probability constraints under the statistical CSI error model, respectively. After approximating the worst-case rate constraints by using the S-procedure and the rate outage probability constraints by using the Bernstein-type inequality, the reformulated problems can be efficiently solved. Numerical results show that the negative impact of the CBIUT error on the system performance is greater than that of the direct CSI error.

Intelligent Reflecting Surface Aided MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer

Abstract: An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternately optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications.

Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems

Abstract: Intelligent reflecting surface (IRS) has recently been envisioned to offer unprecedented massive multiple-input multiple-output (MIMO)-like gains by deploying large-scale and low-cost passive reflection elements. By adjusting the reflection coefficients, the IRS can change the phase shifts on the impinging electromagnetic waves so that it can smartly reconfigure the signal propagation environment and enhance the power of the desired received signal or suppress the interference signal. In this paper, we consider downlink multigroup multicast communication systems assisted by an IRS. We aim for maximizing the sum rate of all the multicasting groups by the joint optimization of the precoding matrix at the base station (BS) and the reflection coefficients at the IRS under both the power and unit-modulus constraint. To tackle this non-convex problem, we propose two efficient algorithms under the majorization–minimization (MM) algorithm framework. Specifically, a concave lower bound surrogate objective function of each user's rate has been derived firstly, based on which two sets of variables can be updated alternately by solving two corresponding second-order cone programming (SOCP) problems. Then, in order to reduce the computational complexity, we derive another concave lower bound function of each group's rate for each set of variables at every iteration, and obtain the closed-form solutions under these loose surrogate objective functions. Finally, the simulation results demonstrate the benefits in terms of the spectral and energy efficiency of the introduced IRS and the effectiveness in terms of the convergence and complexity of our proposed algorithms.

Artificial-Noise-Aided Secure MIMO Wireless Communications via Intelligent Reflecting Surface

Abstract: This article considers an artificial noise (AN)-aided secure MIMO wireless communication system. To enhance the system security performance, the advanced intelligent reflecting surface (IRS) is invoked, and the base station (BS), legitimate information receiver (IR) and eavesdropper (Eve) are equipped with multiple antennas. With the aim for maximizing the secrecy rate (SR), the transmit precoding (TPC) matrix at the BS, covariance matrix of AN and phase shifts at the IRS are jointly optimized subject to constrains of transmit power limit and unit modulus of IRS phase shifts. Then, the secrecy rate maximization (SRM) problem is formulated, which is a non-convex problem with multiple coupled variables. To tackle it, we propose to utilize the block coordinate descent (BCD) algorithm to alternately update the variables while keeping SR non-decreasing. Specifically, the optimal TPC matrix and AN covariance matrix are derived by Lagrangian multiplier method, and the optimal phase shifts are obtained by Majorization-Minimization (MM) algorithm. Since all variables can be calculated in closed form, the proposed algorithm is very efficient. We also extend the SRM problem to the more general multiple-IRs scenario and propose a BCD algorithm to solve it. Simulation results validate the effectiveness of system security enhancement via an IRS.

The Effects of Menstrual Cycle Phase on Exercise Performance in Eumenorrheic Women: A Systematic Review and Meta-Analysis

Abstract: Concentrations of endogenous sex hormones fluctuate across the menstrual cycle (MC), which could have implications for exercise performance in women. At present, data are conflicting, with no consensus on whether exercise performance is affected by MC phase. To determine the effects of the MC on exercise performance and provide evidence-based, practical, performance recommendations to eumenorrheic women. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Four databases were searched for published experimental studies that investigated the effects of the MC on exercise performance, which included at least one outcome measure taken in two or more defined MC phases. All data were meta-analysed using multilevel models grounded in Bayesian principles. The initial meta-analysis pooled pairwise effect sizes comparing exercise performance during the early follicular phase with all other phases (late follicular, ovulation, early luteal, mid-luteal and late luteal) amalgamated. A more comprehensive analysis was then conducted, comparing exercise performance between all phases with direct and indirect pairwise effect sizes through a network meta-analysis. Results from the network meta-analysis were summarised by calculating the Surface Under the Cumulative Ranking curve (SUCRA). Study quality was assessed using a modified Downs and Black checklist and a strategy based on the recommendations of the Grading of Recommendations Assessment Development and Evaluation (GRADE) working group. Of the 78 included studies, data from 51 studies were eligible for inclusion in the initial pairwise meta-analysis. The three-level hierarchical model indicated a trivial effect for both endurance- and strength-based outcomes, with reduced exercise performance observed in the early follicular phase of the MC, based on the median pooled effect size (ES0.5 = − 0.06 [95% credible interval (CrI): − 0.16 to 0.04]). Seventy-three studies had enough data to be included in the network meta-analysis. The largest effect was identified between the early follicular and the late follicular phases of the MC (ES0.5 = − 0.14 [95% CrI: − 0.26 to − 0.03]). The lowest SUCRA value, which represents the likelihood that exercise performance is poor, or among the poorest, relative to other MC phases, was obtained for the early follicular phase (30%), with values for all other phases ranging between 53 and 55%. The quality of evidence for this review was classified as “low” (42%). The results from this systematic review and meta-analysis indicate that exercise performance might be trivially reduced during the early follicular phase of the MC, compared to all other phases. Due to the trivial effect size, the large between-study variation and the number of poor-quality studies included in this review, general guidelines on exercise performance across the MC cannot be formed; rather, it is recommended that a personalised approach should be taken based on each individual's response to exercise performance across the MC.

Pylons ablaze: Examining the role of 5G COVID-19 conspiracy beliefs and support for violence.

Abstract: Amid increased acts of violence against telecommunication engineers and property, this pre-registered study (N = 601 Britons) investigated the association between beliefs in 5G COVID-19 conspiracy theories and the justification and willingness to use violence. Findings revealed that belief in 5G COVID-19 conspiracy theories was positively correlated with state anger, which in turn, was associated with a greater justification of real-life and hypothetical violence in response to an alleged link between 5G mobile technology and COVID-19, alongside a greater intent to engage in similar behaviours in the future. Moreover, these associations were strongest for those highest in paranoia. Furthermore, we show that these patterns are not specific to 5G conspiratorial beliefs: General conspiracy mentality was positively associated with justification and willingness for general violence, an effect mediated by heightened state anger, especially for those most paranoid in the case of justification of violence. Such research provides novel evidence on why and when conspiracy beliefs may justify the use of violence.

Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs).

Abstract: Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

Robust Beamforming Design for Intelligent Reflecting Surface Aided MISO Communication Systems

Abstract: Perfect channel state information (CSI) is challenging to obtain due to the limited signal processing capability at the intelligent reflection surface (IRS). This is the first work to study the worst-case robust beamforming design for an IRS-aided multiuser multiple-input single-output (MU-MISO) system under the assumption of imperfect CSI. We aim for minimizing the transmit power while ensuring that the achievable rate of each user meets the quality of service (QoS) requirement for all possible channel error realizations. With unit-modulus and rate constraints, this problem is non-convex. The imperfect CSI further increases the difficulty of solving this problem. By using approximation and transformation techniques, we convert the optimization problem into a squence of semidefinite program (SDP) subproblems that can be efficiently solved. Numerical results show that the proposed robust beamforming design can guarantee the required QoS targets for all the users.

Secure Communications for UAV-Enabled Mobile Edge Computing Systems

Abstract: In this paper, we propose a secure unmanned aerial vehicle (UAV) mobile edge computing (MEC) system where multiple ground users offload large computing tasks to a nearby legitimate UAV in the presence of multiple eavesdropping UAVs with imperfect locations. To enhance security, jamming signals are transmitted from both the full-duplex legitimate UAV and non-offloading ground users. For this system, we design a low-complexity iterative algorithm to maximize the minimum secrecy capacity subject to latency, minimum offloading and total power constraints. Specifically, we jointly optimize the UAV location, users’ transmit power, UAV jamming power, offloading ratio, UAV computing capacity, and offloading user association. Numerical results show that our proposed algorithm significantly outperforms baseline strategies over a wide range of UAV self-interference (SI) efficiencies, locations and packet sizes of ground users. Furthermore, we show that there exists a fundamental tradeoff between the security and latency of UAV-enabled MEC systems which depends on the UAV SI efficiency and total UAV power constraints.

A new threat from an old enemy: Re­emergence of coronavirus (Review).

Abstract: The new outbreak of coronavirus from December 2019 has brought attention to an old viral enemy and has raised concerns as to the ability of current protection measures and the healthcare system to handle such a threat. It has been known since the 1960s that coronaviruses can cause respiratory infections in humans; however, their epidemic potential was understood only during the past two decades. In the present review, we address current knowledge on coronaviruses from a short history to epidemiology, pathogenesis, clinical manifestation of the disease, as well as treatment and prevention strategies. Although a great amount of research and efforts have been made worldwide to prevent further outbreaks of coronavirus‑associated disease, the spread and lethality of the 2019 outbreak (COVID‑19) is proving to be higher than previous epidemics on account of international travel density and immune naivety of the population. Only strong, joint and coordinated efforts of worldwide healthcare systems, researchers, and pharmaceutical companies and receptive national leaders will succeed in suppressing an outbreak of this scale.

Joint Deployment and Task Scheduling Optimization for Large-Scale Mobile Users in Multi-UAV-Enabled Mobile Edge Computing

Abstract: This article establishes a new multiunmanned aerial vehicle (multi-UAV)-enabled mobile edge computing (MEC) system, where a number of unmanned aerial vehicles (UAVs) are deployed as flying edge clouds for large-scale mobile users. In this system, we need to optimize the deployment of UAVs, by considering their number and locations. At the same time, to provide good services for all mobile users, it is necessary to optimize task scheduling. Specifically, for each mobile user, we need to determine whether its task is executed locally or on a UAV (i.e., offloading decision), and how many resources should be allocated (i.e., resource allocation). This article presents a two-layer optimization method for jointly optimizing the deployment of UAVs and task scheduling, with the aim of minimizing system energy consumption. By analyzing this system, we obtain the following property: the number of UAVs should be as small as possible under the condition that all tasks can be completed. Based on this property, in the upper layer, we propose a differential evolution algorithm with an elimination operator to optimize the deployment of UAVs, in which each individual represents a UAV’s location and the entire population represents an entire deployment of UAVs. During the evolution, we first determine the maximum number of UAVs. Subsequently, the elimination operator gradually reduces the number of UAVs until at least one task cannot be executed under delay constraints. This process achieves an adaptive adjustment of the number of UAVs. In the lower layer, based on the given deployment of UAVs, we transform the task scheduling into a 0-1 integer programming problem. Due to the large-scale characteristic of this 0-1 integer programming problem, we propose an efficient greedy algorithm to obtain the near-optimal solution with much less time. The effectiveness of the proposed two-layer optimization method and the established multi-UAV-enabled MEC system is demonstrated on ten instances with up to 1000 mobile users.

Spectral and Energy Efficiency of IRS-Assisted MISO Communication With Hardware Impairments

Abstract: In this letter, we analyze the spectral and energy efficiency of an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) downlink system with hardware impairments. An extended error vector magnitude (EEVM) model is utilized to characterize the impact of radio-frequency (RF) impairments at the access point (AP) and phase noise is considered at the IRS. We show that the spectral efficiency is limited due to the hardware impairments even when the numbers of AP antennas and IRS elements grow infinitely large, which is in contrast with the conventional case with ideal hardware. Moreover, the performance degradation at high SNR is shown to be mainly affected by the AP hardware impairments rather than by the phase noise at the IRS. We further obtain in closed form the optimal transmit power for energy efficiency maximization. Simulation results are provided to verify the obtained results.

The cognitive consequences of the COVID-19 epidemic: collateral damage?

Abstract: Recovery from coronavirus disease 2019 (COVID-19) will be principally defined in terms of remission from respiratory symptoms; however, both clinical and animal studies have shown that coronaviruses may spread to the nervous system. A systematic search on previous viral epidemics revealed that while there has been relatively little research in this area, clinical studies have commonly reported neurological disorders and cognitive difficulties. Little is known with regard to their incidence, duration or underlying neural basis. The hippocampus appears to be particularly vulnerable to coronavirus infections, thus increasing the probability of post-infection memory impairment, and acceleration of neurodegenerative disorders such as Alzheimer's disease. Future knowledge of the impact of COVID-19, from epidemiological studies and clinical practice, will be needed to develop future screening and treatment programmes to minimize the long-term cognitive consequences of COVID-19.

Fabrication and investigation on ternary heterogeneous MWCNT@TiO2-C fillers and their silicone rubber wave-absorbing composites

Abstract: Ternary heterogeneous MWCNT@TiO2-C wave absorbent was firstly prepared, using glucose, MWCNT, and titanium isopropoxide as raw materials, through the solvothermal process followed by post-heat treatment. Afterwards, MWCNT@TiO2-C/silicone rubber wave-absorbing composites were fabricated via solution casting and subsequent curing process. XRD, Raman, XPS, and TEM analyses demonstrated the MWCNT@TiO2-C fillers were successfully synthesized with TiO2 and amorphous carbon coated on the surface of MWCNT. When the MWCNT@TiO2-C/silicone rubber wave-absorbing composites contained 25 wt% MWCNT@TiO2-C fillers and with the thickness of 2.5 mm, it displayed the minimum reflection loss of −53.2 dB and an effective absorption bandwidth of 3.1 GHz. Remarkable wave-absorbing performances for MWCNT@TiO2-C/silicone rubber composites could be attributed to the synergetic effect of interfacial polarization loss and conduction loss.

More than smell. COVID-19 is associated with severe impairment of smell, taste, and chemesthesis

Abstract: Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, generally lacked quantitative measurements, were mostly restricted to data from single countries. Here, we report the development, implementation and initial results of a multi-lingual, international questionnaire to assess self-reported quantity and quality of perception in three distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, 8 other, ages 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change+/-100) revealed a mean reduction of smell (-79.7+/- 28.7, mean+/- SD), taste (-69.0+/- 32.6), and chemesthetic (-37.3+/- 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell, but also affects taste and chemesthesis. The multimodal impact of COVID-19 and lack of perceived nasal obstruction suggest that SARS-CoV-2 infection may disrupt sensory-neural mechanisms.

Rethinking Financial Contagion: Information Transmission Mechanism During the COVID-19 Pandemic.

Abstract: Rapidly growing numbers of empirical papers assessing the financial effects of COVID-19 pandemic triggered an urgent need for a study summarising the existing knowledge of contagion phenomenon. This paper provides a review of conceptual approaches to studying financial contagion at four levels of information transmission: (i) Catalyst of contagion; (ii) Media Attention; (iii) Spillover effect at financial markets; (iv) Macroeconomic fundamentals. We discuss the unique characteristics of COVID-19 crisis and demonstrate how this shock differs from previous crises and to what extent the COVID-19 pandemic can be considered a ‘black swan’ event. We also review the main concepts, definitions and methodologies that are frequently, but inconsistently, used in contagion literature to unveil the existing problems and ambiguities in this popular area of research. This paper will help researchers to conduct coherent and methodologically rigorous research on the impact of COVID-19 on financial markets during the pandemic and its aftermath.

Progress in biomimetic leverages for marine antifouling using nanocomposite coatings

Abstract: Because of the environmental and economic casualties of biofouling on maritime navigation, modern studies have been devoted toward formulating advanced nanoscale composites in the controlled development of effective marine antifouling self-cleaning surfaces. Natural biomimetic surfaces have the advantages of micro-/nanoroughness and minimized free energy characteristics that can motivate the dynamic fabrication of superhydrophobic antifouling surfaces. This review provides an architectural panorama of the biomimetic antifouling designs and their key leverages to broaden horizons in the controlled fabrication of nanocomposite building blocks as force-driven marine antifouling models. As primary antifouling designs, understanding the key functions of surface geometry, heterogeneity, superhydrophobicity, and complexity of polymer/nanofiller composite building blocks on fouling-resistant systems is crucial. This review also discusses a wide range of fouling release coating systems that satisfy the growing demand in a sustainable future environment. For instance, the integration of block, segmented copolymer-based coatings and inorganic-organic hybrid nanofillers enhanced the model's antifouling properties with mechanical, superhydrophobic, chemically inert, and robust surfaces. These nanoscale antifouling systems offered surfaces with minimized free energy, micro-/nanoroughness, anisotropic heterogeneity, superior hydrophobicity, tunable non-wettability, antibacterial efficiency, and mechanical robustness. The confined fabrication of nanoscale orientation, configuration, arrangement, and direction along the architectural composite building blocks would yield excellent air-entrapping ability along the interfacial surface grooves and interfaces, which optimized the antifouling coating surfaces for long-term durability. This review provides systematic evidence of the effect of structurally folded nanocomposites, nanofiller tectonics, and building blocks on the creation of outstanding superhydrophobicity, self-cleaning surfaces, and potential antifouling coatings. The development of modern research gateways is a candidate for the sustainable future of antifouling coatings.

Acceptance of autonomous delivery vehicles for last-mile delivery in Germany – Extending UTAUT2 with risk perceptions

Abstract: The inevitable need to develop new delivery practices in last-mile logistics arises from the enormously growing business to consumer (B2C) e-commerce and the associated challenges for logistics service providers. Autonomous delivery vehicles (ADVs) are believed to have the potential to revolutionise last-mile delivery in a way that is more sustainable and customer focused. However, if not widely accepted, the introduction of ADVs as a delivery option can be a substantial waste of resources. At present, the research on consumers’ receptivity of innovations in last-mile delivery, such as ADVs, is limited. This study is the first that investigates the users’ acceptance of ADVs in Germany by utilising an extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and adapted it to the context of ADVs in last-mile delivery. Quantitative data was collected through an online survey approach (n = 501) and structural equation modelling was undertaken. The results indicate that price sensitivity is the strongest predictor of behavioural intention (i.e., user acceptance), followed by performance expectancy, hedonic motivation, perceived risk, social influence and facilitating conditions, whereas no effect could be found for effort expectancy. These findings have important theoretical and practical contributions in the areas of technology acceptance and last-mile delivery.

A hierarchical energy management system for multiple home energy hubs in neighborhood grids

Abstract: This paper presents a hierarchical energy management system (HEMS) for multiple home energy hubs in the neighborhood grid (MHEHNG). The main objectives are maximizing financial profit and shaving the peak of upstream grid. This way, the proposed HEMS manages the energy generation and energy storing, as well as energy purchase/sale of each home energy hub (HEH) under the two levels including lower and upper levels. The lower level is responsible for supplying the internal load and reducing the energy cost in each HEH. The upper level is the central energy management system (CEMS) which is focused on forming a coalition between the local HEHs, as well as giving the tempting offers to increase the financial profit through a heuristic bidding strategy. The principle of proposed bidding strategy is based on weighted distributing of excess power among consumers that is one of the contribution of this paper. It leads to trading more energy at the lowest possible price. Determining the most appropriate operational scenario in each HEH requires the investigation of both technical and financial aspects. A novel scenario selector method has been proposed based on SOC-tariff plane. This is another contribution of this work. A simulator has been implemented in the MATLAB/GUI software environment to facilitate the evaluation of proposed HEMS performance. The simulation results indicate the effectiveness of the proposed HEMS. They show a decrease in the total energy cost of the CBs by almost 9.4%, and an increase in the total profit of the HEHs by 4.55%. Also, it was found out that the energy can be purchased from HEHs at varying rates and can be sold to the consumers at almost constant rates by using the proposed bidding strategy. This motivates HEHs to submit more power at lower tariffs to the CEMS.