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Performance analysis for control- and user-plane separation based RAN with non-uniformly distributed users
No full textIn the control- and user-plane separation (CUPS) based radio access networks (RANs), control-signaling and data are transmitted by the control base stations (CBSs) and data base stations (DBSs), respectively. However, existing studies usually model the C/U-planes as two separate homogeneous networks, neglecting the dependence among the two planes and users. To address this problem, we analyze the coverage probability, spectrum efficiency (SE) and delay considering the dependent features among CBSs, DBSs, and non-uniformly distributed users based on stochastic geometry. Firstly, we present an analytical model for CUPS, where the DBSs are deployed at user hotspots based on Poisson point processes (PPPs), users are clustered around DBSs based on Poisson cluster processes (PCPs), and CBSs are deployed according to a dependent thinning of locations of DBSs based on Matérn hard-core processes (MHCPs). Secondly, we design novel distance-based fractional frequency reuse (FFR) schemes by exploiting the properties of PCP and MHCP to improve the coverage of cell edge users. Thirdly, we derive the distributions of user downlink rates, which are used to analyze the average queueing delay under M/M/C queueing model. Numerical results are presented to verify the efficiency of the proposed model compared to independently distributed BSs and users, and show the dependent BS deployment could significantly improve the coverage of the network
Work-in-progress : remote learning and online experimentation for large cohorts
No full textTeaching of Control and Automation usually faces multiple constraints such as the cost of hardware, limited access to the experimental setup, limitation in the parametrization of hardware etc. The emergence of digital technologies has allowed the development of cyber-physical systems. This paper explores the possibility of using a combination of physical hardware and virtual models to allow hybrid learning and practical experience with large cohorts of learners. The authors are currently exploring a multifaceted approach which is described as work in progress in this paper
Characterization on multiphase microstructures of carbon steels using multi-frequency electromagnetic measurements
No full textPhase composition is dominant in determining mechanical properties of carbon steels therefore is one of important microstructural elements that needs to be characterized and monitored in the steel production process. However, the characterization method usually used in steel production is off-line with destructive inspection. Microstructure characterization using electromagnetic signals can be applied in real time with in-line measurement that can meet requirements of steel continuous productions. The key is to establish the accurate electromagnetic responses to steel microstructure variations. This paper studied responses of electromagnetic signals on carbon steels with different phase compositions using a U-shaped and a cylindrical electromagnetic sensor. Relationships between steel microstructures and electromagnetic signals were established using the multi frequency electromagnetic system for steel samples with low to high carbon grades. Influences of phases, phase fractions, grain size and grain shapes on the relative permeability values were investigated. Results show that the low frequency inductance of electromagnetic signals can be used to distinguish the phase composition and the phase fraction of carbon steels. Effects of sensor lift-off distance and sample edge effects are also studied as requirements of industrial application
Computational discovery of superior vanadium niobates based cathode materials for next-generation all-solid-State lithium-ion battery applications
Get PDFAll-solid-state lithium-ion batteries (ASSLIBs) are at the forefront of green and sustainable energy development research. One of the key challenges in the development of ASSLIBs for commercial applications is to find cathode materials that have high capacity, voltage and power density. Using a combination of first-principles calculations and various crystal structure prediction algorithms, we explore the LiVO2-Li3NbO4 pseudobinary tieline to identify novel stoichiometries with improved properties as cathode materials for ASSLIB applications. Based on more than 10,000 Density Functional Theory (DFT + U) calculations using crystal structures obtained from ab initio random structure searching (AIRSS), genetic algorithm, and configuration enumeration procedures, we predict five novel stoichiometries, Li23Nb7V2O32, Li10Nb3VO14, Li7Nb2VO10, Li11Nb3V2O16, Li4NbVO6, along with an experimentally known stoichiometry, Li5NbV2O8. All the novel stoichiometries are found to have cation-disordered rock-salt crystal structures and fall within 30 meV/atom from the convex hull of the parent compositions. These new phases are predicted to have superior properties compared to the current vanadium-niobates-based electrode materials, including a higher theoretical capacity, lower band gap, higher average Li intercalation voltage, minor volume change upon full Li delithiation, good mechanical & dynamical stability, improved Li- conduction activation barrier and high-temperature stability. Our results are anticipated to inspire further experiments to synthesise and test these specific vanadium-niobate-based materials for their actual performance as Li-ion cathode materials
Investigation on the corrosion resistance of 3003 aluminum alloy in acidic salt spray under different processing states
Get PDF3003 aluminum alloy exhibits commendable corrosion resistance, ease of processing, and good formability, rendering it extensively utilized across many industrial sectors. In this study, the corrosion behavior of 3003 aluminum alloy in a homogenized state and after hot extrusion deformation in an acidic salt spray environment for different times was studied. The microstructure of the 3003 aluminum alloy in the homogenized state and after hot extrusion was characterized using scanning electron microscopy (SEM), optical microscope (OM), laser scanning confocal microscope (LSCM) etc., while electrochemical methods were employed to study the difference in corrosion resistance between these two states. The results show that corrosion pits on the surface of the homogenized 3003 aluminum alloy increase with time, and corrosion extends along the second phase arrangement, while the hot extruded 3003 aluminum alloy mainly exhibits corrosion pit extension. The grain size of the homogenized 3003 aluminum alloy is larger than that of the hot extruded state, and the second phase is distributed in a reticular pattern. Hot extrusion deformation ensures not only a uniform distribution of the second phase in the 3003 aluminum alloy but also a reduced grain size, an increased grain boundary density, a heightened electrochemical activity in acidic environments, and an augmented pitting density. Compared with the homogenized 3003 aluminum alloy, the pitting density, maximum pitting depth, and weight loss of the hot extruded state are increased
Participation in club sport in childhood is associated with mental health in preterm and term born adolescents
Get PDFPreterm born individuals have an increased risk for mental health problems. Participation in club sport is associated with better mental health but the causal direction is unclear. It is not known whether this association could also be found in preterm children. Data from term born (N = 10,368), late preterm (N = 630), and very to moderately preterm born (N = 243) children from the Millennium Cohort Study at the ages of 3, 5, 7, 11, and 14 years were used. Longitudinal associations between the parent-reported Strengths and Difficulties questionnaire (SDQ) and club sport participation (days per week) were analysed using multigroup structural equation modelling, adjusting for gender, maternal depression, parental education; motor problems and attrition were controlled for. Multi-group structural equation modelling showed that children with more peer relationship problems, emotional symptoms, conduct problems or hyperactivity-inattention were less likely to participate in club sport at subsequent assessment time points. More days with club sport participation was associated with lower levels of emotional symptoms and peer relationship problems but not conduct problems or hyperactivity-inattention at subsequent ages. Results were similar in all gestational age groups. Club sport participation predicts lower levels of peer relationship and emotional problems in subsequent waves while it is also predicted by lower levels of emotional problems, peer relationship problems, conduct problems and hyperactivity-inattention in preceding waves. Since no differences in the relationship between SDQ subscales and club sport participation were seen with regard to gestational age groups, club sport should be encouraged in all children
Attacking modulation recognition with adversarial federated learning in cognitive radio-enabled IoT
Get PDFInternet of Things (IoT) based on cognitive radio (CR) exhibits strong dynamic sensing and intelligent decision-making capabilities by effectively utilizing spectrum resources. The federal learning (FL) framework based modulation recognition (MR) is an essential component, but its use of uninterpretable deep learning (DL) introduces security risks. This paper combines traditional signal interference methods and data poisoning in FL to propose a new adversarial attack approach. The poisoning attack in distributed frameworks manipulates the global model by controlling malicious users, which is not only covert but also highly impactful. The carefully designed pseudo-noise in MR is also extremely difficult to detect. The combination of these two techniques can generate a greater security threat. We have further advanced our proposal with the introduction of the new adversarial attack method called "Chaotic Poisoning Attack" to reduce the recognition accuracy of the FL-based MR system. We establish effective attack conditions, and simulation results demonstrate that our method can cause a decrease of approximately 80% in the accuracy of the local model under weak perturbations and a decrease of around 20% in the accuracy of the global model. Compared to white-box attack methods, our method exhibits superior performance and transferability
Performance analysis of blockchain-enabled security and privacy algorithms in connected and autonomous vehicles : a comprehensive review
No full textStrategic investment(s) in vehicle automation technologies led to the rapid development of technology that revolutionised transport services and reduced fatalities on a scale never seen before. Technological advancements and their integration in Connected Autonomous Vehicles (CAVs) increased uptake and adoption and pushed firmly for the development of highly supportive legal and regulatory and testing environments. However, systemic threats to the security and privacy of technologies and lack of data transparency have created a dynamic threat landscape within which the establishment and verification of security and privacy requirements proved to be an arduous task. In CAVs security and privacy issues can affect the resilience of these systems and hinder the safety of the passengers. Existing research efforts have been placed to investigate the security issues in CAVs and propose solutions across the whole spectrum of cyber resilience. This paper examines the state-of-the-art in security and privacy solutions for CAVs. It investigates their integration challenges, drawbacks and efficiencies when coupled with distributed technologies such as Blockchain. It has also listed different cyber-attacks being investigated while designing security and privacy mechanism for CAVs
Short‐term psychosocial outcomes of adding a non‐contrast abdominal computed tomography (CT) scan to the thoracic CT within lung cancer screening
Get PDFObjectives
To evaluate psychological, social, and financial outcomes amongst individuals undergoing a non-contrast abdominal computed tomography (CT) scan to screen for kidney cancer and other abdominal malignancies alongside the thoracic CT within lung cancer screening.
Subjects and Methods
The Yorkshire Kidney Screening Trial (YKST) is a feasibility study of adding a non-contrast abdominal CT scan to the thoracic CT within lung cancer screening. A total of 500 participants within the YKST, comprising all who had an abnormal CT scan and a random sample of one-third of those with a normal scan between 14/03/2022 and 24/08/2022 were sent a questionnaire at 3 and 6 months. Outcomes included the Psychological Consequences Questionnaire (PCQ), the short-form of the Spielberger State–Trait Anxiety Inventory, and the EuroQoL five Dimensions five Levels scale (EQ-5D-5L). Data were analysed using regression adjusting for participant age, sex, socioeconomic status, education, baseline quality of life (EQ-5D-5L), and ethnicity.
Results
A total of 380 (76%) participants returned questionnaires at 3 months and 328 (66%) at 6 months. There was no difference in any outcomes between participants with a normal scan and those with abnormal scans requiring no further action. Individuals requiring initial further investigations or referral had higher scores on the negative PCQ than those with normal scans at 3 months (standardised mean difference 0.28 sd, 95% confidence interval 0.01–0.54; P = 0.044). The difference was greater in those with anxiety or depression at baseline. No differences were seen at 6 months.
Conclusion
Screening for kidney cancer and other abdominal malignancies using abdominal CT alongside the thoracic CT within lung cancer screening is unlikely to cause significant lasting psychosocial or financial harm to participants with incidental findings
TPGraph : A spatial-temporal graph learning framework for accurate traffic prediction on arterial roads
No full textThe accurate prediction of traffic conditions, including speed, flow, and travel time, poses a critical challenge in urbanization that significantly impacts car owners and road administrators. However, in certain scenarios with restricted road data availability (e.g. lack of traffic light status and signal control strategies, cooperation between road administrators and third parties, etc.), it is imperative to make effective use of basic road information (e.g. historical traffic data and road connectivity) to improve both prediction accuracy and scalability on various arterial road networks against state-of-art deep learning models. In this paper, we propose a spatial-temporal learning framework TPGraph for an accurate prediction of arterial roads’ traffic data by effectively utilizing upstream and downstream road information. TPGraph is composed of three major parts: 1) A multi-scale temporal feature fusion module that utilizes a multi-head attention mechanism to integrate recently-periodic features, daily-periodic features, and weekly-periodic features; 2) A multi-graph convolution module that employs graph fusion and graph convolution networks to capture richer spatial semantics, and 3) A dynamic spatial-temporal prediction module that leverages a spatial-temporal transformer for single or multiple traffic-state predictions. Our proposed framework, TPGraph, leverages just multi-scale historical traffic conditions and readily accessible spatial factors as input to generate accurate predictions of future traffic conditions. We mainly evaluate the performance of our approach through multi-step prediction experiments conducted at hourly intervals, forecasting travel time or travel speed for each road at 15 mins, 30 mins, and 1 hour. Furthermore, we conduct extensive experiments on real-world arterial road datasets to demonstrate the superior predictive performance of TPGraph compared to existing methods