Better Executive Functions Are Associated With More Efficient Cognitive Pain Modulation in Older Adults: An fMRI Study

in Frontiers in Pain Research (2022), 14

Energy-Efficient Resource Allocation for 6G Backscatter-Enabled NOMA IoV Networks

in IEEE Transactions on Intelligent Transportation Systems (2022), 23(07), 9775-9785

The integration of Ambient Backscatter Communication (AmBC) with Non-Orthogonal Multiple Access (NOMA) is expected to support connectivity of low-powered Internet-of-Vehicles (IoVs) in the upcoming Sixth ... [more ▼]

The integration of Ambient Backscatter Communication (AmBC) with Non-Orthogonal Multiple Access (NOMA) is expected to support connectivity of low-powered Internet-of-Vehicles (IoVs) in the upcoming Sixth-Generation (6G) transportation systems. This paper proposes an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network under imperfect Successive Interference Cancellation (SIC) decoding. In particular, multiple Road-Side Units (RSUs) transmit superimposed signals to their associated IoVs utilizing downlink NOMA transmission. Meanwhile, the Backscatter Tags (BackTags) also transmit data symbols towards nearby IoVs by reflecting the superimposed signals of RSUs. Thus, the objective is to maximize the total energy efficiency of the NOMA IoV network subject to the minimum data rate of all IoVs. A joint problem that simultaneously optimizes the total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags under imperfect SIC decoding is formulated. A Dinkelbach approach is first adopted to transform the optimization problem and then the transformed problem is decoupled into two subproblems for optimal transmit power at RSUs and efficient reflection power at BackTags, respectively. To solve the problems efficiently, dual theory and Karush-Kuhn-Tucker conditions are exploited, where the Lagrangian dual variables are iteratively calculated using the subgradient method. To check the performance of the proposed framework, a benchmark optimization without AmBC is also provided. Numerical results demonstrate the superiority of the proposed AmBC-enabled NOMA IoV framework over the benchmark conventional IoV framework. [less ▲]

Commercialisation en France d’un FIA non autorisé : un manquement à l’obligation d’agir dans l’intérêt des clients

in Banque et Droit (2022)

Sustainable Corporate Governance in the EU: Reasonable Global Ambitions?

in Revue Européenne du Droit (2022), 3(4), 111

Towards real-time patient-specific breast simulations: from full-field information to surrogate model

Scientific Conference (2022, July)

In breast cancer treatment, surgery is one of the most common practices [DeSantis et al., 2019]. The surgery involves a complex pipeline, principally due to the difference between the imaging and the ... [more ▼]

In breast cancer treatment, surgery is one of the most common practices [DeSantis et al., 2019]. The surgery involves a complex pipeline, principally due to the difference between the imaging and the surgical posture [Mazier et al., 2021]. Indeed, because of the stance difference, the surgeon has to rely on radioactive or invasive markers to predict the tumor position in the surgical setup. Biomechanical simulations could predict such complex tumor displacements but often require patient-specific data (material properties, organs geometries, or loading and boundary conditions). Full-field acquisitions coupled with landmark identifications allow obtaining relative deformation between the different configurations. Having this information and assuming a finite element model, an identification procedure of the model parameters can be carried out. Finally, finding a suitable computational model allowing for a compromise between accuracy and speed, one may consider surrogate models for real-time simulations (20 to 50 FPS). In this work, we obtained the patient-specific geometry through micro-computed tomography in 8 different configurations, including 15 bio-markers. Assessing the displacement of the bio-markers enabled us to infer the relative strains between the different configurations. A heterogeneous neo-Hookean model was assumed for simulating soft tissue behavior. Based on the displacements and the position of the biomarkers, model parameters identification was performed to calibrate the experimental data with the finite element method results. To overcome speed performance issues, Convolutional Neural Network (CNN) trained with a synthetic simulation-based dataset generated by applying different gravity directions is used. Preliminary results show that CNN can predict the displacement of anatomical landmarks to millimetric precision and is 100 times faster than the finite element method, satisfying our real-time objective. Plus, the use of Bayesian inferences involves a longer prediction time but allows a 95% confidence interval of the biomarkers' displacements. For a given precision, contrary to CNNs, optimization methods are computationally expensive and depend on an initialization point. Although CNNs require new training for each patient, optimization algorithms can be applied regardless of the patient's geometry. Through this study, we observed that material properties were playing an essential role but not as much as the anatomical structures e.g. infra-mammary or Copper’s ligaments. [less ▲]

Transformation der Grenze und konzeptionelle Aufbrüche

Presentation (2022, June 30)

REPORT OF DATA FUSION AND EVALUATION

Report (2022)

The third deliverable for the SCRIPT Project

Performance Evaluation of Spectral Efficiency for Uplink and Downlink Multi-Cell Massive MIMO Systems

in Journal of Sensors (2022)

Massive multiple-input and multiple-output (MIMO) systems have become the most persuasive technology for 5G as it increased the energy efficiency gigantically as compared to other wireless communication ... [more ▼]

Massive multiple-input and multiple-output (MIMO) systems have become the most persuasive technology for 5G as it increased the energy efficiency gigantically as compared to other wireless communication systems. Being the most vibrant research technology in the communication sector, this research work is based on the optimal model development of energy-efficient massive MIMO systems. The proposed model is a realistic model that augmented the spectral efficiency (SE) of massive MIMO systems where a multi-cell model scenario is considered. Channel estimation is carried out at the base stations (BSs) based on uplink (UL) transmission while the minimum mean-squared error (MMSE), Element-wise MMSE, and Least-square (LS) estimators are used for the estimation. We analyze the achievable SE of the UL based on the MMSE channel estimator with different receive combining schemes. Moreover, the downlink (DL) transmission model is also modelled with different precoding schemes by taking the same vectors used in combining schemes. The simulation results show a significant improvement in spectral efficiency by developing UL and DL transmission models and also realized that the average sum of SE per cell can be improved by optimized MMSE channel estimation, installing multiple BS antennas, and serving multiple UEs per cell. The findings of this work specify that the massive MIMO system can be developed by optimizing the channel estimation for the augmentation of SE in UL and DL transmissions. Conclusively, it can be summarized that some complex computations of MMSE channel estimators can enhance the average sum of SE per cell as per the results verified in this model. [less ▲]

When the “vulnerable” talk back: subjective responses and individual experiences of young refugees

Scientific Conference (2022, June 30)

In the context of international protection and reception, an increasing amount of interest has been given to the assessment of vulnerability among forced migrants in order to provide special provisions ... [more ▼]

In the context of international protection and reception, an increasing amount of interest has been given to the assessment of vulnerability among forced migrants in order to provide special provisions and guarantees to ‘vulnerable refugees’. However, a common definition of vulnerability does not exist. The systematisation of individuals’ experiences in pre-defined bureaucratic categories risks ignoring the contextual and contingent nature of migrants’ vulnerability, the subjective and biographical differences among those considered vulnerable and the agency of each individual facing conditions of vulnerability. Thus, in the context of the H2020 project MIMY, this paper proposes to explore the experiences and feelings of the people living in an apparent condition of vulnerability. Based on in-depth interviews with young adults who obtained refugee status in Luxembourg but still live in ‘temporary’ reception centres, the current study wants to move beyond the bureaucratic vulnerability label, questioning the relationship between structural, contingent and material conditions of vulnerability and the subjective experiences and responses of the active agents who occupy them. Additionally, bringing the stories and perspectives of young refugees living in Luxembourg to the forefront, the study points to the variability of vulnerability at the individual level in the context of integration. We propose to analyse such plurality of stories through the lens of agency and temporality, as the unique product of past personal experiences, current situations of difficulty and conditions of(in)security (based on the interrelationship between obstacles and resources) and individual future perspectives and feelings of self-efficacy. [less ▲]

Recent Developments and Overview of the Country and Practitioner’s Reports

in European Data Protection Law Review (2022), 8(2), 250-254