Cross-layer distributed power control: A repeated games formulation to improve the sum energy-efficiency

The main objective of this work is to improve the energy-efficiency (EE) of a multiple access channel (MAC) system, through power control, in a distributed manner. In contrast with many existing works on energy-efficient power control, which ignore the possible presence of a queue at the transmitter, we consider a new generalized cross-layer EE metric. This approach is relevant when the transmitters have a non-zero energy cost even when the radiated power is zero and takes into account the presence of a finite packet buffer and packet arrival at the transmitter. As the Nash equilibrium (NE) is an energy-inefficient solution, the present work aims at overcoming this deficit by improving the global energy-efficiency. Indeed, as the considered system has multiple agencies each with their own interest, the performance metric reflecting the individual interest of each decision maker is the global energy-efficiency defined then as the sum over individual energy-efficiencies. Repeated games (RG) are investigated through the study of two dynamic games (finite RG and discounted RG), whose equilibrium is defined when introducing a new operating point (OP), Pareto-dominating the NE and relying only on individual channel state information (CSI). Accordingly, closed-form expressions of the minimum number of stages of the game for finite RG (FRG) and the maximum discount factor of the discounted RG (DRG) were established. The cross-layer model in the RG formulation leads to achieving a shorter minimum number of stages in the FRG even for higher number of users. In addition, the social welfare (sum of utilities) in the DRG decreases slightly with the cross-layer model when the number of users increases while it is reduced considerably with the Goodman model. Finally, we show that in real systems with random packet arrivals, the cross-layer power control algorithm outperforms the Goodman algorithm.Comment: 36 pages, single column draft forma

A particle swarm optimization approach for predicting the number of COVID-19 deaths

The rapid spread of the COVID-19 pandemic has raised huge concerns about the prospect of a major health disaster that would result in a huge number of deaths. This anxiety was largely fueled by the fact that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the disease, was so far unknown, and therefore an accurate prediction of the number of deaths was particularly difficult. However, this prediction is of the utmost importance for public health authorities to make the most reliable decisions and establish the necessary precautions to protect people's lives. In this paper, we present an approach for predicting the number of deaths from COVID-19. This approach requires modeling the number of infected cases using a generalized logistic function and using this function for inferring the number of deaths. An estimate of the parameters of the proposed model is obtained using a Particle Swarm Optimization algorithm (PSO) that requires iteratively solving a quadratic programming problem. In addition to the total number of deaths and number of infected cases, the model enables the estimation of the infection fatality rate (IFR). Furthermore, using some mild assumptions, we derive estimates of the number of active cases. The proposed approach was empirically assessed on official data provided by the State of Qatar. The results of our computational study show a good accuracy of the predicted number of deaths. 2021, The Author(s).Scopu

Brain atrophy patterns in multiple sclerosis patients treated with natalizumab and its clinical correlates

BACKGROUND: Multiple sclerosis (MS) is defined as a demyelinating disorder of the central nervous system, witnessing over the past years a remarkable progress in the therapeutic approaches of the inflammatory process. Yet, the ongoing neurodegenerative process is still ambiguous, under‐assessed, and probably under‐treated. Atrophy and cognitive dysfunction represent the radiological and clinical correlates of such process. In this study, we evaluated the effect of one specific MS treatment, which is natalizumab (NTZ), on brain atrophy evolution in different anatomical regions and its correlation with the cognitive profile and the physical disability. METHODS: We recruited 20 patients diagnosed with relapsing‐remitting MS (RR‐MS) and treated with NTZ. We tracked brain atrophy in different anatomical structures using MRI scans processed with an automated image segmentation technique. We also assessed the progression of physical disability and the cognitive function and its link with the progression of atrophy. RESULTS: During the first 2 years of treatment, a significant volume loss was noted within the corpus callosum and the cerebellum gray matter (GM). The annual atrophy rate of the cortical GM, the cerebellum GM, the thalamus, the amygdala, the globus pallidus, and the hippocampus correlated with greater memory impairment. As for the third and fourth years of treatment, a significant atrophy revolved around the gray matter, mainly the cortical one. We also noted an increase of the thalamus volume. CONCLUSION: Atrophy in RR‐MS patients treated with NTZ is regional and targeting highly cognitive regions mainly of the subcortical gray matter and the cerebellum. The cerebellum atrophy was a marker of physical disability progression. NTZ did not accelerate the atrophy process in MS and may play a neuroprotective role by increasing the thalamus volume

Particularites de l’epilepsie au cours des maladies inflammatoires du systeme nerveux central

Introduction : Les crises épileptiques (CE) font partie des manifestations neurologiques des maladies inflammatoires (MI). Elles constituent un tournant évolutif grave de la maladie. Objectifs : Nous avons évalué les particularités sémiologiques, électriques, radiologiques, thérapeutiques et évolutives de l’épilepsie au cours des MI du système nerveux central (SNC). Nous avons également discuté les mécanismes physiopathologiques de l’épilepsie ainsi que les facteurs prédictifs de survenue de CE chez ces patients. Méthodes : C’est une étude rétrospective incluant les patients suivis pour épilepsie dans le cadre d’une MI du SNC. Tous nos patients ont bénéficié d’une imagerie cérébrale. Résultat : Nous avons colligé 32 patients (11 avec sclérose en plaque, 6 avec maladie de Behcet et 15 avec lupus érythémateux disséminé). Le délai des CE au cours des MI était de 3,2 ans. Elles étaient généralisées dans 62,5 % des cas. L’IRM a montré des lésions sous corticales et des lésions du tronc cérébral respectivement dans 71,8 % et 25% des cas. Une thrombose veineuse cérébrale était diagnostiquée chez 3 malades. L’EEG a objectivé des ondes lentes dans 34% des cas, et des anomalies paroxystiques chez 3 patients. Le phénobarbital était le traitement le plus prescrit. Le contrôle des CE était obtenu dans la majorité des cas. Conclusion : La survenue des CE au cours des MI pose un problème de prise en charge. Un diagnostic précoce et un traitement de l’épilepsie permettent de contrôler ces crises afin d’éviter les états de mal épileptiques qui mettent en jeu le pronostic vital des patients. Mots clés: Epilepsie, Facteurs de risque, Maladies inflammatoires  Particularities of epilepsy associated with inflammatory diseases of the central nervous systemIntroduction: The frequency of the central nervous system involvement in autoimmune disorders is very variable. Seizures are among the most common neurological manifestations, and can be occasionally the presenting symptom.Methods: All files of 32 patients with autoimmune disorder diagnosed with epilepsy were evaluated retrospectively (11 with multiple sclerosis, 6 with Behcet disease, and 15 systemic lupus erythematosus). The demographic data, clinical findings including seizures, EEG and neuroimaging findings were reviewed. Results: The sex ratio was 0.45 (10H / 22F). Seizures started 3.2 years after the onset of the inflammatory diseases. They were during either the first or following neurological attacks in 68.7% of cases. 20 patients (62,5%) had only generalized tonic-clonic seizures. Brain magnetic resonance imaging (MRI) was performed to all patients. Sub-cortical and brainstem lesions were identified respectively in 71,8 % and 25%. MRI revealed cerebral sinus thrombosis in three patients. The EEG revealed focal epileptiform discharges in three patients. In 12 patients (34%) slow waves were seen. Antiepileptic drugs were prescribed in all cases (phenobarbital :53%, valproic acid: 31%, Carbamazepine: 15%). A sufficient control of seizures was obtained in most cases. Conclusion: Seizures often complicate systemic autoimmune disorders through a variety of mechanisms. A better understanding of the mechanisms of epileptogenesis in those patients could lead to targeted treatments and better outcomes. Key words: Epilepsy, inflammatory disease, risk factor

PTPA variants and impaired PP2A activity in early-onset parkinsonism with intellectual disability

The protein phosphatase 2A complex (PP2A), the major Ser/Thr phosphatase in the brain, is involved in a number of signalling pathways and functions, including the regulation of crucial proteins for neurodegeneration, such as alpha-synuclein, tau and LRRK2. Here, we report the identification of variants in the PTPA/PPP2R4 gene, encoding a major PP2A activator, in two families with early-onset parkinsonism and intellectual disability. We carried out clinical studies and genetic analyses, including genome-wide linkage analysis, whole-exome sequencing, and Sanger sequencing of candidate variants. We next performed functional studies on the disease-associated variants in cultured cells and knock-down of ptpa in Drosophila melanogaster. We first identified a homozygous PTPA variant, c.893T&gt;G (p.Met298Arg), in patients from a South African family with early-onset parkinsonism and intellectual disability. Screening of a large series of additional families yielded a second homozygous variant, c.512C&gt;A (p.Ala171Asp), in a Libyan family with a similar phenotype. Both variants co-segregate with disease in the respective families. The affected subjects display juvenile-onset parkinsonism and intellectual disability. The motor symptoms were responsive to treatment with levodopa and deep brain stimulation of the subthalamic nucleus. In overexpression studies, both the PTPA p.Ala171Asp and p.Met298Arg variants were associated with decreased PTPA RNA stability and decreased PTPA protein levels; the p.Ala171Asp variant additionally displayed decreased PTPA protein stability. Crucially, expression of both variants was associated with decreased PP2A complex levels and impaired PP2A phosphatase activation. PTPA orthologue knock-down in Drosophila neurons induced a significant impairment of locomotion in the climbing test. This defect was age-dependent and fully reversed by L-DOPA treatment. We conclude that bi-allelic missense PTPA variants associated with impaired activation of the PP2A phosphatase cause autosomal recessive early-onset parkinsonism with intellectual disability. Our findings might also provide new insights for understanding the role of the PP2A complex in the pathogenesis of more common forms of neurodegeneration.</p

A blockchain‐based framework to optimize shipping container flows in the hinterland

Abstract We address two interrelated issues affecting the hinterland portion of the maritime container supply chain: reducing the movement of empty containers and reducing empty trips by trucks carrying these containers. In this paper, we show that empty container flow optimization can be implemented via a blockchain based on the proof‐of‐useful‐work concept where the proof of work requires the solution of an ‐hard optimization problem whose solution benefits the blockchain participants. Accordingly, we propose that anonymous miners compete to solve the container truck routing problem, which seeks to find the most efficient routes for trucks. We show that this problem is ‐hard. Miners must also solve the problem of optimally matching consignees and shippers, which will reduce transportation and storage costs for empty containers. In essence, the proposed framework turns blockchain into a massive optimization engine that directly benefits the hinterland container supply chain ecosystem

A novel proof of useful work for a blockchain storing transportation transactions

Proof-of-Work (PoW) is a common mechanism used to validate peer-to-peer transactions and maintain highly secured immutability of the blockchain. However, this mechanism has been criticized due to its inefficient use of computing resources and its limited usefulness. In this paper, we propose the Proof-of-Useful-Work (PoUW) as an alternative mechanism for transaction validation that puts the squandered computing resources to beneficial use. The main premise is to replace the mathematical puzzle, which constitutes a fundamental part of the Proof-of-Work mechanism, with NP-hard optimization problems whose solutions benefit the participants of the blockchain. We demonstrate its usefulness in the context of transportation. Accordingly, PoUW-based blockchain not only tracks, manages and validates transactions, but also optimizes transportation requests profiting its ecosystem. We describe the framework of the proposed PoUW along with the associated optimization model and the miner's reward mechanism

Energy-Efficient Spectrum Sharing in Relay-Assisted Cognitive Radio Systems (Invited Paper)

Abstract—This work characterizes an important solution concept of a relevant spectrum game. Two energy-efficient sources communicating with their respective destination compete for an extra channel brought by a relay charging the used bandwidth through a pricing mechanism. This game is shown to possess a unique Nash bargaining solution, exploiting a time-sharing argument. This Pareto-efficient solution can be implemented by using a distributed optimization algorithm for which each transmitter uses a simple gradient-type algorithm and alternately updates its spectrum sharing policy. Typical numerical results show to what extent spectral efficiency can be improved in a system involving selfish energy-efficient sources

Exact and heuristic approaches for maximizing flows in UAV-enabled wireless cellular networks with multi-hop backhauls

This paper investigates the problem of data routing in backhaul networks using Unmanned Aerial Vehicles (UAVs) to relay data from Small Cells (SCs) to the core network. The objective is to maximize the total demand of data to be routed, while ensuring technical requirements such as hop constraints and edge capacity. The problem is formulated using a compact mixed-integer programming model, which can solve small- and medium-sized topologies. In addition, a fast constructive heuristic based on a maximal tree is developed to solve large-scale topologies, resulting in a significant reduction in CPU time. The quality of the heuristic is evaluated by using column generation for solving the linear programming relaxation of an exponential formulation. The computational study shows the effectiveness and value of the proposed compact model and constructive heuristic for various topology sizes. Furthermore, experiments demonstrates that by keeping the network setup constant and updating the demand vector only, the computational time of the compact model can be drastically reduced for all topology sizes