Statistical Mechanics of maximal independent sets

The graph theoretic concept of maximal independent set arises in several practical problems in computer science as well as in game theory. A maximal independent set is defined by the set of occupied nodes that satisfy some packing and covering constraints. It is known that finding minimum and maximum-density maximal independent sets are hard optimization problems. In this paper, we use cavity method of statistical physics and Monte Carlo simulations to study the corresponding constraint satisfaction problem on random graphs. We obtain the entropy of maximal independent sets within the replica symmetric and one-step replica symmetry breaking frameworks, shedding light on the metric structure of the landscape of solutions and suggesting a class of possible algorithms. This is of particular relevance for the application to the study of strategic interactions in social and economic networks, where maximal independent sets correspond to pure Nash equilibria of a graphical game of public goods allocation

Amplitudes With Different Helicity Configurations Of Noncommutative QED

The amplitudes of purely photonic and photon{2-fermion processes of non- commutative QED (NCQED) are derived for different helicity configurations of photons. The basic ingredient is the NCQED counterpart of Yang-Mills recursion relations by means of Berends and Giele. The explicit solutions of recursion relations for NCQED photonic processes with special helicity configurations are presented.Comment: 23 pages, 2 figure

Rare Collision Risk Estimation of Autonomous Vehicles with Multi-Agent Situation Awareness

This paper offers a formal framework for the rare collision risk estimation of autonomous vehicles (AVs) with multi-agent situation awareness, affected by different sources of noise in a complex dynamic environment. In our proposed setting, the situation awareness is considered for one of the ego vehicles by aggregating a range of diverse information gathered from other vehicles into a vector. We model AVs equipped with the situation awareness as general stochastic hybrid systems (GSHS) and assess the probability of collision in a lane-change scenario where two self-driving vehicles simultaneously intend to switch lanes into a shared one, while utilizing the time-to-collision measure for decision-making as required. Due to the substantial data requirements of simulation-based methods for the rare collision risk estimation, we leverage a multi-level importance splitting technique, known as interacting particle system-based estimation with fixed assignment splitting (IPS-FAS). This approach allows us to estimate the probability of a rare event by employing a group of interacting particles. Specifically, each particle embodies a system trajectory and engages with others through resampling and branching, focusing computational resources on trajectories with the highest probability of encountering the rare event. The effectiveness of our proposed approach is demonstrated through an extensive simulation of a lane-change scenario

Hookah smoking is strongly associated with diabetes mellitus, metabolic syndrome and obesity: a population-based study

Objectives The adverse effects of cigarette smoking have been widely studied before, whilst the effects of hookah smoking has received less attention, although it is a common habit in the Middle East. Here we have investigated the effects of cigarette and hookah smoking on biochemical characteristics in a representative population sample derived from the Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study, from Northeastern Iran. Study design A total of 9840 subjects from the MASHAD population study were allocated to five groups; non-smokers (6742), ex-smokers (976), cigarette smokers (864), hookah smokers (1067), concomitant cigarette and hookah smokers (41). Methods Baseline characteristics were recorded in a questionnaire. Biochemical characteristics were measured by routine methods. Data were analyzed using SPSS software and p < 0.05 was considered significant. Results After adjustment for age and sex; the presence of CVD, obesity, metabolic syndrome, DM and dyslipidemia were significantly (p < 0.001) related to smoking status. After multivariate analysis, HDL (p < 0.001), WBC (p < 0.001), MCV (p < 0.05), PLT (p < 0.01) and RDW (p < 0.001), and the presence of CVD (p < 0.01), obesity (p < 0.001), metabolic syndrome (p < 0.05) and DM (p < 0.01) remained significant between cigarette smokers and non-smokers. Between hookah smokers and non-smokers; uric acid (p < 0.001), PLT (p < 0.05) and RDW (p < 0.05), and the presence of obesity (p < 0.01), metabolic syndrome (p < 0.001), diabetes (p < 0.01) and dyslipidemia (p < 0.01) remained significant after logistic regression. Conclusion There was a positive association between hookah smoking and metabolic syndrome, diabetes, obesity and dyslipidemia which was not established in cigarette smoking

An enhanced contingency-based model for joint energy and reserve markets operation by considering wind and energy storage systems

This paper presents a contingency-based stochastic security-constrained unit commitment to address the integration of wind power producers to the joint energy and reserve markets. The contingency ranking is a popular method for reducing the computation burden of the unit commitment problem, but performing the contingency analysis changes the high-impact events in previous ranking methods. This paper employs an intelligent contingency ranking technique to address the above issue and to find the actual top-ranked outages based on the final solution. Also, energy storage systems are considered to evaluate the impact of the scheduling of storage under uncertainties. Numerical results on a six-bus and the IEEE 118-bus test systems show the effectiveness of the proposed approach. Furthermore, it shows that utilizing both wind farms and storage devices will reduce the total operational cost of the system, while the intelligent contingency ranking analysis and enough reserves ensure the security of power supply.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Preliminary Evidence of Acceptance and Commitment Therapy for Death Anxiety in Iranian Clients Diagnosed with OCD

This study investigated the effectiveness of Acceptance and Commitment Therapy (ACT) on death anxiety and obsessive-compulsive disorder (OCD) with eight adult females in Iran. The ACT protocol was conducted in 8 weekly solo sessions (45 minutes each). The results were analyzed by visual analysis method and improvement percentage. ACT resulted in decreases in death anxiety (60-80%) and obsessive-compulsive symptoms (51-60%), thereby indicating promise for ACT as a treatment for OCD and death anxiety

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches. MethodsRNA-Seq data from peripheral blood mononuclear cells (PSPRINGER NATUREs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules. ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis. ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic

Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers

AIM To evaluate a deep learning-based time-of-flight (DLToF) model trained to enhance the image quality of non-ToF PET images for different tracers, reconstructed using BSREM algorithm, towards ToF images. METHODS A 3D residual U-NET model was trained using 8 different tracers (FDG: 75% and non-FDG: 25%) from 11 sites from US, Europe and Asia. A total of 309 training and 33 validation datasets scanned on GE Discovery MI (DMI) ToF scanners were used for development of DLToF models of three strengths: low (L), medium (M) and high (H). The training and validation pairs consisted of target ToF and input non-ToF BSREM reconstructions using site-preferred regularisation parameters (beta values). The contrast and noise properties of each model were defined by adjusting the beta value of target ToF images. A total of 60 DMI datasets, consisting of a set of 4 tracers ($^{18}$F-FDG, $^{18}$F-PSMA, $^{68}$Ga-PSMA, $^{68}$Ga-DOTATATE) and 15 exams each, were collected for testing and quantitative analysis of the models based on standardized uptake value (SUV) in regions of interest (ROI) placed in lesions, lungs and liver. Each dataset includes 5 image series: ToF and non-ToF BSREM and three DLToF images. The image series (300 in total) were blind scored on a 5-point Likert score by 4 readers based on lesion detectability, diagnostic confidence, and image noise/quality. RESULTS In lesion SUV$_{max}$ quantification with respect to ToF BSREM, DLToF-H achieved the best results among the three models by reducing the non-ToF BSREM errors from -39% to -6% for $^{18}$F-FDG (38 lesions); from -42% to -7% for $^{18}$F-PSMA (35 lesions); from -34% to -4% for $^{68}$Ga-PSMA (23 lesions) and from -34% to -12% for $^{68}$Ga-DOTATATE (32 lesions). Quantification results in liver and lung also showed ToF-like performance of DLToF models. Clinical reader resulted showed that DLToF-H results in an improved lesion detectability on average for all four radiotracers whereas DLToF-L achieved the highest scores for image quality (noise level). The results of DLToF-M however showed that this model results in the best trade-off between lesion detection and noise level and hence achieved the highest score for diagnostic confidence on average for all radiotracers. CONCLUSION This study demonstrated that the DLToF models are suitable for both FDG and non-FDG tracers and could be utilized for digital BGO PET/CT scanners to provide an image quality and lesion detectability comparable and close to ToF