Autonomous vehicles will "invade" our street in a not so far future. They must be coordinated in order to exploit the resources in a fair yet effective way. One of these resources, whose management is quite challenging, is represented by crossings: vehicles come and aim at passing the intersection, often as soon as possible, but they must compete with other vehicles. In this paper we propose an auction-based mechanism to coordinate the vehicles at a crossing, considering the presence of both human-driven and autonomous vehicles; moreover, we introduce an enhancement mechanism to take into consideration also the presence of vehicles in the lanes behind the first one. We analyze different strategies and, by means of simulation experiments, we compare them and show how waiting times change depending on the strategies. The results show that the cooperative strategy presents average waiting times lower for all vehicles than a competitive strategy. CCS CONCEPTS • Computing methodologies → Cooperation and coordination; Self-organization.
The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight two specific misfolded proteins in its development: Amyloid-beta and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on a machine learning approach. The proposed method applies an autoregressive model, constrained by structural connectivity, to predict concentrations of Amyloid-beta two years after the provided baseline. In experiments, the autoregressive model generally outperformed the state-of-art models yielding the lowest average prediction error (mean squared-error 0.0062). Moreover, we assess its effectiveness and suitability for real case scenarios, for which we provide a web service for physicians and researchers. Despite predicting amyloid pathology alone is not sufficient to clinical outcome, its prediction can be helpful to further plan therapies and other cures.
BACKGROUND Highly detailed and invasive clinical investigations are needed to stratify haematuria patients with no disease, benign disease, and malignant disease. Due to the heterogeneity in the patient population and wide range of potential causes of haematuria, possibility to indicate patient-specific biomarkers could improve and speed up diagnostic process, which is crucial for patients with suspected cancer. OBJECTIVE We developed a new algorithm to identify risk of bladder cancer in haematuria patients by analyzing multiple urine and serum biomarkers and identifying the most significant using complex network theory. METHODS We analyzed data collected in the HABIO case – control study of haematuria patients, containing 675 participants (190 females, 485 males) aged between 40 and 80 years. In the study, we used the initial analysis pipeline of our Self-Supervised Semantic Learning (3SL) framework grounded on the complex network theory to stratify participants into two groups: healthy (with no clear cause of haematuria) or sick (with bladder cancer, infection etc.). We compared our model sensitivity and specificity with logistic regression and binary decision tree outcomes. To assess model performance, we used balanced accuracy to account for imbalance between the number of healthy and sick participants in the dataset. Additionally, to assess how linearly separable the biomarkers were, we applied k-means clustering. RESULTS Our modelling outperformed logistic regression and binary decision trees obtaining balance accuracies of 0.693 (females) and 0.715 (males) vs 0.621 (females) and 0.533 (males) for logistic regression and 0.570 (females) and 0.597 (males) for binary trees. K-means clustering showed that the distribution of the biomarkers did not match clear macro-patterns. For the sick population (both genders) the most significant biomarkers were previously associated with infectious diseases and inflammation (thrombomodulin, sTNFRII and osmolarity) or bladder cancer (IL-8, TGF-β). Additionally, CXCL16, midkine, clusterin, CEA, 8-OHdG were previously described in the literature as a potential biomarker for urinary tract cancers. CONCLUSIONS In the study we applied a new algorithm to improve diagnosis of haematuria in study participants. The algorithm performs better than currently widely applied methods (logistic regression, binary trees, k-means clustering). Additionally, applying 3SL algorithm we identified biomarkers most relevant for the specific group of patients and dependencies between those biomarkers. We hope that our results can guide further research and provide new personalised diagnostic tools directly tailored to individual patients' needs. CLINICALTRIAL Ethical approval was obtained from the Office of Research Ethics Committee Northern Ireland (11/NI/0164).
The coordination of autonomous vehicles is an open field that is addressed by different researches comprising many different techniques. In this paper we focus on decentralized approaches able to provide adaptability to different infrastructural and traffic conditions. We formalize an Emergent Behavior Approach that, as per our knowledge, has never been performed for this purpose, and a Decentralized Auction approach. We compare them against existing centralized negotiation approaches based on auctions and we determine under which conditions each approach is preferable to the others.
After the SARS-CoV-2 outbreak in spring 2020, Italy faced a second epidemic wave in autumn. Using a SIRD model calibrated on COVID19-related deaths, we describe the regional epidemic dynamics from August to November 2020. We explore the time-varying reproductive number, R0(t), and quantify the number of infections, included their submerged portion, under different infection fatality rate scenarios. Results indicate that during the second epidemic wave, R0(t) changed over time heterogeneously across regions, with some important common elements including a mid-October peak and a decline during November, which suggest the possible role in inflating or deflating the contagion rate of specific events (e.g. schools reopening, regional elections) and of the restrictions imposed at the national and local level to reduce the infection spread. Despite the decline of R0(t) in most regions, the prevalence of circulating infections estimated at the end of the study period was not negligible, in particular in the North of the country. This suggests that even small increases of R0(t) in December may lead in a short time to unsustainable levels of contagion spread, depending on the regional supply of hospital and ICU beds and healthcare services throughout the territory.
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