Why Energy Matters? Profiling Energy Consumption of Mobile Crowdsensing Data Collection Frameworks

Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data with their mobile devices. To produce data, citizens sustain costs, i.e., the energy consumed for sensing and reporting operations. Hence, devising energy efficient data collection frameworks (DCF) is essential to foster participation. In this work, we investigate from an energy-perspective the performance of different DCFs. Our methodology is as follows: (i) we developed an Android application that implements the DCFs, (ii) we profiled the energy and network performance with a power monitor and Wireshark, (iii) we included the obtained traces into CrowdSenSim simulator for large-scale evaluations in city-wide scenarios such as Luxembourg, Turin and Washington DC. The amount of collected data, energy consumption and fairness are the performance indexes evaluated. The results unveil that DCFs with continuous data reporting are more energy-efficient and fair than DCFs with probabilistic reporting. The latter exhibit high variability of energy consumption, i.e., to produce the same amount of data, the associated energy cost of different users can vary significantly

Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Smart City Applications

Mobile crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. In MCS, citizens actively participate in the sensing process by contributing data with their smartphones, tablets, wearables and other mobile devices to a collector. As citizens sustain costs while contributing data, i.e., the energy spent from the batteries for sensing and reporting, devising energy efficient data collection frameworks (DCFs) is essential. In this work, we compare the energy efficiency of several DCFs through CrowdSenSim, which allows to perform large-scale simulation experiments in realistic urban environments. Specifically, the DCFs under analysis differ one with each other by the data reporting mechanism implemented and the signaling between users and the collector needed for sensing and reporting decisions. Results reveal that the key criterion differentiating DCFs' energy consumption is the data reporting mechanism. In principle, continuous reporting to the collector should be more energy consuming than probabilistic reporting. However, DCFs with continuous reporting that implement mechanisms to block sensing and data delivery after a certain amount of contribution are more effective in harvesting data from the crowd

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology

Metadata-Aware Measures for Answer Summarization in Community Question Answering

My thesis report presents a framework for automatically processing information coming from community Question Answering (cQA) portals. The purpose is that of automatically generating a summary in response to a question posed by a human user in natural language. The goal is to ensure that such answer be as trustful, complete, relevant and succinct as possible. In order to do so, the author exploits the metadata intrinsically present in User Generated Content (UGC) to bias automatic multi-document summarization techniques toward higher quality information. The originality of this work lies in the fact that it adopts a representation of concepts alternative to n-grams, which is the standard choice for text summarization tasks; furthermore it proposes two concept-scoring functions based on the notion of semantic overlap. Experimental results on data drawn from Yahoo! Answers demonstrate the effectiveness of the presented method in terms of ROUGE scores. This shows that the information contained in the best answers voted by users of cQA portals can be successfully complemented by the proposed method

Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data

Abstract Background Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learning algorithms trained on unselected, nation-wide population data from Sweden to solve the binary classification problem of predicting survival versus non-survival 2 years after first myocardial infarction (MI). Methods This prospective national registry study for prognostic accuracy validation of predictive models used data from 51,943 complete first MI cases as registered during 6 years (2006–2011) in the national quality register SWEDEHEART/RIKS-HIA (90% coverage of all MIs in Sweden) with follow-up in the Cause of Death register (> 99% coverage). Primary outcome was AUROC (C-statistic) performance of each model on the untouched test set (40% of cases) after model development on the training set (60% of cases) with the full (39) predictor set. Model AUROCs were bootstrapped and compared, correcting the P-values for multiple comparisons with the Bonferroni method. Secondary outcomes were derived when varying sample size (1–100% of total) and predictor sets (39, 10, and 5) for each model. Analyses were repeated on 79,869 completed cases after multivariable imputation of predictors. Results A Support Vector Machine with a radial basis kernel developed on 39 predictors had the highest complete cases performance on the test set (AUROC = 0.845, PPV = 0.280, NPV = 0.966) outperforming Boosted C5.0 (0.845 vs. 0.841, P = 0.028) but not significantly higher than Logistic Regression or Random Forest. Models converged to the point of algorithm indifference with increased sample size and predictors. Using the top five predictors also produced good classifiers. Imputed analyses had slightly higher performance. Conclusions Improved mortality prediction at hospital discharge after first MI is important for identifying high-risk individuals eligible for intensified treatment and care. All models performed accurately and similarly and because of the superior national coverage, the best model can potentially be used to better differentiate new patients, allowing for improved targeting of limited resources. Future research should focus on further model development and investigate possibilities for implementation

Northward Spread of the Parrotfish Sparisoma cretense (Teleostei: Scaridae) in the Mediterranean Sea: An Update on Current Distribution with Two New Records from Sardinia

The parrotfish Sparisoma cretense, a marine species native to the eastern and southern coastal areas of the Mediterranean, has extended its distribution northward. Here, we provide an update on its distribution based on currently published data and two new records from the coastline of Sardinia, Italy (central-western Mediterranean). The survey methods were scuba diving and spearfishing: one specimen of S. cretense was caught along the Argentiera coastline (northwest Mediterranean) and the others were photographed in the Gulf of Orosei, Osalla Bay (central-eastern Mediterranean). A literature update, together with new records, documents the distribution of this species in the northernmost areas of the Mediterranean. Probably a result of global warming, the ongoing northward expansion of S. cretense highlights the need for sampling campaigns to obtain timely updates on population and distribution of this thermophilic species

Occurrence and Spatial Distribution of Dibothriocephalus Latus (Cestoda: Diphyllobothriidea) in Lake Iseo (Northern Italy): An Update

Dibothriocephalus latus (Linnaeus, 1758) (Cestoda: Diphyllobothriidea; syn. Diphyllobothrium latum), is a fish-borne zoonotic parasite responsible for diphyllobothriasis in humans. Although D. latus has long been studied, many aspects of its epidemiology and distribution remain unknown. The aim of this study was to investigate the prevalence, mean intensity of infestation, and mean abundance of plerocercoid larvae of D. latus in European perch (Perca fluviatilis) and its spatial distribution in three commercial fishing areas in Lake Iseo (Northern Italy). A total of 598 specimens of P. fluviatilis were caught in 2019. The total prevalence of D. latus was 6.5%. However, there were significant differences between areas (10.2% North; 7.3% Center; 1.5% South) (Chi-square test, p = 0.0018). The mean intensity of infestation ranged from 1 larva in southern area to 1.2 larvae in both the central and northern (Pisogne) areas. In addition, the mean abundance ranged from 0.02 in the southern area to 0.26 in the northern area (Pisogne). The total number of larvae (anterior dorsal\u2014AD = 21; anterior ventral\u2014AV = 1; posterior dorsal\u2014PD = 15; posterior ventral\u2014PV = 5) differed significantly between the four anatomical quadrants (Kruskal\u2013Wallis test; p = 0.0001). The prevalence of D. latus plerocercoid larvae in European perch from Lake Iseo has long been investigated, but without an appropriate sampling design. With the present study, a broader analysis in spatial distribution has been added to the existing literature, revealing new information about D. latus distribution and occurrence in Lake Iseo, with new data that will be useful for health authorities and future studies

Reply to Pavlik et al. Clinical Relevance and Environmental Prevalence of <i>Mycobacterium fortuitum</i> Group Members. Comment on “Mugetti et al. Gene Sequencing and Phylogenetic Analysis: Powerful Tools for an Improved Diagnosis of Fish Mycobacteriosis Caused by <i>Mycobacterium fortuitum</i> Group Members. <i>Microorganisms</i> 2021, <i>9</i>, 797”

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