Marche Polytechnic University

IRIS Università Politecnica delle Marche
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    62135 research outputs found

    Coupled Multi-risk Mitigation in Historical Urban Outdoor Built Environment: Preliminary Strategies Evaluation Through Typological Scenarios

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    Outdoor Built Environment (BE), such as squares, are paramount scenarios in historic cities. They attract many users that can be affected by both Slow and Sudden onset disasters, depending on the combination of possible hazards, BE modification in view of the BE morphological and constructive features, and the users’ vulnerability and exposure. The coupling of sudden and slow-onset disasters represents a critical but not remote situation. This work hence provides an approach to assess coupled multi-risk in historical outdoor BE by using behavioural simulation methods and to evaluate the effectiveness of mitigation strategies. The simulation model is based on a probabilistic, multi-agent and cellular automata approach, developed in a slow-to-sudden events perspective. Heatwaves (as a slow onset disaster) affect the initial users’ position in the outdoor BE in view of outdoor temperature. Then, a terrorist act (as a sudden onset disaster) appears, thus making users evacuate from the outdoor BE. The application involves relevant typological conditions of outdoor BE to trace rapid and generalisable overviews of emergency impacts that can be then verified in specific case-studies. The slow-to-sudden events approach is applied to different BE typologies characterized by different climate conditions for hazards, terrorist attacks, and mitigation strategies. Simulation analysis mainly concerns evacuation to focus on quick events faced by users. Results demonstrate the approach capabilities in comparing coupled multi-risks conditions depending on BE configurations. The approach can outline quick solutions in the considered typological BE, and can be applied to real-world scenarios to “tailor” strategies on effective BE conditions

    Characterization and outcomes of difficult-to-treat patients starting modern first-line ART regimens: Data from the ICONA cohort

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    Objectives: Treatment failures to modern antiretroviral therapy (ART) raise concerns, as they could re- duce future options. Evaluations of occurrence of multiple failures to modern ART are missing and their significance in the long run is unclear. Methods: People with HIV (PWH) in the ICONA cohort who started a modern first-line ART were defined as ‘difficult to treat’ (DTT) if they experienced ≥1 among: i) ≥2 VF (2 viral loads, VL > 200 copies/mL or 1 VL > 10 0 0 copies/mL) with or without ART change; ii) ≥2 treatment discontinuations (TD) due to toxic- ity/intolerance/failure; iii) ≥1 VF followed by ART change plus ≥1 TD due to toxicity/intolerance/failure. A subgroup of the DTT participants were matched to PWH that, after the same time, were non-DTT. Treat- ment response, analysing VF, TD, treatment failure, AIDS/death, and SNAE (Serious non-AIDS event)/death, were compared. Survival analysis by KM curves and Cox regression models were employed. Results: Among 8061 PWH, 320 (4%) became DTT. Estimates of becoming DTT was 6.5% (95% CI: 5.8–7.4%) by 6 years. DTT PWH were significantly older, with a higher prevalence of AIDS and lower CD4 + at nadir than the non-DTT. In the prospective analysis, DTT demonstrated a higher unadjusted risk for all the outcomes. Once controlled for confounders, significant associations were confirmed for VF (aHR 2.23, 1.33–3.73), treatment failure (aHR 1.70, 1.03–2.78), and SNAE/death (aHR 2.79, 1.18–6.61). Conclusion: A total of 6.5% of PWH satisfied our definition of DTT by 6 years from ART starting. This appears to be a more fragile group who may have higher risk of failure

    Improving Sustainable Management of University Buildings Based on Occupancy Data

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    The sustainability of buildings during their life cycle could be increased by optimizing their facility management. In this sense, data-driven approaches could support the improvement of building operation and maintenance (O&M), because they can exploit collected data to provide useful correlations to assess the sustainability performance depending on the surrounding constraints. Universities are among the most relevant and largest organizations, generally hosted in multistory buildings, that could take advantage of such data to improve the sustainable goals of class occupancy and timetable. A high level of classroom occupancy is the main goal for class timetabling, and its effect on other O&M performance generally is overlooked. In the literature, class timetabling effects on university O&M, and especially on elevator maintenance tasks, have not yet been addressed in depth. Therefore this work adopted a data-driven approach to jointly optimize class scheduling and corrective maintenance actions required for elevators in university buildings. Elevator use is influenced greatly by schedule-dependent occupant movement, and thus is one of the main components of the total maintenance costs, and significantly affects safety performance. A 15-month experimental campaign on a university campus hosting as many as 7,000 occupants daily was performed to correlate occupant presence and movement with the number of corrective actions on elevators. The data-driven correlation was integrated with open-source timetabling software to assess the impact of alternative timetables (affecting occupant movement and occupancy levels) on expected maintenance needs. According to the results, the optimized timetable can reduce current elevator maintenance needs by 65%, whereas the classroom occupancy performance is reduced by only 7%, thus still leading to sustainable building use. The proposed optimization approach allows facility managers to implement a university class timetabling that achieves higher maintenance cost savings, thus moving toward more-sustainable management of building scheduling and maintenance performance in a joint manner

    A label free chemoproteomic-based platform to disclose cannabidiol molecular mechanism of action on chronic myelogenous leukemia cancer cells

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    : The discovery of the interactome of cannabidiol (CBD), a non-psychoactive cannabinoid from Cannabis sativa L., has been here performed on chronic myelogenous leukemia cancer cells, using an optimized chemo-proteomic stage, which links Drug Affinity Responsive Target Stability with Limited Proteolysis Multiple Reaction Monitoring approaches. The obtained results showed the ability of CBD to target simultaneously some potential protein partners, corroborating its well-known poly-pharmacology activity. In human chronic myelogenous leukemia K562 cancer cells, the most fascinating protein partner was identified as the 116 kDa U5 small nuclear ribonucleoprotein element called EFTUD2, which fits with the spliceosome complex. The binding mode of this oncogenic protein with CBD was clarified using mass spectrometry-based and in silico analysis

    Combining an LNS-based approach and organizational mining for the Resource Replacement Problem

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    For companies, it is crucial to promptly react to (even short-term) lack of resources, for guaranteeing the continuity of the operations in business processes. This leads to the solution of a Resource Replacement Problem (RRP) aimed at reassigning as many activities performed by resources that are no longer available to those that are available. To this purpose, several aspects are considered simultaneously, e.g., resources skills, workloads and other domain-specific constraints. In this paper, we propose an innovative hybrid approach for solving RRP, combining mathematical optimization with organizational mining. In particular, logs of past process executions are used to model a social network of resources by organizational mining techniques. Then, a similarity measure among resources is derived and exploited along with run-time resource workload and information on activities priority to formulate an Integer Linear Programming (ILP) model for reassigning the activities of unavailable resources, minimizing the total reassignment cost. To efficiently solve RRP, a Large Neighborhood Search based matheuristic is developed. Computational experiments show that the proposed matheuristic outperforms the commercial solver used to solve the ILP model. A sensitivity analysis, on possible variations of the input parameters and on the moves of the matheuristic, concludes the work

    White Paper on Innovative Joining Technologies for Naval Applications

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    This documentwants to provide an in-depth framework on the use of innovative joining technologies in the shipbuilding industry that must innovate both its own ship product and production process to maintain high levels of competitiveness, however, it wants also to attract the attention of the shipbuilders, because they often are reluctant to change, to stimulate them to innovation. After an introduction focused on both the impact and the evolution of the industry of European shipbuilding into the contest of the Blue Economy, a state-of-art is developed highlighting the progress in terms not only scientific but also in terms of design and application. Then, the document aims to identify the technological needs and the possible solutions also with the aid of the information provided by some companies such as Intermarine, Fincantieri and Caronte and Tourist

    Development of sourdough bread from roll‐milled and stone‐ground soft (Triticum aestivum) wheat flours milled to different extraction rates

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    The aims of the present study are: (i) to verify the influence of different flour extraction rates and milling procedures on bread quality, (ii) to optimize the bread-making process by using different percentage and time of fermentation of three spontaneously developed type I sourdoughs. These latter were prepared with a whole-meal wheat flour blend (SA), a type 0 wheat flour blend (SB) both obtained by steel roll milling, and a type 2 wheat flour blend obtained by stone grinding (SC). The pH, total titratable acidity (TTA), and stability of the microbiota of the three sourdoughs were assessed before baking trials. TTA, specific volume, weight, crumb core moisture, texture, and global liking of sourdough bread, in comparison to control bread made with commercial baker’s yeast, were determined. Moisture, texture, and global liking of bread were also evaluated during 6 days of storage. SA was characterized by a significantly higher pH and TTA values than SB and SC. Differences in the LAB-to-yeast ratio were registered among the three sourdoughs although no differences were seen in terms of the dominant microbial community. Concerning breads, although the type 0 roll-milled wheat flour showed better dough rheological performance compared to whole-meal wheat flour and stone-ground wheat flour, sourdough fermentation positively affected the specific volume, texture, and global liking of bread manufactured with stone-ground wheat flour. Overall, for an efficient use of sourdough and bread quality improvement, optimal conditions need to be found by tailoring sourdough to the type of flour used

    Calibration in the "real world" of a partially specified stochastic volatility model

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    We study the "real-world" calibration of a partially specified stochastic volatility model, where the analytic expressions of the asset price drift rate and of the stochastic variance drift are not specified. The model is calibrated matching the observed asset log returns and the priors assigned by the investor. No option price data are used in the calibration. The priors chosen for the asset price drift rate and for the stochastic variance drift are those suggested by the Heston model. For this reason, the model presented can be considered as an "enhanced" Heston model. The calibration problem is formulated as a stochastic optimal control problem and solved using the dynamic programming principle. The model presented and the Heston model are calibrated using synthetic and Standard & Poor 500 (S&P500) data. The calibrated models are used to produce 6, 12, and 24 months in the future synthetic and S&P500 forecasts

    In silico and in vitro human metabolism of IOX2, a performance-enhancing doping agent

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    IOX2 is a potent inhibitor of prolyl hydroxylase 2, a key enzyme in the regulation of hypoxia-inducible factor (HIF) and oxygen homeostasis. As such, it can be used to enhance athletic performance and is currently banned by the World Anti-Doping Agency (WADA). Detection of metabolites is critical to demonstrate drug use in doping. However, there is currently little data on IOX2 human metabolism. Our aim was to identify relevant biomarkers of IOX2 use in humans. For this purpose, IOX2 was incubated with 10-donor-pooled human hepatocytes for 3 h, incubates were analyzed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS), and LC-HRMS/MS data were screened with Compound Discoverer (Thermo Scientific) for a comprehensive identification of IOX2 metabolites. Additionally, IOX2 human metabolites were predicted with GLORYx open-access software (University of Hamburg, Germany) to assist in the LC-HRMS/MS analysis and data mining. Thirteen metabolites were identified, oxidation at the quinolinyl group, O-glucuronidation, and combinations being predominant biotransformations. The results were consistent with previous animal studies and a single case of oral microdose administration. We suggest hydroxyquinolinyl-IOX2 as major biomarker of IOX2 use in biological samples, glucuronide hydrolysis being critical to increase IOX2 and hydroxyquinolinyl-IOX2 detectability in urine


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