7 resultados para Drug Related Problems
em AMS Tesi di Dottorato - Alm@DL - Università di Bologna
Resumo:
We study automorphisms and the mapping class group of irreducible holomorphic symplectic (IHS) manifolds. We produce two examples of manifolds of K3[2] type with a symplectic action of the alternating group A7. Our examples are realized as double EPW-sextics, the large cardinality of the group allows us to prove the irrationality of the associated families of Gushel-Mukai threefolds. We describe the group of automorphisms of double EPW-cubes. We give an answer to the Nielsen realization problem for IHS manifolds in analogy to the case of K3 surfaces, determining when a finite group of mapping classes fixes an Einstein (or Kähler-Einstein) metric. We describe, for some deformation classes, the mapping class group and its representation in second cohomology. We classify non-symplectic involutions of manifolds of OG10 type determining the possible invariant and coinvariant lattices. We study non-symplectic involutions on LSV manifolds that are geometrically induced from non-symplectic involutions on cubic fourfolds.
Resumo:
Crew scheduling and crew rostering are similar and related problems which can be solved by similar procedures. So far, the existing solution methods usually create a model for each one of these problems (scheduling and rostering), and when they are solved together in some cases an interaction between models is considered in order to obtain a better solution. A single set covering model to solve simultaneously both problems is presented here, where the total quantity of drivers needed is directly considered and optimized. This integration allows to optimize all of the depots at the same time, while traditional approaches needed to work depot by depot, and also it allows to see and manage the relationship between scheduling and rostering, which was known in some degree but usually not easy to quantify as this model permits. Recent research in the area of crew scheduling and rostering has stated that one of the current challenges to be achieved is to determine a schedule where crew fatigue, which depends mainly on the quality of the rosters created, is reduced. In this approach rosters are constructed in such way that stable working hours are used in every week of work, and a change to a different shift is done only using free days in between to make easier the adaptation to the new working hours. Computational results for real-world-based instances are presented. Instances are geographically diverse to test the performance of the procedures and the model in different scenarios.
Resumo:
Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.
Resumo:
The olive oil extraction industry is responsible for the production of high quantities of vegetation waters, represented by the constitutive water of the olive fruit and by the water used during the process. This by-product represent an environmental problem in the olive’s cultivation areas because of its high content of organic matter, with high value of BOD5 and COD. For that reason the disposal of the vegetation water is very difficult and needs a previous depollution. The organic matter of vegetation water mainly consists of polysaccharides, sugars, proteins, organic acids, oil and polyphenols. This last compounds are the principal responsible for the pollution problems, due to their antimicrobial activity, but, at the same time they are well known for their antioxidant properties. The most concentrate phenolic compounds in waters and also in virgin olive oils are secoiridoids like oleuropein, demethyloleuropein and ligstroside derivatives (the dialdehydic form of elenolic acid linked to 3,4-DHPEA, or p-HPEA (3,4-DHPEA-EDA or p-HPEA-EDA) and an isomer of the oleuropein aglycon (3,4-DHPEA-EA). The management of the olive oil vegetation water has been extensively investigated and several different valorisation methods have been proposed, such as the direct use as fertilizer or the transformation by physico-chemical or biological treatments. During the last years researchers focused their interest on the recovery of the phenolic fraction from this waste looking for its exploitation as a natural antioxidant source. At the present only few contributes have been aimed to the utilization for a large scale phenols recovery and further investigations are required for the evaluation of feasibility and costs of the proposed processes. The present PhD thesis reports a preliminary description of a new industrial scale process for the recovery of the phenolic fraction from olive oil vegetation water treated with enzymes, by direct membrane filtration (microfiltration/ultrafiltration with a cut-off of 250 KDa, ultrafiltration with a cut-off of 7 KDa/10 KDa and nanofiltration/reverse osmosis), partial purification by the use of a purification system based on SPE analysis and by a liquid-liquid extraction system (LLE) with contemporary reduction of the pollution related problems. The phenolic fractions of all the samples obtained were qualitatively and quantitatively by HPLC analysis. The work efficiency in terms of flows and in terms of phenolic recovery gave good results. The final phenolic recovery is about 60% respect the initial content in the vegetation waters. The final concentrate has shown a high content of phenols that allow to hypothesize a possible use as zootechnic nutritional supplements. The purification of the final concentrate have garanteed an high purity level of the phenolic extract especially in SPE analysis by the use of XAD-16 (73% of the total phenolic content of the concentrate). This purity level could permit a future food industry employment such as food additive, or, thanks to the strong antioxidant activity, it would be also use in pharmaceutical or cosmetic industry. The vegetation water depollutant activity has brought good results, as a matter of fact the final reverse osmosis permeate has a low pollutant rate in terms of COD and BOD5 values (2% of the initial vegetation water), that could determinate a recycling use in the virgin olive oil mechanical extraction system producing a water saving and reducing thus the oil industry disposal costs .
Resumo:
This thesis aims to present the ORC technology, its advantages and related problems. In particular, it provides an analysis of ORC waste heat recovery system in different and innovative scenarios, focusing on cases from the biggest to the lowest scale. Both industrial and residential ORC applications are considered. In both applications, the installation of a subcritical and recuperated ORC system is examined. Moreover, heat recovery is considered in absence of an intermediate heat transfer circuit. This solution allow to improve the recovery efficiency, but requiring safety precautions. Possible integrations of ORC systems with renewable sources are also presented and investigated to improve the non-programmable source exploitation. In particular, the offshore oil and gas sector has been selected as a promising industrial large-scale ORC application. From the design of ORC systems coupled with Gas Turbines (GTs) as topper systems, the dynamic behavior of the GT+ORC innovative combined cycles has been analyzed by developing a dynamic model of all the considered components. The dynamic behavior is caused by integration with a wind farm. The electric and thermal aspects have been examined to identify the advantages related to the waste heat recovery system installation. Moreover, an experimental test rig has been realized to test the performance of a micro-scale ORC prototype. The prototype recovers heat from a low temperature water stream, available for instance in industrial or residential waste heat. In the test bench, various sensors have been installed, an acquisitions system developed in Labview environment to completely analyze the ORC behavior. Data collected in real time and corresponding to the system dynamic behavior have been used to evaluate the system performance based on selected indexes. Moreover, various operational steady-state conditions are identified and operation maps are realized for a completely characterization of the system and to detect the optimal operating conditions.
Resumo:
Water Distribution Networks (WDNs) play a vital importance rule in communities, ensuring well-being band supporting economic growth and productivity. The need for greater investment requires design choices will impact on the efficiency of management in the coming decades. This thesis proposes an algorithmic approach to address two related problems:(i) identify the fundamental asset of large WDNs in terms of main infrastructure;(ii) sectorize large WDNs into isolated sectors in order to respect the minimum service to be guaranteed to users. Two methodologies have been developed to meet these objectives and subsequently they were integrated to guarantee an overall process which allows to optimize the sectorized configuration of WDN taking into account the needs to integrated in a global vision the two problems (i) and (ii). With regards to the problem (i), the methodology developed introduces the concept of primary network to give an answer with a dual approach, of connecting main nodes of WDN in terms of hydraulic infrastructures (reservoirs, tanks, pumps stations) and identifying hypothetical paths with the minimal energy losses. This primary network thus identified can be used as an initial basis to design the sectors. The sectorization problem (ii) has been faced using optimization techniques by the development of a new dedicated Tabu Search algorithm able to deal with real case studies of WDNs. For this reason, three new large WDNs models have been developed in order to test the capabilities of the algorithm on different and complex real cases. The developed methodology also allows to automatically identify the deficient parts of the primary network and dynamically includes new edges in order to support a sectorized configuration of the WDN. The application of the overall algorithm to the new real case studies and to others from literature has given applicable solutions even in specific complex situations.
Resumo:
Using Computational Wind Engineering, CWE, for solving wind-related problems is still a challenging task today, mainly due to the high computational cost required to obtain trustworthy simulations. In particular, the Large Eddy Simulation, LES, has been widely used for evaluating wind loads on buildings. The present thesis assesses the capability of LES as a design tool for wind loading predictions through three cases. The first case is using LES for simulating the wind field around a ground-mounted rectangular prism in Atmospheric Boundary Layer (ABL) flow. The numerical results are validated with experimental results for seven wind attack angles, giving a global understanding of the model performance. The case with the worst model behaviour is investigated, including the spatial distribution of the pressure coefficients and their discrepancies with respect to experimental results. The effects of some numerical parameters are investigated for this case to understand their effectiveness in modifying the obtained numerical results. The second case is using LES for investigating the wind effects on a real high-rise building, aiming at validating the performance of LES as a design tool in practical applications. The numerical results are validated with the experimental results in terms of the distribution of the pressure statistics and the global forces. The mesh sensitivity and the computational cost are discussed. The third case is using LES for studying the wind effects on the new large-span roof over the Bologna stadium. The dynamic responses are analyzed and design envelopes for the structure are obtained. Although it is a numerical simulation before the traditional wind tunnel tests, i.e. the validation of the numerical results are not performed, the preliminary evaluations can effectively inform later investigations and provide the final design processes with deeper confidence regarding the absence of potentially unexpected behaviours.