4,803 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Topological data analysis enhanced prediction of hydrogen storage in metal–organic frameworks (MOFs)

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    Metal–organic frameworks (MOFs) have the capacity to serve as gas capturing, sensing, and storing systems. It is usual practice to select the MOF from a vast database with the best adsorption property in order to do an adsorption calculation. The costs of computing thermodynamic values are sometimes a limiting factor in high-throughput computational research, inhibiting the development of MOFs for separations and storage applications. In recent years, machine learning has emerged as a promising substitute for traditional methods like experiments and simulations when trying to foretell material properties. The most difficult part of this process is choosing characteristics that produce interpretable representations of materials that may be used for a variety of prediction tasks. We investigate a feature-based representation of materials using tools from topological data analysis. In order to describe the geometry of MOFs with greater accuracy, we use persistent homology. We show our method by forecasting the hydrogen storage capacity of MOFs during a temperature and pressure swing from 100 bar/77 K to 5 bar/160 K, using the synthetically compiled CoRE MOF-2019 database of 4029 MOFs. Our topological descriptor is used in conjunction with more conventional structural features, and their usefulness to prediction tasks is explored. In addition to demonstrating significant progress over the baseline, our findings draw attention to the fact that topological features capture information that is supplementary to the structural features

    Assessing the reversed exponential decay of the electrical conductance in molecular wires: the undeniable effect of static electron correlation

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    An extraordinary new family of molecular junctions, inaccurately referred to as "anti-Ohmic" wires in the recent literature, has been proposed based on theoretical predictions. The unusual electron transport observed for these systems, characterized by a reversed exponential decay of their electrical conductance, might revolutionize the design of molecular electronic devices. This behavior, which has been associated with intrinsic diradical nature, is reexamined in this work. Since the diradical character arises from a near-degeneracy of the frontier orbitals, the employment of a multireference approach is mandatory. CASSCF calculations on a set of nanowires based on polycyclic aromatic hydrocarbons (PAHs) demonstrate that, in the frame of an appropriate multireference treatment, the ground state of these systems shows the expected exponential decay of the conductance. Interestingly, these calculations do evidence a reversed exponential decay of the conductance, although now in several excited states. Similar results have been obtained for other recently proposed candidates to "anti-Ohmic" wires. These findings open new horizons for possible applications in molecular electronics of these promising systems.Xunta de Galicia | Ref. GRC2019/24Agencia Estatal de Investigación | Ref. PGC2018-095953-B-I0

    A Trust Management Framework for Vehicular Ad Hoc Networks

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    The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers

    Graduate Catalog of Studies, 2023-2024

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    Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality

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    As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach. Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties. Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel

    The anisotropic grain size effect on the mechanical response of polycrystals: The role of columnar grain morphology in additively manufactured metals

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    Additively manufactured (AM) metals exhibit highly complex microstructures, particularly with respect to grain morphology which typically features heterogeneous grain size distribution, anomalous and anisotropic grain shapes, and the so-called columnar grains. In general, the conventional morphological descriptors are not suitable to represent complex and anisotropic grain morphology of AM microstructures. The principal aspect of microstructural grain morphology is the state of grain boundary spacing or grain size whose effect on the mechanical response is known to be crucial. In this paper, we formally introduce the notions of axial grain size and grain size anisotropy as robust morphological descriptors which can concisely represent highly complex grain morphologies. We instantiated a discrete sample of polycrystalline aggregate as a representative volume element (RVE) which has random crystallographic orientation and misorientation distributions. However, the instantiated RVE incorporates the typical morphological features of AM microstructures including distinctive grain size heterogeneity and anisotropic grain size owing to its pronounced columnar grain morphology. We ensured that any anisotropy arising in the macroscopic mechanical response of the instantiated sample is mainly associated with its underlying anisotropic grain size. The RVE was then used for meso-scale full-field crystal plasticity simulations corresponding to uniaxial tensile deformation along different axes via a spectral solver and a physics-based crystal plasticity constitutive model. Through the numerical analyses, we were able to isolate the contribution of anisotropic grain size to the anisotropy in the mechanical response of polycrystalline aggregates, particularly those with the characteristic complex grain morphology of AM metals. Such a contribution can be described by an inverse square relation

    Evolution of polygonal crack patterns in mud when subjected to repeated wetting-drying cycles

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    The present paper demonstrates how a natural crack mosaic resembling a random tessellation evolves with repeated 'wetting followed by drying' cycles. The natural system here is a crack network in a drying colloidal material, for example, a layer of mud. A spring network model is used to simulate consecutive wetting and drying cycles in mud layers until the crack mosaic matures. The simulated results compare favourably with reported experimental findings. The evolution of these crack mosaics has been mapped as a trajectory of a 4-vector tuple in a geometry-topology domain. A phenomenological relation between energy and crack geometry as functions of time cycles is proposed based on principles of crack mechanics. We follow the crack pattern evolution to find that the pattern veers towards a Voronoi mosaic in order to minimize the system energy. Some examples of static crack mosaics in nature have also been explored to verify if nature prefers Voronoi patterns. In this context, the authors define new geometric measures of Voronoi-ness of crack mosaics to quantify how close a tessellation is to a Voronoi tessellation, or even, to a Centroidal Voronoi tessellation

    AI in drug discovery and its clinical relevance

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    The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.  Other InformationPublished in:HeliyonLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://doi.org/10.1016/j.heliyon.2023.e17575 </p
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