Feasibility Analysis of Hydrogen Implementation In The Norwegian Fishing Fleet Industry

Abstract This thesis explores the financial and risk implications of transitioning from traditional diesel engines to hydrogen propulsion systems within the Norwegian fishing fleet industry. The study aims to assess the feasibility and benefits of hydrogen as a sustainable alternative to diesel. It considers various factors such as emissions, fuel consumption, lifetime engine costs, generated income, and risk management. The findings indicate that while hydrogen propulsion systems entail high initial investment and infrastructure challenges, they offer significant long-term cost savings and environmental benefits. The analysis emphasises the importance of government support, strategic planning, and phased implementation for successfully transitioning to hydrogen-powered vessels

Multi-Attribute Bias Mitigation in Recommender Systems

An abundance of data in this day and age opens up many opportunities for data consumption through many outlets. This calls for a framework to optimally categorize relevant data to be suggested to the users. Recommender systems have been in the limelight for quite some time now for this exact use case. Variational Autoencoder (VAE) based recommender systems have been very successful in matching users with potentially relevant items. VAEs work on the assumption that similar user profiles have similar likenesses and behavior and can be suggested items by finding a pattern in their item relevancy. User profiles can be grouped up based on various factors, the most important one being their history of item rankings, and other personal attributes such as age, country, and sex. An optimal output from the VAE should take into account the most relevant items from the user groups but can also develop a bias towards their attributes which has to be mitigated or it can propagate as the data increases, i.e. learning a pattern that deduces that a certain nationality finds a certain item relevant, which can add unfairness and bias in the results. A VAE-based framework (Stefanidis & Borges, 2022) was developed to tackle this problem and minimize user unfairness and bias in recommendations by actively taking into account a user's sensitive attribute while training. We propose an improvement to this framework by taking into account multiple sensitive attributes at a time to minimize user unfairness as much as possible and improve the PREC@1 performance metric. In the end, we compare the results of our improved framework to F2VAE and document our findings through multiple metrics like precision (PREC), unfairness (UFAIR), normalized discounted cumulative gain (NDCG), and recall

Antibacterial activity of a marine bacterium against pathogenic and environmental isolates of vibrio species

The marine environment covers three quarters of the surface of the planet is estimated to be home to more than 80% of life and yet it remains largely unexplored. The rich diversity of marine flora and fauna and its adaptation to the harsh marine environment coupled with new developments in biotechnology, has opened up a new exciting vista for extraction of bioactive products of use in medicine. In this study inhibitory activity of a marine bacterium isolated from gut of ribbonfish was studied against pathogenic and environmental isolates of Vibrio species. This strain was identified as Pseudomonas stutzeri and it was found active against V. harveyi (luminescent bacteria), V. cholerae, V. alginolyticus, V. damseal, V. fluvialis. The antibacterial substance produced by Pseudomonas stutzeri was soluble in organic solvent and closely bound to external surface of bacterial cells. Reduction of the absorbance of the V. cholera cell suspension was observed when log phase cells of V. cholerae were treated with MIC and 4xMIC concentration of crude extract of Pseudomonas stutzeri

Towards Greener Nights: Exploring AI-Driven Solutions for Light Pollution Management

This research endeavors to address the pervasive issue of light pollution through an interdisciplinary approach, leveraging data science and machine learning techniques. By analyzing extensive datasets and research findings, we aim to develop predictive models capable of estimating the degree of sky glow observed in various locations and times. Our research seeks to inform evidence-based interventions and promote responsible outdoor lighting practices to mitigate the adverse impacts of light pollution on ecosystems, energy consumption, and human well-being

Anomalous entities detection and localization in pedestrian flows

We propose a novel Gaussian kernel based integration model (GKIM) for anomalous entities detection and localization in pedestrian flows. The GKIM integrates spatio-temporal features for efficient and robust motion representation to capture the distinctive and meaningful information about the anomalous entities. We next propose a block based detection framework by training a recurrent conditional random field (R-CRF) using the GKIM features. The trained R-CRF model is then used to detect and localize the anomalous entities during the online testing stage. We conduct comprehensive experiments on three benchmark datasets and compare the performance of the proposed method with the state-of-the-art anomalous entities detection methods. Our experiments show that the proposed GKIM outperforms the compared methods in terms of equal error rate (EER) and detection rate (DR) in both frame-level and pixel-level comparisons. The frame-level analysis detects the presence of an anomalous entity in a frame regardless of its location. The pixel-level analysis localizes the anomalous entity in term of its pixels.acceptedVersion© 2018. This is the authors’ accepted and refereed manuscript to the article. Locked until 15.3.2020 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0

How growing tumour impacts intracranial pressure and deformation mechanics of brain

Brain is an actuator for control and coordination. When a pathology arises in cranium, it may leave a degenerative, disfiguring and destabilizing impact on brain physiology. However, the leading consequences of the same may vary from case to case. Tumour, in this context, is a special type of pathology which deforms brain parenchyma permanently. From translational perspective, deformation mechanics and pressures, specifically the intracranial cerebral pressure (ICP) in a tumour-housed brain, have not been addressed holistically in literature. This is an important area to investigate in neuropathy prognosis. To address this, we aim to solve the pressure mystery in a tumour-based brain in this study and present a fairly workable methodology. Using image-based finite-element modelling, we reconstruct a tumour-based brain and probe resulting deformations and pressures (ICP). Tumour is grown by dilating the voxel region by 16 and 30 mm uniformly. Cumulatively three cases are studied including an existing stage of the tumour. Pressures of cerebrospinal fluid due to its flow inside the ventricle region are also provided to make the model anatomically realistic. Comparison of obtained results unequivocally shows that as the tumour region increases its area and size, deformation pattern changes extensively and spreads throughout the brain volume with a greater concentration in tumour vicinity. Second, we conclude that ICP pressures inside the cranium do increase substantially; however, they still remain under the normal values (15 mmHg). In the end, a correlation relationship of ICP mechanics and tumour is addressed. From a diagnostic purpose, this result also explains why generally a tumour in its initial stage does not show symptoms because the required ICP threshold has not been crossed. We finally conclude that even at low ICP values, substantial deformation progression inside the cranium is possible. This may result in plastic deformation, midline shift etc. in the brain

Analysis of final state lepton polarization-dependent observables in H→ℓ+ℓ−γH\to \ell^{+}\ell^{-} \gamma in the SM at loop level

Recently, the CMS and ATLAS collaborations have announced the results for $H\rightarrow Z[\rightarrow \ell^{+}\ell^{-}]\gamma$ with $\ell=e$ or $\mu$, where $H\rightarrow Z\gamma$ is a sub-process of $H\rightarrow \ell^{+} \ell^{-} \gamma$. This semi-leptonic Higgs decay receives loop induced resonant $H\rightarrow Z[\rightarrow \ell^{+}\ell^{-}]\gamma$ as well as non-resonant contributions as discussed in. To probe further features coming from these contributions to $H\rightarrow \ell^{+} \ell^{-} \gamma$, we suggest that the polarization of the final state lepton is an important parameter. We show that the resonant and non-resonant cross-terms play an important role when the polarization of final state lepton is taken into account, which is negligible in the case of un-polarized leptons. For this purpose, we have calculated the polarized decay rates and the longitudinal, normal and transverse polarization asymmetries. We find that these asymmetries purely come from the loop contributions and are helpful to further investigate the resonant and non-resonant nature of $H\rightarrow Z[\rightarrow \ell^{+}\ell^{-}]\gamma$ decay. We observe that for $\ell=e,\mu$, the longitudinal decay rate is highly suppressed around $m_{\ell\ell}\approx 60$GeV when the final lepton spin is $-\frac{1}{2}$, dramatically increasing the corresponding lepton polarization asymmetries. Furthermore, we analyze another clean observable, the ratio of decay rates $\Gamma$, $R^{\ell\ell'}\equiv \frac{\Gamma_{H\rightarrow \ell^{+} \ell^{-} \gamma}}{\Gamma_{H\rightarrow \ell^{'+} \ell^{'-} \gamma}}$, where $\ell$ and $\ell'$ refer to different final state lepton generations. Therefore, the precise measurements of these observables at CMS and ATLAS can provide a fertile ground to test not only the Standard Model (SM) but also to examine the signatures of possible new physics (NP) beyond the SM.Comment: 28 pages, 7 figure