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An adaptive large neighbourhood search algorithm for diameter bounded network design problems
No full textThis paper focuses on designing a diameter - constrained network where the maximum distance between any pair of nodes is bounded. The objective considered is to minimise a weighted sum of the total length of the links followed by the total length of the paths between the pairs of nodes. First, the problem is formulated in terms of Mixed Integer Linear Programming and Constraint Programming to provide two alternative exact approaches. Then, an adaptive large neighbourhood search (LNS) to overcome memory and runtime limitations of the exact methods in large size instances is proposed. Such approach is based on computing an initial solution and repeatedly improve it by solving relatively small subproblems. We investigate various alternatives for finding an initial solution and propose two different heuristics for selecting subproblems. We have introduced a tighter lower bound, which demonstrates the quality of the solution obtained by the proposed approach. The performance of the proposed approach is assessed using three real-world network topologies from Ireland, UK and Italy, which are taken from national telecommunication operators and are used to design a transparent optical core network. Our results demonstrate that the LNS approach is scalable to large networks and it can compute very high quality solutions that are close to being optimal
High working capacity acetylene storage at ambient temperature enabled by a switching adsorbent layered material
No full textUnlike most gases, acetylene storage is a challenge because of its inherent
pressure sensitivity. Herein, a square lattice (sql) coordination network [Cu(4,4′-
bipyridine)2(BF4)2]n (sql-1-Cu-BF4) is investigated with respect to its C2H2 sorption
behavior from 189 to 298 K. The C2H2 sorption studies revealed that sql-1-Cu-BF4
exhibits multistep isotherms that are temperature-dependent and consistent with the
transformation from “closed” (nonporous) to four “open” (porous) phases induced by
the C2H2 uptake. The Clausius−Clapeyron equation was used to calculate the
performance of sql-1-Cu-BF4 for C2H2 storage at pressures >1 bar, which revealed
that its volumetric working capacity at 288 K is slightly superior to acetone (174 vs 170
cm3 cm−3
) over a safer pressure range (1−3.5 vs 1−15 bar). Molecular simulations
provided insights into the observed switching phenomena, revealing that the layer
expansion of sql-1-Cu-BF4 occurs via intercalation and inclusion of C2H2. These results
indicate that switching adsorbent layered materials offer promise for utility in the context
of C2H2 storage and deliver
Current and potential diagnostic modalities in the detection and management of breast cancer
No full textGlobal statistics have demonstrated that breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among female patients, accounting for 23 percent of total cancer cases and 14 percent of cancer deaths. Survival following a diagnosis of breast cancer is grossly determined by the stage of the disease at the time of initial diagnosis, highlighting the gravity of early detection. Improving early diagnosis will require a multi-faceted approach, including optimising the use of current available imaging modalities, in addition to investigating new methods of detection from a biochemical and radiological perspective. To further improve our knowledge on the use of current modalities, this thesis investigated the use of preoperative breast MRI in patients with invasive lobular breast cancer. While MRI was associated with minor delays in treatment and did not reduce overall rates of margin re-excision or completion mastectomy, it altered the choice of surgical procedure in almost a quarter of cases where MRI was utilised. Next, the potential role of microwave breast imaging was explored, and a First-in-Human trial conducted where the Wavelia system was introduced into the clinical setting. This novel system demonstrated exciting potential in the detection, localisation and characterisation of breast lesions. This modality may have the potential to offer a non-invasive, non-ionizing and painless adjunct to breast cancer diagnosis. Further larger studies are planned to validate the findings of this study. Finally, the potential of a circulating biomarker of breast cancer was investigated. Extracellular vesicles (EVs) are secreted by cells and shuttle bioactive materials including microRNAs (miRNA) throughout the circulation and are believed to represent a fingerprint of the cell of origin. As a result, recent years have seen an immense interest in their potential as a biomarker of breast cancer. In this work, EVs derived from breast-tumour bearing animals, breast cancer patients and healthy control volunteers were isolated and detailed characterisation performed. An array-based interrogation of the microRNA profile was performed and absolute quantitative polymerase chain reaction (AQ-PCR) employed to analyse EV-miRNA-451a expression. A significant increase in miRNA-451a copies/EV was detected in sera of breast cancer patients compared to healthy controls, suggesting a role for EV miRNA- 451a as a novel biomarker of breast cancer. A further novel finding from this work is that the previously widely used approach of indirectly quantifying EVs based on protein yield, bears no relationship with the number of EVs present in a sample when quantified directly using Nanoparticle Tracking Analysis (NTA). This has important implications for reproducibility of In Vitro, In Vivo, and clinical trials investigating the biomarker potential of EVs. When testing the therapeutic potential of an EV population, it is imperative that equal amounts of EVs from different cell sources or with different modifications are loaded to ensure standardised comparison. The findings presented in this thesis offer a multimodal contribution to the ongoing efforts to optimise and accelerate breast cancer detection and offers insight into the potential role of novel diagnostic strategies.2023-07-0
Next-generation multiparameter flow cytometry assay improves the assessment of oxidative stress in probiotics
No full textStability of probiotic products’ potency throughout shelf life is essential to ensure systematic delivery of the dosages required to provide clinically-proven health benefits. Due to the oxygen sensitivity of gut-derived microorganisms, methods for the rapid and accurate monitoring of oxidative stress in probiotics are greatly needed as they can be instrumental to both bioprocess optimization and quality control. This study introduces a next-generation flow cytometry method multiplexing the CellROX® Green and Propidium Iodide probes for the simultaneous measurement of free total reactive oxygen species (ROS) and membrane integrity, respectively. The multiparameter method was compared to the single-parameter assays, measuring either ROS or membrane integrity, for the ability to evaluate the fitness of Lactobacillus rhamnosus GG (LGG) after freeze drying, spray drying and H2O2-mediated oxidative stress. Each stand-alone assay detected only three cell populations, showing either differential membrane integrity (Syto 24+/PI-, Syto 24+/PI+, Syto 24-/PI+) or ROS levels (ROS-, low-ROS, high-ROS), and no correlation could be drawn between these groups. Conversely, the multiparameter method detected up to five physiologically distinct cell populations and allowed the integrated assessment of their membrane integrity and oxidative stress. It also revealed a much larger fitness heterogeneity in LGG as each group of low-ROS and high-ROS cells was found to be formed by a healthier population with an intact membrane (L-ROS/PI-, H-ROS/PI-) and a population with damaged membrane (L-ROS/PI+, H-ROS/PI+). As the CRG probe only detects free unreacted ROS, these populations are suggested to reflect the dynamic lifecycle of ROS formation, accumulation and reactive depletion leading to oxidative damage of macromolecules and consequent cell death. With the stand-alone CRG assay being unable to detect ROS lifecycle, the multiparameter method here presented delivers a superior profiling of the heterogeneity generated by oxidative stress in bacteria and enables a more correct interpretation of CRG fluorescence data. We provide recent examples from literature where the use of a single-parameter fluorescence approach may have led to misinterpret oxidative stress data and eventually draw erroneous conclusions
Social innovation and deep institutional innovation for sustainability and human development
No full textComprehensive design procedure for racetrack microinductors
No full textPresent needs in efficiency and integration are driving research towards the miniaturization of power converters. Among the latest components to achieve the desired degree of integration are cored micro-inductors and they are still one of the hardest devices to optimize, due to the high number of freedom degrees in their fabrication. In this paper, a comprehensive design procedure for these micro-inductors is presented. The proposed method makes it possible to design the optimal device in a single iteration. It also allows the designer to easily ascertain the limits of the inductor in terms of handled current and losses and provides valuable physical insight on the output of the process
Beyond car efficiency and electrification: Examining the role of demand reduction, public transit, and active travel measures to reduce GHG emissions in transport
No full textThis paper applies a data set of passenger kilometre transport demand by trip-purpose, mode type and tripdistance based on the outcomes of a National Travel Survey. The Irish Passenger Transport Emissions and Mobility (IPTEM) model enables a system-wide perspective on various measures which could be introduced to reduce passenger transport emissions. Combined with the LEAP Ireland 2050 energy systems simulation model, the carbon abatement potential of trip-purpose based policies, modal shift policies and technology improvements in public transport can be assessed. The results indicate that significant savings can be achieved from modal shift in Ireland, and that trip-purpose based targets and policies have a relevance in the effort to reduce Ireland’s energy related transport CO2 emissions. The active mode scenarios, which focus on increased walking and cycling achieve a 0.2 – 1 MTCO2 reduction in annual passenger transport emissions in 2030. The range of public transport scenarios, inspired by targets set out by the Irish Government’s Climate Action Plan achieve a between 0.001 – 0.3 MTCO2 reduction in annual passenger transport emissions in 2030. In addition, traffic camera data is used to model the impact of COVID-19 travel restrictions on transport CO2 emissions. The calculated reduction in transport CO2 emissions is 3.9 MTCO2 for 2020 and 2021. This study highlights the importance of factoring modal shift, trip distance and trip purpose into scenario analysis for transport emissions reduction, as it provides a framework for looking beyond only improving technologies in private vehicle transport
Investigating the impact of pre‑processing techniques and pre‑trained word embeddings in detecting Arabic health information on social media
No full textThis paper presents a comprehensive evaluation of data pre-processing and word
embedding techniques in the context of Arabic document classification in the domain
of health-related communication on social media. We evaluate 26 text pre-processings
applied to Arabic tweets within the process of training a classifier to identify healthrelated tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset
Physics-informed deep learning for modelling particle aggregation and breakage processes
No full textParticle aggregation and breakage phenomena are widely found in various industries such as chemical, agricultural and pharmaceutical processes. In this study, a physics-informed neural network is developed for solving both the forward and inverse problems of particle aggregation and breakage processes. In this method, the population balance equation is directly embedded in the loss function of a neural network so that the network can be trained efficiently and fulfil physical constraints. For the forward problems, solutions of population balance equations are obtained through the optimization of the neural network where the predictions well match the analytical solutions. In the inverse modelling, the data-driven discovery of model parameters of population balance equations is investigated. The sensitivity regarding the selection of different neural network structures is also investigated. The developed population balance equations embedded with neural network approach is promising for solving inverse problems of particle aggregation and breakage processes with noisy observation data
The effect of Pulsed Electric Field as a pre-treatment step in Ultrasound Assisted Extraction of phenolic compounds from fresh rosemary and thyme by-products, Innovative Food Science and Emerging Technologies
No full textEmerging extraction techniques, including pulsed electric field (PEF) and ultrasound (US), are attracting considerable interest in the recovery of bioactives. Though, limited work has focused on PEF application as pre-treatment for US assisted extraction to enhance the release of phenolics from herbs. Hence, the present study investigated the use of an optimized PEF pre-treatment to enhance the recovery of phenolics from fresh rosemary and thyme by-products in a subsequent US assisted extraction step. Total phenolic content (TPC), 2, 2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity and ferric reducing antioxidant power (FRAP) were assessed as an index of extraction efficacy. Qualitative and quantitative analyses were performed through liquid chromatography-mass spectrometry analyses to evaluate the influence of the methods on individual phenolic compounds and the formation of potential derivatives. The results indicated that in a number of cases PEF pre-treatment enhanced (p < 0.05) the recovery of phenolic compounds and antioxidant capacity compared to US individually.Teagas