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Editorial: "The value of microbial bioreactors to meet challenges in the circular bioeconomy".
No full textThe use of microbial bioreactors has harnessed immense attention to support many
of the United Nation’s Sustainable Development Goals where this innovative platform
provides crucial insights into unlocking a diversity of solutions to society ranging from
fundamental research to hydrocarbon purification (Ghosh et al., 2023) and living lab
demonstrations (O’Neill et al., 2022). Bioreactor-based solutions have desirable
qualities such as safety compliance, scalability, non-toxicity, proven culture-based
processes, and ease of operation (Rowan and Galanakis, 2020). There has been a
growing interest in the progress of bioreactors to improve supply chains, enabling
circularity (the bioeconomy) and food sustainability, along with the potential of using
bio-based properties from food and waste products, which is aligned with the “One-
Health” concept and digitalization (O’Neill et al., 2022; Rowan et al., 2022). The
creative use of bioreactors can also be exploited as a novel toolbox approach for
informing and modeling the potential impact of climate change variance on aquatic
ecosystems, including testing new green innovation aligned with biodiversity (O’Neill
et al., 2022). In recent years, researchers have adopted bioreactor-based cultivation
methods to utilize microbial biomass for social advantages (O’Neill et al., 2022).
Overall, the manuscripts presented in this Research Topic provided significant
paradigms for the usage of bioreactors (Figure 1), which will benefit academia,
bioreactor manufacturers, cultivation technologists, small–mid-sized enterprises
(SMEs), and factory technicians.ye
Enhancement of scaffold in vivo biodegradability for bone regeneration using P28 peptide formulations
No full textThe field of bone tissue engineering has shown a great variety of bone graft substitute
materials under development to date, with the aim to reconstruct new bone tissue while maintaining
characteristics close to the native bone. Currently, insufficient scaffold degradation remains the critical
limitation for the success of tailoring the bone formation turnover rate. This study examines novel
scaffold formulations to improve the degradation rate in vivo, utilising chitosan (CS), hydroxyapatite
(HAp) and fluorapatite (FAp) at different ratios. Previously, the P28 peptide was reported to present
similar, if not better performance in new bone production to its native protein, bone morphogenetic
protein-2 (BMP-2), in promoting osteogenesis in vivo. Therefore, various P28 concentrations were
incorporated into the CS/HAp/FAp scaffolds for implantation in vivo. H&E staining shows minimal
scaffold traces in most of the defects induced after eight weeks, showing the enhanced biodegradability
of the scaffolds in vivo. The HE stain highlighted the thickened periosteum indicating a new
bone formation in the scaffolds, where CS/HAp/FAp/P28 75 g and CS/HAp/FAp/P28 150 g
showed the cortical and trabecular thickening. CS/HAp/FAp 1:1 P28 150 g scaffolds showed a
higher intensity of calcein green label with the absence of xylenol orange label, which indicates that
mineralisation and remodelling was not ongoing four days prior to sacrifice. Conversely, double
labelling was observed in the CS/HAp/FAp 1:1 P28 25 g and CS/HAp/FAp/P28 75 g, which
indicates continued mineralisation at days ten and four prior to sacrifice. Based on the HE and
fluorochrome label, CS/HAp/FAp 1:1 with P28 peptides presented a consistent positive osteoinduction
following the implantation in the femoral condyle defects. These results show the ability of
this tailored formulation to improve the scaffold degradation for bone regeneration and present a
cost-effective alternative to BMP-2.ye
Invisible encoded backdoor attack on DNNs using conditional GAN
No full textDeep Learning (DL) models deliver superior performance and have achieved remarkable results for classification and vision tasks. However, recent research focuses on exploring these Deep Neural Networks (DNNs) weaknesses as these can be vulnerable due to transfer learning and outsourced training data. This paper investigates the feasibility of generating a stealthy invisible backdoor attack during the training phase of deep learning models. For developing the poison dataset, an interpolation technique is used to corrupt the sub-feature space of the conditional generative adversarial network. Then, the generated poison dataset is mixed with the clean dataset to corrupt the training images dataset. The experiment results show that by injecting a 3% poison dataset combined with the clean dataset, the DL models can effectively fool with a high degree of model accuracy.ye
Studies on the comparative effectiveness of x-rays, gamma rays and electron beams to inactivate microorganisms at different dose rates in industrial sterilization of medical devices
Get PDFThe radiation resistance of Bacillus pumilus spores to gamma rays, X-rays, and electron beam (e-beam) was
investigated using industrial irradiators operating at various dose rates. The dose rates were as follows: gamma 1
and 10 kGy/h; X-ray 10 and 200 kGy/h; e-beam 2000 kGy/h. The regression analysis showed that survivor
curves were log10 linear for all three sources within the investigated absorbed dose range of 1–6 kGy, irrespective
of the dose rate applied. All irradiation technologies were equally efficient to inactivate the spores, which is
reflected in their comparable D-values (p > 0.05), and dose rate had no impact on the microbicidal efficacy.
These results suggest that wherever a specified minimum dose is delivered, the sterilization dose can be trans ferred between irradiation technologies in industrial sterilization of medical devices without any impact on
product sterility. These findings from a novel single study encompassing all available industrial radiation
technologies for the purpose of medical devices sterilization, advance our understanding of microbial destruction
as related to exposure to important sterilization modalities, which will help inform future applicability of these
technologies for emerging industry opportunities.n
D7.5. Sustainability strategy - 1st version.
Get PDFWith a strong focus on building strong partnerships, and effective exploitation of project outcomes, the sustainability strategy for the RUN-EU PLUS project includes actions to be undertaken during the project (version 1, M24) and after the end of the project (version 2, M36). The establishment of joint governance and management systems greatly contributes to ensuring the sustainability of the project, fostering synergies within the alliance and the complementarity in relation to the RUN European University (Erasmus+ project). Supporting the dual goal of further cooperation in R&I and the sustainability of the Professional Practice-based Research Degrees, the sustainability strategy will also include recommendations to national and European policymakers and other stakeholders, supported by concrete instruments such as a cost-benefit analysis, and suggested strategies to overcome barriers, such as response to the COVID-19 pandemic restrictions and impacts.
RUN-EU PLUS WP3 (Common Research and Innovation Agenda) has developed an Action Plan for validation and accreditation, and an Economic Resource/ Impact Assessment Model to analyse the added-value, resource model and cost-benefit model to support and enhance conditions for the future sustainability of the project’s outputs with a focus on the Professional Practice-based Research Degrees. Upon project completion, an Innovation Impact and Scaling Potential Report will analyse the innovation potential and scalability of the Professional Practice-based Research Degrees, including the potential for synergy with other trans-European initiatives. The Sustainability Strategy of RUN-EU PLUS will also contribute and benefit from the actions planned in the RUN-EU alliance, namely: enlargement of the network to other HEIs in other countries, engagement of additional associated partners to expand the capability and relevance of the alliance in terms of its educational and research offer, plan for the realisation of the 10-year vision of the alliance for the creation of a Multinational European Zone for Interregional Development applying a quadruple helix approach.
The Sustainability Strategy for the RUN-EU PLUS project is presented in 2 reports at M24 and M36 of the project. The strategy will describe the best practices and recommendations to ensure the operational and financial sustainability of the RUN-EU PLUS Practice-based master’s and Doctoral Programmes beyond the lifetime of the RUN-EU PLUS project.
This first report (D7.5 Sustainability Strategy) represents the first version of the sustainability strategy for the RUN-EU PLUS project. It describes the strategic plan to ensure the continuation and further development of cooperation in Research & Innovation (R&I) and the sustainability and development of Professional Practice-based Research degrees. This initial sustainability report
D7.5 Sustainability Strategy – 1st version
6
presents the first version of the concrete methodology for actions which support the project’s sustainability to be undertaken during the project.
D7.6 Sustainability Strategy is the final sustainability and development strategy which will be made available at the end of the project. It will approve the main conclusions and recommendations to national and European policymakers on cost-benefit analysis, identification of the main barriers and suggestions, including the main strategies and methodological approaches to respond to operational and financial challenges which may arise.
The final conclusions in terms of identification of the main barriers to the development of Professional Practice-based Research Degrees, the policy recommendations and the cost-benefit analysis of the actions and programmes will be incorporated in this final sustainability report (D 7.6) which will be disseminated to the relevant national and European stakeholders
Deep reinforcement learning-based industrial robotic manipulation
Get PDFPick and place robotic systems can be found in all major industries in order to increase
throughput and efficiency. But most of the pick-and-place applications in the industry
today have been designed through hard-coded, static programming approaches. These
approaches completely lack the element of learning. This requires, in case of any
modification in the task or environment, reprogramming from scratch is required every
time. This thesis targets this particular area and introduces the learning ability in the
robotic pick-and-place operation which makes the operation more efficient, and
increases its strength of adaptability. We divide this thesis into three parts. In the first
part, we focus on learning and carrying out pick and place operations on various objects
moving on a conveyor belt in a non-visual environment i.e., without using vision
sensors, using proximity sensors. The problem under consideration is formulated as a
Markov Decision Process (MDP). and solved by using Reinforcement Learning (RL).
We train and test both model-free off-policy and on-policy RL algorithms in this
approach and perform their comparative analysis. In the second part, we develop a self learning deep reinforcement learning-based (DRL) framework for industrial pick-and place of regular and irregular-shaped objects tasks in a cluttered environment. We
design the MDP and solve it by deploying the model-free off-policy Q-learning
algorithm. We use the pixelwise-parameterization technique in the fully connected
network (FCN) being used as the Q-function approximator. In the third and main part,
we extend this vision-based self-supervised DRL-based framework to enable the robotic
arm to learn and perform prehensile (grasping) and non-prehensile (non-grasping,
sliding, pushing) manipulations together in sequential manner to improve the efficiency
and throughput of the pick-and-place task. We design the MDP and solve it by using
the Deep Q-networks. We consider three robotic manipulations from both prehensile and non-prehensile category and design large network of three FCNs without creating
any bottleneck situation. The pixel-wise parameterization technique is utilized for Q function approximation. We also present the performance comparisons among various
variants of the framework and very promising test results at varying clutter densities
across a range of complex scenario test cases.ye
Liquid-based 4D printing of shape memory nanocomposites: a review
Get PDFSignificant advances have been made in recent years in the materials development of liquidbased
4D printing. Nevertheless, employing additive materials such as nanoparticles for enhancing
printability and shape memory characteristics is still challenging. Herein, we provide an overview
of recent developments in liquid-based 4D printing and highlights of novel 4D-printable polymeric
resins and their nanocomposite components. Recent advances in additive manufacturing technologies
that utilise liquid resins, such as stereolithography, digital light processing, material jetting and direct
ink writing, are considered in this review. The effects of nanoparticle inclusion within liquid-based
resins on the shape memory and mechanical characteristics of 3D-printed nanocomposite components
are comprehensively discussed. Employing various filler-modified mixture resins, such as nanosilica,
nanoclay and nanographene, as well as fibrous materials to support various properties of 3D printing
components is considered. Overall, this review paper provides an outline of liquid-based 4Dprinted
nanocomposites in terms of cutting-edge research, including shape memory and mechanical
properties.ye
Numerical investigation of the impact of injectors location on fuel mixing in the hifire 2 scramjet combustor
Get PDFIn scramjets, the position and direction of the injectors plays a crucial role for fuel/air mixing
and combustion efficiency. Fuel injection is still a potential topic of research to be addressed, in
fact an effective fuel injection strategy is critical for increasing the streamwise vorticity that has
been found to be the main responsible for the fuel-air mixing in compressible flows. In fact, the
position and the direction of the fuel injectors, the presence of a cavity scramjet has a critical
influence on the density and pressure gradients, and consequently on the baroclinic term that
is a source of vorticity. In this regard, this research wants to investigate the nature of the mixing
in supersonic flows, investigating the contribution between the streamwise and stretching
component for the vorticity. Numerical modelling of supersonic combustion using Large Eddy
Simulations was carried out in HIFiRE 2 Scramjet to better understand the physics of the
combustion and mixing.ye
Sensory substitution with balance and weightbearing training after stroke : development of a prototype and mixed methods design /
No full textIntroduction
The aim of this doctoral thesis was to investigate the feasibility and effectiveness of
sensory substitution interventions in improving weight-bearing symmetry and balance
outcomes after stroke. Stroke is the third leading cause of death and number one cause of
acquired disability in Ireland, with multiple impacts on individuals, health systems and
society. Balance impairment (including weight-bearing asymmetry) is one of the
principal dysfunctions observed. The exact physiological process explaining the
relationship between variables influencing weight-bearing symmetry and balance after
stroke was yet to be explored, a gap this thesis attempted to explain.
Methods
This thesis was structured as a PhD by publication and includes a mixed-methods
approach. Systematic literature review and meta-analysis methodology was used to
collect, appraise and synthesise current quantitative data investigating sensory
substitution interventions. The NextStep™ prototype (developed for this research) was
used to investigate combined tactile and auditory sensory substitution alongside weight bearing and balance training for a case study and pilot randomised controlled trial in a
cohort of stroke survivors.
Results
The results indicated that sensory substitution interventions, including the NextStep™
prototype, were feasible and highlighted preliminary effectiveness of the interventions
for improving weight-bearing symmetry and balance outcomes after stroke. Feasibility
was indicated through positive findings of safety, retention, adherence, and acceptability.
There were also positive findings for feasibility and preliminary effectiveness for these
iv
interventions to improve balance, weight-bearing symmetry, gait and subjective
outcomes in patients with other neurological and orthopaedic disorders.
Conclusion
The findings of this thesis provide conceptual understanding of the relationship between
variables which influence weight-bearing symmetry and balance after stroke. The
findings also broadly contribute to existing knowledge on interventions to improve
balance, weight-bearing symmetry, gait and subjective outcomes in patients with
neurological and orthopaedic disorders. To better understand the implications of these
results, future studies should explore the thesis recommendations.n
Prediction of Hotspots in Injection moulding by Using Simulation, In-mould Sensors, and Machine Learning /
Get PDF© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Injection moulding is an industrial process for the mass production of plastic components, with many parameters affecting the quality of this process. Hotspot regions in the component occur due to non-optimised process variables or limitations in the cooling system and can lead to warpage or shrinkage. Hotspots should be minimised to avoid part defects and achieve the required dimensional tolerances for precision components. This work outlines a machine-learning-based approach for predicting the maximum hotspot temperature in an injection moulded component using process simulation and in-mould sensor data. The hotspots were identified through software simulation, and then their locations and temperatures were confirmed through an actual experiment using in-mould thermocouples. Two different machine learning approaches, artificial neural network (ANN) and support vector regression (SVR), were developed using the extracted data from the sensors and a design of experiment (DOE) method. The performance of linear and Gaussian kernels was compared for the SVR method. The Gaussian SVR resulted in superior
performance compared to the linear kernel. The Gaussian SVR was then compared to the ANN prediction method, where ANN
showed a slightly better prediction performance. This study has two primary outcomes. First, we show the simulation results
can be used to identify critical areas of the part for real-time monitoring. Secondly, embedding sensors in these locations and
applying a machine learning approach to the data, provides a good indication of potential quality issues such as warpage
and shrinkage post-production. The use of ANN indicates an accurate prediction performance, facilitating rapid optimisation
of the process for the minimisation of hotspots.ye