20505 research outputs found
Sort by
Exploring factors shaping consumer behaviour towards circular fashion: a focus on Generations Y and Z
Get PDFThe fashion industry faces significant challenges due to its linear systems and environmental impact. As sustainability gains priority among consumers, especially Generations Y and Z, the industry is urged to transition towards a circular economy. Despite this, the ‘attitude-behaviour gap’ persists, indicating minimal impact on consumer behaviour. This study explores factors influencing consumer behaviour towards circular fashion, focusing on Generations Y and Z. Nine hypotheses were developed, exploring relationships among environmental awareness, circular fashion awareness, willingness to change, willingness to pay a premium, and circular behaviour. Online surveys yielded 408 responses from participants from developing and developed countries. Structural Equation Modelling (SEM) was used for hypothesis testing. Results show consumer behaviour is influenced by environmental awareness and circular fashion awareness, and willingness to change. Additionally, purchasing decisions are driven by product quality and durability. The findings assist fashion businesses in aligning strategies with consumer perception among Generations Y and Z.Frontiers in Sustainabilit
Addressing incremental backstepping control limitations with direct online Gaussian process adaptation
Get PDFSensor-based incremental control is a recently developed technique that reduces dependency on precise model knowledge. This approach uses measurements or estimates of current state derivatives and actuator states to linearise the dynamics with respect to the previous time instant, albeit at the expense of increased sensitivity to measurement quality. Unknown system behaviour due to unforeseen malfunctions in measurement or actuation systems can lead to significant performance degradation. The novelty of this paper lies in a new method that combines the reduced model dependency of sensor-based Incremental Backstepping (IBKS) control with the adaptive capabilities of data-driven Gaussian Processes (GPs). The resulting controller exhibits significantly reduced sensitivity to model and measurement uncertainties. A theoretical proof of the global uniform ultimate boundedness of the IBKS tracking error is provided. The direct GP-based adaptation reduces the error bound and offers both long-term dependency learning and noise filtering capabilities. The effectiveness of the proposed approach is demonstrated through a missile flight control example.This research is partially funded by the European Union in the scope of INCEPTION project, which has received funding from the EU’s Horizon2020 Research and Innovation Programme under grant agreement No. 723515.Journal of the Franklin Institut
Exploring tasks and challenges in human-robot collaborative systems: a review
Get PDFThis paper presents an in-depth exploration of Human-Robot Collaborative Systems (HRCSs) within industrial environments, a dynamic field that has witnessed significant advancements due to technological innovation and the increasing integration of Artificial Intelligence (AI). As industries evolve towards more collaborative and adaptive manufacturing systems, the dynamic interaction between humans and robots becomes pivotal. This study reviews the current state of HRCSs, focusing on the challenges of task allocation and skill alignment, safety, trust, and the psychological wellbeing of human workers. We review control strategies and architectural frameworks that underpin effective human-robot interactions (HRI), emphasising the critical role of AI in enhancing decision-making processes and the adaptability of collaborative efforts. Our review sheds light on the complexities involved in designing HRCSs that are not only efficient but also cognisant of the human experience, advocating for a balanced approach that leverages the strengths of both human and robotic counterparts. We argue that research in and implications of HRCSs should extend beyond technical considerations, touching on ethical, social, and organisational dimensions, thereby contributing to the broader discourse on the future of work in the era of Industry 4.0 and future Industry 5.0.Engineering and Physical Sciences Research CouncilRobotics and Computer-Integrated Manufacturin
Advanced lignocellulose bioprocessing for Aloe vera leaf rind through novel termite gut microbiome consortia for acetone butanol ethanol (ABE) production: metagenomics insights and process economic analysis
Get PDFThe 16S rRNA and ITS raw sequence reads of the termite microbiome and ETC-3 were submitted to the NCBI, with the accession numbers provided under the Bioproject PRJNA1182483.Consolidated bioprocessing (CBP) of lignocellulosic biomass (LCB) using microbes simplifies the process, eliminates enzyme cost and reduces the overall processing expenses. In this regard, termite gut, a potent reservoir of microbial symbionts produces various lignocellulolytic enzymes which acts synergistically to degrade LCB. However, the effectiveness of adapting the microbes with LCB for improved lignocellulolytic enzyme secretion and substrate degradation has been overlooked. Hence, in this study adaptive laboratory (ALE) of termite gut isolates was performed with various substrates such as saw dust (SD) and Aloe vera leaf rind (AVLR) under different conditions. Among the consortia, enriched termite consortium (ETC-3) showed the highest degradation of lignin (51.86 ± 2.03 %, w/w), hemicellulose (29.27 ± 1.29 %, w/w) and cellulose (41.97 ± 2.99 %, w/w) with maximum specific enzyme activities. High throughput sequencing revealed the significant enrichment of Proteobacteria (88.95 %) and Ascomycota (99.94 %) groups in ETC-3. Further, the efficiency of ETC-3 in consolidated pretreatment and bioprocessing (CPBP) and CBP of AVLR towards acetone, butanol and ethanol (ABE) production was studied. Compared to the CPBP, CBP resulted in 1.6-fold higher glucose yield which subsequently enhanced the butanol yield (7.97 ± 0.40 g/L). Finally, cost benefit analysis ensured the economic feasibility of process strategies for AVLR valorization.International Journal of Biological Macromolecule
Phenotyping the nutritional status of crops using proximal and remote sensing techniques
Get PDFUnderstanding the nutritional needs of crops is crucial for ensuring their health and
maximising yield. However, the capability to accurately measure relevant physical
characteristics (phenotypes) of important crops in response to complex nutrient stresses is
limited. For crop breeders and researchers, the existing capacity to characterise crops with
adequate precision, detail and efficiency is hindering significant progress in crop
development. In this PhD thesis, the use of advanced sensing techniques to assess the
nutritional status of African crops was explored, focusing on three main objectives.
First, the use of a handheld proximal sensor was investigated to evaluate the spectral
properties of quinoa and cowpea crops grown under different N and P supplies in controlled
glasshouse conditions (Chapter 3). By analysing these spectral properties, the aim was to
identify spectral indices that could show early signs of N and P stress separately in the plants.
These stress indicators were related to the overall performance of the crops. Spectral indices
were found that could distinguish between N and P stress at the early growth stage of the
crops. However, identifying spectral indices for P stress was limited, particularly in cowpea
due to the shorter wavelength range of the handheld device. The results showed significant
relationships between the spectral indices and traits related to the morphology, physiology
and agronomy of the crops.
Second, it was demonstrated that different levels of N impact the drought responses of spring
wheat (Chapter 4). By evaluating morpho-physiological changes in the plants under high N
and low N conditions, an understanding of how spectral reflectance measured at the leaf level
could help distinguish between combined and complex stresses such as drought and nutrient
deficiency was investigated. The results showed a greater amplitude of drought response in
plants that were supplied with high N compared to low N levels, with interactive effects on
many morphological and physiological traits. Out of a group of 39 different SRIs, only the
Renormalised Difference Vegetation Index (RDVI) and the Red Difference Vegetation Index
(rDVI_790) showed better accuracy in detecting drought stress. The results also revealed that
indices sensitive to chlorophyll levels, such as the chlorophyll Index (mNDblue_730),
Greenness Index (G) and Lichtenthaler Index (Lic2), as well as red-edge indices like
Modified Red-Edge Simple Ratio (MRESR), chlorophyll Index Red-Edge (CIrededge) and
Normalised Difference Red-Edge (NDRE), were more accurate in detecting N stress.
Lastly, the effectiveness of using spectral information from images collected from a drone
and spectral reflectance measured with proximal sensors on the ground were compared for
detecting N stress in winter wheat under field conditions (Chapter 5). By comparing these
two sensing methods, it was assessed which approach is more accurate, reliable and cost-
effective for assessing the N nutritional needs of the crop in real-world agricultural settings.
The results indicated that the NDVI measured on the ground at the leaf level could accurately
detect the small changes in N levels earlier compared to the drone NDVI and canopy level
NDVI and for assessing the agronomic performance of winter wheat. Overall, this PhD
research sheds new light on the potential of advanced sensing techniques to improve crop
management practices and enhance agricultural productivity by providing timely and accurate
information about the nutritional status of the studied crops.PhD in Environment and Agrifoo
Mechanistic origin of size effects in crystal plasticity: strain gradients and other theories explained
No full textMechanical properties–strength, fracture toughness, fatigue resistance–arise from the inherently multiscale nature of deformation and failure. Forces at the macroscopic level drive atomic-scale processes, which are regulated by mesoscale attributes such as grain size and dislocation structures. Thus, the engineering of novel materials requires a thorough understanding of complex interactions across multiple length scales. However, mechanistic explanations for size effects remain elusive in the literature. Instead, most modeling efforts have relied on phenomenological formulations, which offer limited predictive accuracy beyond their calibration domains. This paper systematically explores mechanistic contributions to size effects to predict single- and poly-crystal mechanical responses. We identify three size-dependent mechanisms that can be incorporated into plastic deformation models to capture size effects in single- and poly-crystals for metals and alloys under tension, compression, and bending. The size-dependent algorithms do not introduce new phenomenological parameters but rely on material-invariant formulations that can be employed across single-phase FCC materials without recalibration. Notably, this understanding enables the tuning of microstructures for specific mechanical properties before manufacturing. The analysis further explains the relative contribution of size effects on isotropic and kinematic hardening as well as their significance for different crystallographic orientations. We further provide a physical interpretation for the origin of strain gradient theories and mechanistic size effects in the absence of geometry-induced strain gradients. We conclude by highlighting the coupling of mechanisms and their relative contributions at different strain levels.International Journal of Plasticit
Evaluation of high-temperature wear behaviour of selective laser melted 17-4 PH stainless steel through laser shock peening
Get PDFThis study investigates the high-temperature wear resistance of laser shock peened (LSP) 17-4 precipitation-hardened stainless steel fabricated via selective laser melting (SLM), addressing the critical need for enhanced surface durability in elevated-temperature conditions. Four material conditions were analysed: as-printed (AP), heat-treated (AP + HT), laser shock peened (AP + LSP), and both treatments combined (AP + HT + LSP). Wear tests using a pin-on-disc setup were conducted at room temperature (RT), 150 °C, 250 °C, and 350 °C. The AP + LSP condition consistently exhibited the lowest wear, attributed to the introduction of compressive residual stresses, severe plastic deformation, and strain hardening. In contrast, AP + HT and AP + HT + LSP suffered higher wear due to oxidation-assisted delamination and brittle morphology. At RT and 350 °C, AP + LSP achieved 34 % and 17 % lower wear rates than AP + HT, respectively. These findings highlight the effectiveness of LSP in enhancing the high-temperature tribological performance of SLM 17-4 PH stainless steel and offer valuable insights for its deployment in aerospace and energy applications.Journal of Materials Research and Technolog
Overcoming barriers to fuel cell electric vehicles adoption: greener environmental governance in the Indian subcontinent
Get PDFFuel cell electric vehicles (FCEVs) are gaining global attention as a sustainable transportation solution, yet their adoption in emerging South Asian economies remains limited. This study addresses the research gap by identifying and prioritizing barriers to FCEV adoption in India, Pakistan, and Bangladesh, regions underrepresented in existing literature. A hybrid methodology integrating Exploratory Factor Analysis (EFA), Fuzzy Set Theory (FST), and Evidential Reasoning Approach (ERA) was applied to analyze responses from 221 experts across five stakeholder groups. The results reveal that high cost, inadequate infrastructure, and lack of skilled workforce are the most critical and stable barriers. The study also proposes a scenario-based stability analysis for targeted and adaptive policy design. The study offers practical implications for policymakers and industry leaders aiming to accelerate the FCEV transition in emerging markets by providing region-specific insights.Energ
Estimation and visualisation of brain functional and effective connectivity
Get PDFFunctional and effective connectivity are two important concepts in the field of
neuroscience that describe how different regions of the brain communicate and
work together to support various cognitive and behavioural functions. Despite
the many advances in functional and effective connectivity research, there are
still several important research gaps that need to be addressed. This thesis
explores the novel estimation and visualisation of brain functional and effective
connectivity using electroencephalography recordings, with a particular focus on
its potential impact on the diagnosis and monitoring of neurological disorders.
This thesis proposes two novel methods for estimating brain functional
connectivity and effective connectivity. The first method, Revised Hilbert-Huang
Transformation, outperforms wavelet-based methods in terms of promising
features and time-frequency resolution, providing a potential biomarker and
diagnostic tool for Alzheimer's disease. The second method, causality detection
attention-based convolutional neural networks, effectively estimates effective
connectivity networks and identifies disrupted connectivity in Alzheimer’s
disease patients. These methods contribute to the growing literature on
connectivity estimation and offer valuable insights into the neural mechanisms
underlying cognitive processes and neurodegenerative diseases, providing
potential diagnostic and monitoring tools for healthcare professionals. This
thesis also introduces a novel directed structure learning GNN (DSL-GNN) to
leverage several EBC estimations to extract discriminative biomarkers for
dementia classification. In studies of Alzheimer's disease, epilepsy, Parkinson's
disease, and workload classification, the thesis demonstrates that the proposed
brain connectivity methods have better performance compared with traditional
methods based on individual channel. It suggests that functional and effective
connectivity may track more changes from healthy people to patients to a
certain extent, providing the possibility for earlier and more accurate diagnoses.
Specifically, the thesis finds that specific regions of the brain can contribute to
the diagnosis of epilepsy and dementia disease as well as workload
classification based on brain connectivity. By advising the appropriate
placement of electroencephalography sensors based on these identified
regions, doctors and researchers can more efficiently and accurately diagnose
and classify these neurological disorders, reducing the burden on healthcare
systems.PhD in Manufacturin
Novel deep learning approaches for flight delays prediction
Get PDFThis thesis focuses on the development of predictive models for flight delay using
the United States (US) Bureau of Transportation Statistics (BTS) dataset. The
research aims to assess the potential of the BTS data for creating data-driven
flight delay predictive models. Exploratory Data Analysis (EDA) reveals complex
relationships, irregularities, outliers, missing values and invalid values within the
dataset, posing challenges for traditional Machine Learning algorithms and
resulting in low-performance models.
To address these limitations, six advanced prediction algorithms are developed.
The approaches include binary classification using a filter-based procedure and
deep-stacked auto-encoder, a hybrid ensemble learner, a fully connected Bi-
Long-Short Term Memory (LSTM), a social ski driver conditional autoregressive
value at risk optimisation-aware deep neural network, and a novel gradient
mayfly-based optimisation DeepONet approach.
The US BTS dataset, along with expert-defined records, is used to validate the
proposed algorithms. The research findings demonstrate that an optimisation-
aware data-driven predictive approach yields reliable and robust results for flight
delay prediction. Among the developed approaches, the gradient mayfly
optimisation DeepONet model, particularly the proposed data-driven
optimisation-aware DeepONet, outperforms other strategies in terms of
prediction error on benchmark evaluation metrics.
The validation results highlight the effectiveness of the data-driven optimisation-
aware DeepONet model, achieving a minimum prediction error of 0.0043. This
suggests that more than 90% of flights can be accurately predicted based on the
findings of this research.PhD in Aerospac