NUI Maynooth Eprint Archive
Not a member yet
18159 research outputs found
Sort by
A stochastic dynamic programming approach for the machine replacement problem
No full textThis paper addresses both the modeling and the resolution of the replacement problem for a population of machines. The main objective is the computation of a minimum cost replacement policy, which, based on the status of each machine, determines whether one or more machines have to be replaced over a given finite time horizon.
The replacement problem of a set of machines can be regarded as a sequential decision-making problem under uncertainty. Thanks to this, we propose a novel formulation for such problems consisting of a composition of discrete-time multi-state Markov Decision Processes (MDPs), one for each specific machine. The underlying optimization problem is formulated as a stochastic Dynamic Programming (DP), and then solved by using the principles of the backward DP algorithm. Moreover, to deal with the curse of dimensionality due to the high-cardinality state–space of real-world/industrial applications, a new generalized multi-trajectory Least-Squares Temporal Difference (LSTD) based method is introduced. The resulting algorithm computes an approximate optimal cost function by: (i) running Monte Carlo simulations over different trajectories of a given length; (ii) embedding the policy improvement step within the recursive LSTD iterations; (iii) enforcing an off-policy mechanism to improve the LSTD exploration capabilities. A study on the convergence properties of the proposed approach is also provided. Several numerical examples are given to illustrate its effectiveness in terms of parametric sensitivity, computational burden, and performance of the computed policies compared with some heuristics defined in the literature
Implementation of photon partial distinguishability in a quantum optical circuit simulation
No full textWe are concerned with numerical simulations of quantum optical circuits under certain realistic conditions, specifically that photon quantum states are not perfectly indistinguishable. The partial photon distinguishability presents a
serious limitation in implementation of optical quantum information processing. In order to properly assess its effect
on quantum information protocols, accurate numerical simulations, which closely emulate quantum circuit operations,
are essential. Our specific objective is to provide a computer implementation of the partial photon distinguishability
which is in principle applicable to existing simulation techniques used for ideal quantum circuits and which avoids
a need for their significant modification. Our approach is based on the Gram-Schmidt orthonormalization process,
which is well suited for our purpose. Photonic quantum states are represented by wavepackets which contain information on their time and frequency distributions. In order to account for the partial photon distinguishability, we expand
the number of degrees of freedom associated with the circuit operation extending the definition of the photon channels
to incorporate wavepacket degrees of freedom. This strategy allows to define delay operations in the same footing as
the linear optical elements
Going Global? Defining, Characterising and Constructing Global Citizenship
No full textLed by the principal investigator for
the present project, Dr. Barry Cannon,
from Maynooth University’s Sociology
Department, the project provided
participants with theoretical inputs around
democracy, citizenship, and globalisation,
supporting and challenging the group to
examine, deepen and extend their work
on global citizenship. The project report
concluded that:
• Globalisation has created new
complexities around the sovereignty of
states, who are the traditional guarantors
of citizenship. It has opened up new
avenues for citizenship claims while
bringing challenges in the nature and
realisation of these claims.
• The concept of global citizenship is
one response to the challenge of
conceptualising democracy beyond the
nation state frame. Yet it was noted that
there was no specific model for global
citizenship and in fact contemporary
citizenship responds to multiple
sovereignties, below, within, across and
beyond states.
• The report recommended that further
work be done with the sector to explore
these issues on global citizenship
Educational relational networks: indigenous and feminist worlding. A response to Troy Richardson
No full textThis paper is a response to Troy Richardson’s Terence
McLaughlin’s Lecture. In it, I discuss how Richardson provides a unique reading of relationality, drawing together technology studies, Indigenous Education and feminist philosophy of education. Seeking to walk with key ideas he develops, this response also points to a possible limitation in seeing Noddings ethic of care as part of a feminist relational ontology that can help inform new ways of understanding ‘machine learning’. In particular, I introduce the notion of worlding as a way of complementing Richardson’s reading of relationality – a notion that has profound implications for pedagogical practice
Factor structure of the international trauma questionnaire in trauma exposed LGBTQ+ adults: Role of cumulative traumatic events and minority stress heterosexist experiences.
No full textExposure to prolonged and/or multiple types of psychological trauma and stressors has been shown to be more
strongly associated with ICD-11 complex posttraumatic stress disorder (CPTSD) than posttraumatic stress disorder (PTSD). Lesbian, gay, bisexual, trans- and queer adults (LGBTQ+) are at a heightened risk of exposure to
traumatic events, and minority stressors including harassment, discrimination, rejection by family, and isolation. Objective: To examine the factor structure of the international trauma questionnaire (ITQ), a self-report
measure of PTSD and CPTSD, and the associations of cumulative lifetime trauma exposure assessed via the
life events checklist and minority stress assessed via the daily heterosexist experiences scale, with CPTSD
(three PTSD symptom clusters, three clusters reflecting disturbances in self-organization [DSO]) among
LGBTQ+ adults. Method: Participants comprised 225 LGBTQ+ adults (including 74 transgender and gender\ud
diverse individuals; age range: 18–60 years; M/SD = 31.35/9.48) residing in Spain. Results: Confirmatory factor analyses indicated that both a first-order six-factor model and a hierarchical two-factor model, comprising
PTSD and DSO as second-order factors, fit the data best. Cumulative traumatic events score was associated
with PTSD, and cumulative minority stress was associated with PTSD and DSO. Among the minority stress
subscales, harassment based on gender expression was positively associated with all symptom clusters of
PTSD and DSO. Conclusion: This is the first study to examine the role of minority stressors alongside exposure to psychological traumas in ICD-11 PTSD and CPTSD and emphasizes the inclusion of minority stressors
in trauma-related assessments
A structured comparison of causal machine learning methods to assess heterogeneous treatment effects in spatial data
No full textThe development of the “causal” forest by Wager and Athey (J Am Stat Assoc
113(523): 1228–1242, 2018) represents a significant advance in the area of explanatory/causal machine learning. However, this approach has not yet been widely
applied to geographically referenced data, which present some unique issues: the
random split of the test and training sets in the typical causal forest design fractures
the spatial fabric of geographic data. To help solve this issue, we use a simulated
dataset with known properties for average treatment effects and conditional average treatment effects to compare the performance of CF models across different
definitions of the test/train split. We also develop a new “spatial” T-learner that can
be implemented using predictive methods like random forest to provide estimates
of heterogeneous treatment effects across all units. Our results show that all of the
machine learning models outperform traditional ordinary least squares regression at
identifying the true average treatment effect, but are not significantly different from
one another. We then apply the preferred causal forest model in the context of analysing the treatment effect of the construction of the Valley Metro light rail (tram)
system on on-road CO2 emissions per capita at the block group level in Maricopa
County, Arizona, and find that the neighbourhoods most likely to benefit from treatment are those with higher pre-treatment proportions of transit and pedestrian commuting and lower proportions of auto commuting
The ultrasensitive detection of p-nitrophenol using a simple activated carbon electrode modified with electrodeposited bismuth dendrites
No full textBismuth dendrites were electrodeposited onto an activated glassy carbon electrode and the resulting modified
electrode was employed in the electrochemical detection of para-nitrophenol, an aromatic aquatic pollutant. The
carbon electrode was activated by cycling between − 2.0 and 2.0 V vs. SCE to create active sites that can promote
the electron transfer reaction. Bismuth dendrites were subsequently deposited at a potential of − 1.0 V vs. SCE
for 400 s. A near 5-fold increase in the peak current associated with the conversion of para-nitrophenol (100 μM)
to para-hydroxyaminophenol was obtained on activating the glassy carbon electrode. A more impressive 7-fold
increase in the peak current was achieved on decorating the activated glassy carbon with bismuth dendrites.
Calibration curves with linear regions from 1.6 to 170 μM and between 0.005 and 1.6 μM para-nitrophenol were
obtained to give a LOD value of 0.18 nM and a sensitivity of 29.4 μA μM− 1 cm− 2 for the lower concentration
range. Good repeatability, reproducibility and selectivity were achieved, while recovery values between 96.5 and
106.7 % were obtained in water samples. In addition, the bismuth dendrites were easily regenerated through an
oxidation step at 0.6 V vs. SCE followed by a 400 s electrodeposition period in 1.0 mM Bi(III)
Examining the Validity of ICD-11 PTSD and Complex PTSD using international data
No full textPosttraumatic stress disorder (PTSD) is traditionally understood as a disorder that occurs more commonly in women than in men, and in younger age groups than in older age groups. The objective of this study was to determine if these patterns are also observed in relation to ICD-11 PTSD and Complex PTSD (CPTSD). Secondary data analysis was performed using data collected from three nationally representative samples from the Republic of Ireland (N = 1,020), the United States (N = 1,839) and Israel (N = 1,003), and one community sample from the United Kingdom (N = 1,051). Estimated prevalence rates of ICD-11 PTSD were higher in women than in men in each sample, and at a level consistent with existing data derived from DSM-based models of PTSD. Furthermore, rates of ICD-11 PTSD were generally lower in older age groups for men and women. For CPTSD, there was inconsistent evidence of sex and age differences, and some indication of a possible interaction between these two demographic variables. Despite considerable revisions to PTSD in ICD-11, the same sex and age profile was observed to previous DSM-based models of PTSD. CPTSD, however, does not appear to show the same sex and age differences as PTSD. Theoretical models that seek to explain sex and age differences in trauma-related psychopathology may need to be reconsidered given the distinct effects for ICD-11 PTSD and CPTSD
Hybrid coordination scheme based on fuzzy inference mechanism for residential charging of electric vehicles
No full textThe charging of electric vehicles (EVs) at residential premises is orchestrated through either centralized or decentralized control mechanisms. The former emphasizes adherence to power grid constraints, employing demand management techniques to restrict EV charging when the aggregated demand exceeds a predetermined threshold, which may result in user discontentment. Conversely, the latter endows EV users with the authority to self-regulate their charging behavior to optimize cost, allowing a multitude of interconnected EVs to charge during the same off-peak window. However, this decentralized approach gives rise to the herding problem, wherein a simultaneous surge in EV charging during off-peak periods burdens the power grid, leading to potential system overloads. This paper presents a hybrid coordinating scheme that integrates a fuzzy inference mechanism to synergistically blend the merits of centralized and decentralized coordinations. The proposed hybrid coordination scheme aims to minimize peak load, alleviate herding, and optimize charging costs while ensuring adherence to EV users’ charging obligations at the lowest feasible expense. The problem is formulated with the introduction of a novel fuzzy objective function and subsequently resolved through the fuzzy inference mechanism. The fuzzy inference encapsulates independent and uncertain price profiles, consumption load patterns, and state-of-charge data collected from the power grid, households, and EV domains, which are effectively integrated into weighted variables for the requesting EVs. The proposed hybrid coordinating scheme leverages weighted variables to optimize the objective function, enabling the determination of an optimal charging schedule that satisfies the charging requirements of the requesting EVs, while adhering to stringent power grid operational constraints and minimizing charging costs. To assess the efficacy of the hybrid coordination scheme, we conducted two meticulous case studies employing the IEEE 34 bus system as a testbed, thoroughly evaluating performance metrics encompassing charging cost, load profile impact, and peak-to-average ratio. The results demonstrate the superior performance of the proposed hybrid coordination scheme compared to alternative charging strategies, including uncoordinated charging, standard-rate charging, time-of-use charging, and two-layer decentralized approaches
Uncertainty estimation in wave energy systems with applications in robust energy maximising control
No full textUnder control action, wave energy devices typically display nonlinear hydrodynamic behaviour, making the design of energy maximising control somewhat onerous. One solution to approach the optimal performance for nonlinear control problem under model mismatches is to employ a linear control strategy, which can be robust to linear model mismatches. However, accurate characterisation of the uncertainty in the linear model is vital, if the controller is to adequately capture the full extent of the uncertainty, while not being overly conservative due to overestimation of the uncertainty. This paper describes a procedure, employing CFD-based numerical tank experiments, to accurately produce a nominal linear empirical transfer function model, along with an accurate estimate of the uncertainty bounds in that linear model, due to hydrodynamic uncertainty. A robust control case study is provided, illustrating the nominal model estimation process, and its corresponding uncertainty set, including the complete procedure, required to generate the robust controller. Robust control results, on the fully nonlinear CFD model, are provided to demonstrate the efficacy of the modelling and control philosophy