Engineering and Information Technology Collected Works - Research Publications
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ItemThe Challenge of Cartilage Integration: Understanding a Major Barrier to Chondral Repair(MARY ANN LIEBERT, INC, 2022-02-01)Articular cartilage defects caused by injury frequently lead to osteoarthritis, a painful and costly disease. Despite widely used surgical methods to treat articular cartilage defects and a plethora of research into regenerative strategies as treatments, long-term clinical outcomes are not satisfactory. Failure to integrate repair tissue with native cartilage is a recurring issue in surgical and tissue-engineered strategies, seeing eventual degradation of the regenerated or surrounding tissue. This review delves into the current understanding of why continuous and robust integration with native cartilage is so difficult to achieve. Both the intrinsic limitations of chondrocytes to remodel injured cartilage, and the significant challenges posed by a compromised biomechanical environment are described. Recent scaffold and cell-based techniques to repair cartilage are also discussed, and limitations of existing methods to evaluate integrative repair. In particular, the importance of evaluating the mechanical integrity of the interface between native and repair tissue is highlighted as a meaningful assessment of any strategy to repair this load-bearing tissue. Impact statement The failure to integrate grafts or biomaterials with native cartilage is a major barrier to cartilage repair. An in-depth understanding of the reasons cartilage integration remains a challenge is required to inform cartilage repair strategies. In particular, this review highlights that integration of cartilage repair strategies is frequently assessed in terms of the continuity of tissue, but not the mechanical integrity. Given the load-bearing nature of cartilage, evaluating integration in terms of interfacial strength is essential to assessing the potential success of cartilage repair methods.
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ItemThe use of mobile apps and fitness trackers to promote healthy behaviors during COVID-19: A cross-sectional survey.(Public Library of Science (PLoS), 2022-08)OBJECTIVES: To examine i) the use of mobile apps and fitness trackers in adults during the COVID-19 pandemic to support health behaviors; ii) the use of COVID-19 apps; iii) associations between using mobile apps and fitness trackers, and health behaviors; iv) differences in usage amongst population subgroups. METHODS: An online cross-sectional survey was conducted during June-September 2020. The survey was developed and reviewed independently by co-authors to establish face validity. Associations between using mobile apps and fitness trackers and health behaviors were examined using multivariate logistic regression models. Subgroup analyses were conducted using Chi-square and Fisher's exact tests. Three open-ended questions were included to elicit participants' views; thematic analysis was conducted. RESULTS: Participants included 552 adults (76.7% women; mean age: 38±13.6 years); 59.9% used mobile apps for health, 38.2% used fitness trackers, and 46.3% used COVID-19 apps. Users of mobile apps or fitness trackers had almost two times the odds of meeting aerobic physical activity guidelines compared to non-users (odds ratio = 1.91, 95% confidence interval 1.07 to 3.46, P = .03). More women used health apps than men (64.0% vs 46.8%, P = .004). Compared to people aged 18-44 (46.1%), more people aged 60+ (74.5%) and more people aged 45-60 (57.6%) used a COVID-19 related app (P < .001). Qualitative data suggest people viewed technologies (especially social media) as a 'double-edged sword': helping with maintaining a sense of normalcy and staying active and socially connected, but also having a negative emotional effect stemming from seeing COVID-related news. People also found that mobile apps did not adapt quickly enough to the circumstances caused by COVID-19. CONCLUSIONS: Use of mobile apps and fitness trackers during the pandemic was associated with higher levels of physical activity, in a sample of educated and likely health-conscious individuals. Future research is needed to understand whether the association between using mobile devices and physical activity is maintained in the long-term.
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ItemExtracting Dynamical Understanding From Neural-Mass Models of Mouse Cortex(FRONTIERS MEDIA SA, 2022-04-25)New brain atlases with high spatial resolution and whole-brain coverage have rapidly advanced our knowledge of the brain's neural architecture, including the systematic variation of excitatory and inhibitory cell densities across the mammalian cortex. But understanding how the brain's microscale physiology shapes brain dynamics at the macroscale has remained a challenge. While physiologically based mathematical models of brain dynamics are well placed to bridge this explanatory gap, their complexity can form a barrier to providing clear mechanistic interpretation of the dynamics they generate. In this work, we develop a neural-mass model of the mouse cortex and show how bifurcation diagrams, which capture local dynamical responses to inputs and their variation across brain regions, can be used to understand the resulting whole-brain dynamics. We show that strong fits to resting-state functional magnetic resonance imaging (fMRI) data can be found in surprisingly simple dynamical regimes-including where all brain regions are confined to a stable fixed point-in which regions are able to respond strongly to variations in their inputs, consistent with direct structural connections providing a strong constraint on functional connectivity in the anesthetized mouse. We also use bifurcation diagrams to show how perturbations to local excitatory and inhibitory coupling strengths across the cortex, constrained by cell-density data, provide spatially dependent constraints on resulting cortical activity, and support a greater diversity of coincident dynamical regimes. Our work illustrates methods for visualizing and interpreting model performance in terms of underlying dynamical mechanisms, an approach that is crucial for building explanatory and physiologically grounded models of the dynamical principles that underpin large-scale brain activity.
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ItemTechnology-assisted assessment of spasticity: a systematic review(BMC, 2022-12-09)BACKGROUND: Spasticity is defined as "a motor disorder characterised by a velocity dependent increase in tonic stretch reflexes (muscle tone) with exaggerated tendon jerks". It is a highly prevalent condition following stroke and other neurological conditions. Clinical assessment of spasticity relies predominantly on manual, non-instrumented, clinical scales. Technology based solutions have been developed in the last decades to offer more specific, sensitive and accurate alternatives but no consensus exists on these different approaches. METHOD: A systematic review of literature of technology-based methods aiming at the assessment of spasticity was performed. The approaches taken in the studies were classified based on the method used as well as their outcome measures. The psychometric properties and usability of the methods and outcome measures reported were evaluated. RESULTS: 124 studies were included in the analysis. 78 different outcome measures were identified, among which seven were used in more than 10 different studies each. The different methods rely on a wide range of different equipment (from robotic systems to simple goniometers) affecting their cost and usability. Studies equivalently applied to the lower and upper limbs (48% and 52%, respectively). A majority of studies applied to a stroke population (N = 79). More than half the papers did not report thoroughly the psychometric properties of the measures. Analysis identified that only 54 studies used measures specific to spasticity. Repeatability and discriminant validity were found to be of good quality in respectively 25 and 42 studies but were most often not evaluated (N = 95 and N = 78). Clinical validity was commonly assessed only against clinical scales (N = 33). Sensitivity of the measure was assessed in only three studies. CONCLUSION: The development of a large diversity of assessment approaches appears to be done at the expense of their careful evaluation. Still, among the well validated approaches, the ones based on manual stretching and measuring a muscle activity reaction and the ones leveraging controlled stretches while isolating the stretch-reflex torque component appear as the two promising practical alternatives to clinical scales. These methods should be further evaluated, including on their sensitivity, to fully inform on their potential.
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ItemNo Preview AvailableLa jaula de oro: The Border Crossing Journey as Dream and Reality(Project MUSE, 2022)
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ItemA parametric similarity measure for extended picture fuzzy sets and its application in pattern recognition(University of Sistan & Baluchestan, 2022-11-01)This article advances the idea of extended picture fuzzy set (E-PFS), which is especially an augmentation of generalised spherical fuzzy set (GSFS) by releasing the restricted selection of p in the description of GSFSs. Moreover, by the use of triangular conorm term in the description of E-PFS, it indeed widens the scope of E-PFS not only compared to picture fuzzy set (PFS) and spherical fuzzy set (SFS), but also to GSFS. In the sequel, a given fundamental theorem concerning E-PFS depicts its more ability in comparison with the special types to deal with the ambiguity and uncertainty. Further, we propose a parametric E-PFS similarity measure which plays a critical role in information theory. In order for revealing the advantages and authenticity of E-PFS similarity measure, we exhibit its applicability in multiple criteria decision making entitling the recognition of building material, the recognition of patterns, and the selection process of mega project(s) in developing countries. Furthermore, through the experimental studies, we demonstrate that E-PFS is able to handle uncertain information in real-life decision procedures with no extra parameter, and it has a prominent role in decision making whenever the concepts of PFS, SFS and GSFS do not make sense.
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ItemOn the Functionality of Radar and Laser Ocean Wave Sensors(MDPI, 2022-09)Ocean wave design criteria are required for the design of offshore platforms and floating systems, which are derived using in situ measurements. However, there is uncertainty regarding the performance of the instruments used for the in situ measurements. The main instruments used by the offshore industry are the Datawell Directional Waverider buoy and Rosemount WaveRadar, with Laser instruments also having been used for specific studies. Recent reports indicate measurements from these three instruments differ in the order of 10% but given the quite disparate nature of the measurements made by these instruments, it is far from clear what the source of this difference is. This paper investigates the wave measurement principles of Radar and Laser instruments using linear wave field simulations to better understand how the instruments perform. The Radar and Laser simulations include modeling electromagnetic signal beam reflections from water surfaces of an area equal to their footprint sizes, considering their beam characteristics and antenna pattern. The study confirms that the Radar underestimates spectral levels at frequencies above 0.5 Hz due to its significantly larger footprint at the water sea surface compared to the Laser (5.25 m vs. 0.15 m). The Laser performs well for almost the entire frequency range for all the cases considered.
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ItemExtreme Waves(MDPI, 2022-05)The occurrence of exceptionally large waves in regions of high maritime traffic has severe consequences, ranging from complicating navigation routes to the loss of ships and human lives [...]
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ItemExpert-Machine Collaborative Decision Making: We Need Healthy Competition(IEEE COMPUTER SOC, 2022-09-01)Much has been written and discussed in previous years about human-AI interaction. However, the debate so far has mainly concentrated on "Aaverage" decision makers, neglecting important differences when it is experts who require support. In this article, we are going to talk about expert-machine collaboration for decision-making. We investigate the current approaches for expert decision support and exemplify the inefficiency of this approach for a real clinical decision-making problem. We propose two solutions for expert-machine collaboration to overcome the shortcomings of the current state of the art. We think that the proposed approaches open new horizons for expert-machine collaborative decision-making.