Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
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Article Non-stationary Bayesian Spatial Model for Disease Mapping based on Sub-regions
(SAGE Publications, 2024) Abdul Fattah, Esmail; Krainski, Elias Teixeira; Niekerk, Janet Van; Rue, Haavard; Statistics Program; Extreme Computing Research Center; Computer, Electrical and Mathematical Science and Engineering (CEMSE) DivisionThis paper aims to extend the Besag model, a widely used Bayesian spatial model in disease mapping, to a non-stationary spatial model for irregular lattice-type data. The goal is to improve the model’s ability to capture complex spatial dependence patterns and increase interpretability. The proposed model uses multiple precision parameters, accounting for different intensities of spatial dependence in different sub-regions. We derive a joint penalized complexity prior for the flexible local precision parameters to prevent overfitting and ensure contraction to the stationary model at a user-defined rate. The proposed methodology can be used as a basis for the development of various other non-stationary effects over other domains such as time. An accompanying R package fbesag equips the reader with the necessary tools for immediate use and application. We illustrate the novelty of the proposal by modeling the risk of dengue in Brazil, where the stationary spatial assumption fails and interesting risk profiles are estimated when accounting for spatial nonstationary. Additionally we model different causes of death in Brazil, where we use the new model to investigate the spatial stationarity of these causes.
Article Sensing fugitive hydrogen emissions
(Springer Science and Business Media LLC, 2024-03-14) Cai, Yichen; Chatterjee, Sudipta; Salama, Khaled N.; Li, Lain-Jong; Huang, Kuo-Wei; Chemistry Program, Division of Physical Sciences and Engineering and KAUST Catalysis Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Physical Science and Engineering (PSE) Division; KAUST Solar Center (KSC); Vice President for Research-VPR; Electrical and Computer Engineering Program; Advanced Membranes and Porous Materials Research Center; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Chemical Science Program; KAUST Catalysis Center (KCC); Department of Chemistry, Birla Institute of Technology and Science − Pilani, K.K. Birla Goa Campus, Zuarinagar, India; Department of Mechanical Engineering, University of Hong Kong, Hong Kong, P. R. China; Agency for Science, Technology and Research, Institute of Materials Research and Engineering and Institute of Sustainability for Chemicals, Energy and Environment, Singapore, SingaporeFor the transition to a sustainable energy sector, massive hydrogen production and use is crucial. There is growing awareness of a connection between an indirect global warming potential and the production of hydrogen, so its fugitive emissions must be addressed. This Comment emphasizes the need for affordable hydrogen-sensing methods to benefit safety, energy efficiency and the climate.
Article Characteristics of Stacked GaInN-Based Red, Green, and Blue Full-Color Monolithic μLED Arrays Connected via Tunnel Junctions
(John Wiley and Sons Inc, 2024-03-12) Saito, Tatsunari; Hasegawa, Naoki; Suehiro, Yoshinobu; Koide, Norikatsu; Takeuchi, Tetsuya; Kamiyama, Satoshi; Iida, Daisuke; Ohkawa, Kazuhiro; Iwaya, Motoaki; Electrical and Computer Engineering Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Electrical and Computer Engineering Program; Department of Materials Science and Engineering, Meijo University, Nagoya, 468-8502, JapanThe use of μLEDs in self-luminous displays is crucial for the development of high-efficiency displays for virtual space services. For this purpose, a stacked monolithic GaInN-based micro light-eimtting diodes (μLED) device that emits red, green, and blue (RGB) light is obtained. The RGB layers are connected via tunnel junction (TJ) layers. The pixel density is 330 ppi (35 × 15 μm2 of μLED mesa area corresponding to the emission). The cross-sectional transmission electron microscopy analysis indicates that there is not a significant increase in threading dislocations even after stacking the three RGB active layers and two TJ layers. In addition, the X-ray diffraction reciprocal lattice space mapping shows that all the layers grow almost coherently with respect to the GaN substrate. A detailed evaluation of the resulting device shows that the BT.2020 color gamut coverage reaches a maximum of 71%, and the luminance is sufficiently high for head-mounted display applications. Redshifting of the emission wavelength of the red μLEDs by increasing the InN mole fraction in the GaInN active layer would likely lead to improvement of the color gamut coverage in future studies.
Preprint Spatial Latent Gaussian Modelling with Change of Support
(arXiv, 2024-03-13) Chacon Montalvan, Erick; Atkinson, Peter M.; Nemeth, Christopher; Taylor, Benjamin M.; Moraga, Paula; Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Lancaster Environment Centre, Lancaster University, United Kingdom; Department of Mathematics and Statistics, Lancaster University, United Kingdom; School of Mathematical Sciences, University College Cork, IrelandSpatial data are often derived from multiple sources (e.g. satellites, in-situ sensors, survey samples) with different supports, but associated with the same properties of a spatial phenomenon of interest. It is common for predictors to also be measured on different spatial supports than the response variables. Although there is no standard way to work with spatial data with different supports, a prevalent approach used by practitioners has been to use downscaling or interpolation to project all the variables of analysis towards a common support, and then using standard spatial models. The main disadvantage with this approach is that simple interpolation can introduce biases and, more importantly, the uncertainty associated with the change of support is not taken into account in parameter estimation. In this article, we propose a Bayesian spatial latent Gaussian model that can handle data with different rectilinear supports in both the response variable and predictors. Our approach allows to handle changes of support more naturally according to the properties of the spatial stochastic process being used, and to take into account the uncertainty from the change of support in parameter estimation and prediction. We use spatial stochastic processes as linear combinations of basis functions where Gaussian Markov random fields define the weights. Our hierarchical modelling approach can be described by the following steps: (i) define a latent model where response variables and predictors are considered as latent stochastic processes with continuous support, (ii) link the continuous-index set stochastic processes with its projection to the support of the observed data, (iii) link the projected process with the observed data. We show the applicability of our approach by simulation studies and modelling land suitability for improved grassland in Rhondda Cynon Taf, a county borough in Wales.
Article Direct Ink Writing of Strained Carbon Nanotube-Based Sensors: Toward 4D Printable Soft Robotics
(American Chemical Society (ACS), 2024-03-14) Joharji, Lana N.; Alam, Fahad; Elatab, Nazek; SAMA Laboratories, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Electrical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi ArabiaFour-dimensional (4D) printing has attracted significant attention, because it enables structures to be reconfigured based on an external stimulus, realizing complex architectures that are useful for different applications. Nevertheless, most previously reported 4D-printed components have focused on actuators, which are just one part of a full soft robotic system. In this study, toward achieving fully 4D-printed systems, the design and direct ink writing of sensors with a straining mechanism that mimics the 4D effect are explored. Solution-processable carbon nanotubes (CNTs) were used as the sensing medium, and the effect of a heat-shrinkable shape-memory polymer-based substrate (i.e., potential 4D effect) on the electronic and structural properties of CNTs was assessed, followed by their application in various sensing devices. Herein, we reveal that substrate shrinking affords a more porous yet more conductive film owing to the compressive strain experienced by CNTs, leading to an increase in the carrier concentration. Furthermore, it improves the sensitivity of the devices without the need for chemical functionalization. Interestingly, the results show that, by engineering the potential 4D effect, the selectivity of the sensor can be tuned. Finally, the sensors were integrated into a fully 4D-printed flower structure, exhibiting their potential for different soft robotic applications.