67639 research outputs found
Sort by
Attributional life cycle assessment of recycling and disposal strategies for construction and demolition waste
No full textThe management of Construction and Demolition Waste (CDW) poses significant environmental challenges in Saudi Arabia, exacerbated by rapid urbanization and the Giga Projects under Vision 2030. This study presents a Life Cycle Assessment (LCA) of CDW management practices across seventeen municipalities, providing the first region-specific evaluation of environmental impacts using comprehensive data. By integrating primary and secondary data from local sources, Ecoinvent 3.91, and GaBi Sphera, the study evaluates key CDW disposal methods: sanitary landfilling, incineration, and recycling. The findings reveal that advanced recycling infrastructure (e.g., compactor trucks, recycling facilities) significantly reduces non-renewable energy use, Global Warming Potential (GWP), and downstream emissions, particularly in high CDW-generating regions like Riyadh and Jeddah. Notably, recycling achieves a GWP reduction of up to 75 million kg CO2-eq annually for key materials like mixed soil and concrete. Furthermore, the study contributes to global CDW management literature by presenting a replicable LCA framework that can be applied to other rapidly urbanizing economies facing similar waste management challenges. This framework offers valuable insights for policymakers, urban planners, and researchers seeking to develop sustainable CDW management strategies aligned with circular economy principles and international sustainability goals
Mode Control and Dynamic Population Gratings in Quantum-Dot Lasers
No full textSingle-mode operation is essential for integrated semiconductor lasers, yet most solutions rely on regrowth, etched gratings, or other complex fabrication steps that limit scalability. We show that quantum-dot (QD) lasers can achieve stable single-mode lasing through a simple cavity design using dynamic population gratings (DPGs). Owing to the low lateral carrier diffusion of QDs, a strong standing-wave-induced carrier grating forms in a reverse-biased saturable absorber and provides self-aligned, mode-selective feedback not attainable in quantum-well devices. A single-ring laser achieves 46 dB side-mode suppression ratio (SMSR), while a dual-ring Vernier laser delivers ( 46 nm) tuning range and up to 52.6 dB SMSR, with continuous-wave operation up to . The laser remains single-mode under dB external optical feedback and supports isolator-free data transmission at 32 Gbps. These results establish DPG-enabled QD lasers as a simple and scalable route to tunable, feedback-resilient on-chip light sources for communication, sensing, and reconfigurable photonic systems.The authors acknowledge the support of the KAUST Core Labs for device fabrication. This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) under Award No. RFS-TRG20246196, ORFS-CRG12-2024-6487, RFS-OFP2023-5558, and FCC/1/5939
CCDC 2389644: Experimental Crystal Structure Determination : catena-((mu-Iodo)-(3-iodopyridine)-copper(i))
No full textAn entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures
Scalable skewed Bayesian inference for latent Gaussian models
No full textApproximate Bayesian inference for the class of latent Gaussian models can be achieved efficiently with integrated nested Laplace approximations (INLA). Based on recent reformulations in the INLA methodology, we propose a further extension that is necessary in some cases like heavy-tailed likelihoods or binary regression with imbalanced data. This extension formulates a skewed version of the Laplace method such that some marginals are skewed and some are kept Gaussian while the dependence is maintained with the Gaussian copula from the Laplace method. Our approach is formulated to be scalable in model and data size, using a variational inferential framework enveloped in INLA. We illustrate the necessity and performance using simulated cases, as well as a case study of a rare disease where class imbalance is naturally present
Variational Inference for Quantum HyperNetworks
No full textBinary Neural Networks (BiNNs), which employ single-bit precision weights, have emerged as a promising solution to reduce memory usage and power consumption while maintaining competitive performance in large-scale systems. However, training BiNNs remains a significant challenge due to the limitations of conventional training algorithms. Quantum HyperNetworks offer a novel paradigm for enhancing the optimization of BiNN by leveraging quantum computing. Specifically, a Variational Quantum Algorithm is employed to generate binary weights through quantum circuit measurements, while key quantum phenomena such as superposition and entanglement facilitate the exploration of a broader solution space. In this work, we establish a connection between this approach and Bayesian inference by deriving the Evidence Lower Bound (ELBO), when direct access to the output distribution is available (i.e., in simulations), and introducing a surrogate ELBO based on the Maximum Mean Discrepancy (MMD) metric for scenarios involving implicit distributions, as commonly encountered in practice. Our experimental results demonstrate that the proposed methods outperform standard Maximum Likelihood Estimation (MLE), improving trainability and generalization
Life cycle assessment of ammonia and hydrogen as alternative fuels for marine internal combustion engines
No full textThis study presents a life cycle assessment of marine internal combustion engines using ammonia and hydrogen as alternative fuels. The research aims to provide an evaluation of the environmental impacts of these alternatives as compared to conventional fossil fuels using three assessment methods: ReCiPe Midpoint 2016, CML-IA, and IPCC, complemented by Monte Carlo simulation to account for uncertainties. Results demonstrate that the green ammonia with on-board reforming and green hydrogen scenario shows the lowest environmental impacts, potentially providing greenhouse gas reductions of 75% from 0.65 to 0.14 kgCO2 eq/kWh compared to a fossil fuel. For alternative fuels, environmental impacts are dominated by the production phase, whereas fossil fuels are mainly influenced by their utilisation, accounting for 84% of total emissions. While all alternative fuel scenarios satisfy IMO emissions regulations, an exhaust after-treatment system would be required to meet criteria pollutant limits. Uncertainty analysis indicates that alternative fuels have more variability of environmental impact, especially for the blue ammonia, which is 16.9%, and blue hydrogen scenarios, which is 15.8%.This publication has been produced with support from the HYDROGENi Research Centre (hydrogeni.no), performed under the Norwegian research program FMETEKN. The authors acknowledge the industry partners in HYDROGENi for their contributions and the Research Council of Norway (333118)
Variational structure of Fokker-Planck equations with variable mobility
No full textWe study Fokker--Planck equations with symmetric, positive definite mobility matrices capturing diffusion in heterogeneous environments. A weighted Wasserstein metric is introduced for which these equations are gradient flows. This metric is shown to emerge from an optimal control problem in the space of probability densities for a class of variable mobility matrices, with the cost function capturing the work dissipated via friction. Using the Nash-Kuiper isometric embedding theorem for Riemannian manifolds, we demonstrate the existence of optimal transport maps. Additionally, we construct a time-discrete variational scheme, establish key properties for the associated minimizing problem, and prove convergence to weak solutions of the associated Fokker-Planck equation.This work originated from a collaboration between the authors during H. Liu’s visit to KAUST. H. Liu was partially supported by the National Science Foundation under Grant DMS1812666. AET was supported by King Abdullah University of Science and Technology (KAUST), baseline funds No. BAS/1/1652-01-01. AET thanks Prof. Cleopatra Christoforou and Prof. Dominik Inauen for helpful discussions during the preparation of this wor
Measuring information transfer between nodes in a brain network through spectral transfer entropy
No full textBrain connectivity characterizes interactions between different regions of a brain network during resting-state or performance of a cognitive task. In studying brain signals, such as electroencephalograms (EEG), one formal approach to investigating connectivity is through an information-theoretic causal measure called transfer entropy (TE). To enhance the functionality of TE in brain signal analysis, we propose a novel methodology that captures cross-channel information transfer in the frequency domain. Specifically, we introduce a new measure, the spectral transfer entropy (STE), to quantify the magnitude and direction of information flow from a band-specific oscillation of one channel to another band-specific oscillation of another channel. The main advantage of our proposed approach is that it formulates TE in a novel way to perform inference on band-specific oscillations while maintaining robustness to the inherent problems associated with filtering. In addition, an advantage of STE is that it allows adjustments for multiple comparisons to control false positive rates. Another novel contribution is a simple yet efficient method for estimating STE using vine copula theory. This method can produce an exact zero estimate of STE (which is the boundary point of the parameter space) without the need for bias adjustments. With the vine copula representation, a null copula model, which exhibits zero STE, is defined, thus enabling straightforward significance testing through standard resampling. Lastly, we demonstrate the advantage of the proposed STE measure through numerical experiments and provide interesting and novel findings on the analysis of EEG data in a visual-memory experiment.The authors thank Sarah Bernadette Aracid for the elaborate artworks (Figures 1, 3, 4, 5, 6 and 7)
Passive subsurface imaging around the KAUST shallow well site using a multi-scale seismic acquisition system
No full textPassive seismic monitoring is an effective tool to assess seismic activity and analyze subsurface structures in and around well sites because it does not require an active source and, thus, is cheap, non-intrusive, and environmentally friendly. The on-campus shallow monitoring well at King Abdullah University of Science and Technology (KAUST) was drilled from February to April 2024, reaching a target depth of ~392 meters, and subsequently cased and cemented along its entire length to total depth. The main challenge for seismic monitoring at the site is the unconsolidated topmost layer of sand and construction debris that strongly weakens the seismic wave energy. At this site, we deployed various types of seismic sensors targeting different spatial and frequency-resolution scales to monitor seismic energy during drilling and from other random surface sources (e.g., vehicular traffic). The seismic monitoring system includes four three-component broadband stations deployed over a period of 5 to 18 months before drilling and a dense array of autonomous STRYDE nodes (measuring the vertical component of the particle acceleration field), which acquired data for about one month during drilling. The array was composed of 89 nodes with a spacing of 2 m and a total offset of 176 m. Seismic interferometry was applied on a portion of the data acquired while drilling operations were stopped (about 7 days) to synthesize surface waves and extract their dispersive behavior (i.e., dispersion curves) between 5 and 15 Hz. The resulting dispersion curves were then used to estimate a 2D near-surface shear wave velocity model down to 20 m depth. The horizontal-to-vertical spectral ratio (HVSR) was instead used on the recordings from the broadband seismic stations to estimate the site response transfer functions from which we extracted four 1D shallow shear wave velocity profiles. We first computed the HVSR for 14 days each month, then stacked the HVSR for 5 months to obtain the final HVSR of each station. The HVSR curves show two main peak frequencies ~2 Hz and 6 Hz. 1D shear wave velocity profiles down to 150 were finally obtained by joint inversion of the HVSR and dispersion curves. The velocity profiles from the broadband seismic stations and the closest profile from the nodes are consistent in the depth range where they overlap. Moreover, the shear wave profiles agree with the lithology interpreted from drilling cuttings. Our project demonstrates that a multi-scale seismic monitoring system can effectively reveal the subsurface structure of a specific site
Green quantum computing in the sky
No full textThe cryogenic cooling requirements of quantum computing pose significant challenges to sustainable deployment. We propose deploying quantum processors on stratospheric High Altitude Platforms (HAPs), leveraging −50 °C ambient temperatures to reduce cooling demands by 21%. Our analysis demonstrates that quantum-enabled HAPs support 30% more qubits than terrestrial quantum data centers while maintaining superior reliability, especially when leveraging advanced hardware capabilities. By leveraging strategic atmospheric positioning, this solar-powered solution enables sustainable, high-performance quantum computing