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Evaluating Combinations of Biochar, Compost, Water-Absorbing Polymers, and Nutrients to Identify Optimal Potting Mixes for Plants and Crops in Saudi Arabia
No full textThis study evaluates combinations of engineered biochar, compost, water-absorbing polymers, and nutrients as potting mixes for urban greening efforts in Saudi Arabia. The focus is to create potting media based on engineered biochar (Carbosoil), which is produced by pyrolyzing chicken manure at 500℃ followed by a post-treatment. If successful, this approach may present substitutes for traditional potting mixes, especially those based on peat moss that have a notoriously high carbon footprint. In the primary experiment with Acacia tortilis (seedlings and seed-derived), Dodonaea viscosa, and Leptadenia SP seedlings, twenty-four treatment regimens were established in a standardized potting medium by crossing bulk materials, binding agents, conventional NPK fertilizers, biochar-based slow-release fertilizers, micronutrients, chemical stimulants, and a microbial stimulant. Height progression and stem diameter development were monitored for over ten weeks as a function of the potting media composition. The group treated with biochar-based-slow-release-urea in a non-compost medium across all the different plants led to the largest gain in the stem diameter, producing an average stem diameter of 3.8 ± 0.1 mm, a 28 % increase over the control’s 3.0 ± 0.1 mm. In the biomass assays, Acacia tortilis (seedlings and seed-derived), Dodonaea viscosa, and Leptadenia SP seedlings planted in a potting mix amended with 60 % (v/v) Carbosoil yielded the next most pronounced effect, having 54.5 g total biomass; 40.2 g shoot + 14.3 g root and dry biomass of 402.7 g total biomass; 307.6 g shoot + 95.1 g root. Soil physicochemical measurements taken at week 4 indicated that Carbosoil treatments maintained neutral pH and moderate electrical conductivity, supporting nutrient availability
Next, Capsicum annuum was chosen as a representative of sensitive non-native plants to study the effects of several potting mixes. Combinations of Carbosoil, peat moss, compost, and an NPK-based fertilizer + Micromix at 5–80 % volume ratios were evaluated in a sandy potting medium. Peat-moss amendments delivered the highest performance across all metrics, at 80 % (v/v) amendment, including mean shoot height 14.3in (± 0.4 in; +30% vs. control) stem diameter 6.5mm (± 0.2 mm; +40% vs. control), fresh biomass 24.8g (± 0.9 g; 17% vs. control) and chlorophyll content measured 74.6 ± 2.1 CCI; +40% vs. control. Fertilizer blends at the same 80 % inclusion delivered moderate improvements: shoot height of 9.1 in (± 0.3 in; +15 % vs. control), stem diameter of 5.1 mm (± 0.2 mm; +20 %), and chlorophyll content of 62 CCI (± 1.8; +18 %). Total fresh biomass was (5.425 g ± 0.7 g) and dry mass (shoot 0.584 g; root 0.0893 g). Notably, all treatments amended with compost or Carbosoil at 80% or 50% v/v exhibited complete seedling mortality within the first week of the experiment, indicating potential phytotoxic effects or unsuitability of such high amendment concentrations under the given conditions. Across the gradient of amendment concentrations (0 % to 80 %) for all various components of the potting mix, substrate pH declined from 7.2 to 5.5, while electrical conductivity rose from 0.4 to 3.5 dS m⁻¹. These shifts in soil chemistry correlated strongly with declines in plant performance beyond 50 % amendment, indicating that both acidic conditions and elevated salinity can offset the benefits of higher organic or mineral inputs. Taken together, these findings should enable the development of locally produced, low-cost, and sustainable potting mixes for optimal seedling development for urban greening efforts in KSA and beyond
Evaluating and mitigating bias in AI-based medical text generation
No full textArtificial intelligence (AI) systems, particularly those based on deep learning models, have increasingly achieved expert-level performance in medical applications. However, there is growing concern that such AI systems may reflect and amplify human bias, reducing the quality of their performance in historically underserved populations. The fairness issue has attracted considerable research interest in the medical imaging classification field, yet it remains understudied in the text-generation domain. In this study, we investigate the fairness problem in text generation within the medical field and observe substantial performance discrepancies across different races, sexes and age groups, including intersectional groups, various model scales and different evaluation metrics. To mitigate this fairness issue, we propose an algorithm that selectively optimizes those underserved groups to reduce bias. Our evaluations across multiple backbones, datasets and modalities demonstrate that our proposed algorithm enhances fairness in text generation without compromising overall performance.X.C. was supported by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) through grant award 8481000078
Fuelprop: Fuel property prediction from ATR-FTIR spectroscopic data
No full textSynthetic fuels are crucial for decarbonizing the transportation sector. A significant challenge lies in the rapid and efficient characterization of these fuels. Chemometric methods using ATR-FTIR data offer a potential alternative to conventional techniques. This study expands the applicability and performance of chemometric models by providing an extensive ATR-FTIR spectral dataset and exploring various data enhancement strategies. Data enhancement was achieved by semi-supervised data generation, consistency enforcement through unsupervised data augmentation, and data imputation using synthetic spectra blending and pseudo-labeling. Models were trained on surrogate fuels and rigorously tested on real fuels, representing out-of-distribution testing conditions. We believe that this work will enhance the adoption of chemometric models for fuel characterization.This work was funded by the Office of Sponsored Research at King Abdullah University of Science and Technology (KAUST)
Hierarchical porous carbon biochar nanotubes encapsulated metal nanocrystals with a strong metal-carbon interaction for high-performance supercapacitors
No full textBiomass-based engineered hierarchical porous carbons derived from biomass waste are deemed cost-effective and sustainable materials for supercapacitors, thanks to their tunable properties, e.g., high conductivity, and stability; however, their multiple preparation steps and low capacitance are deemed significant challenges. These issues were addressed herein through the rational design of hierarchical porous carbon nanotubes (p-CNTs) enriched with metal nanocrystals (M/p-CNTs) (M=Cu, Co, W, Bi, and Mo) via the ultrasonic impregnation of coconut silks in metal precursor solutions and pyrolysis under nitrogen. This approach promotes a green, one-pot method that eliminates the need for activation steps or hazardous chemicals, and endows the formation of nanotubes through a strong-metal carbon interaction, resulting in metal electron-deficiency, with a BET surface area of 288.6 m2/g, along with interconnected tri-modal porosity, which accelerates charge mobility and facilitates ion transport. These advantages significantly improved the performance of supercapacitors, which was fine-tuned by the intermolecular electron transfer between metals and p-CNTs, which reached the optimum specific capacitance of 558.1F/g at 0.5 A/g, energy density of 27.9 Wh/kg, and power density of 150/3000 W/kg at 1/10 A/g on Cu/p-CNTs, which was amongst highest reported for active supercapacitors. These findings pave the way for the simple and sustainable synthesis of active materials from biomass for energy storage devices.This project is received by Dr. Khouloud Jlassi and Jehad K. El-Demellawi, who will pay the APC of the article. So they should exist as co-authors
Hydrogen Sulfide Splitting via the Iodine Thermochemical Cycle: A Theoretical and Experimental Study of a Potential Route to Hydrogen Production
No full textHydrogen sulfide (H2S) is a hazardous and toxic gas that is often found as a byproduct in the oil and gas industry processes. Traditional methods for converting H2S into less harmful chemicals, such as the Claus process, are energetically demanding and produce sulfur oxides. In this study, we present an aqueous phase thermochemical cycle that utilizes H2S and iodine (I2) to produce hydrogen iodide (HI)–an intermediate for hydrogen (H2) production. This cycle was characterized by combined theoretical and experimental work, predicting two possible stepwise reaction mechanisms for the degradation of H2S by I2 and the production of HI. One mechanism is more prone to take place at high H2S concentrations, with an overall reaction of 8H2S + 7I2 → S8 + H2 + 14HI and thus resulting in an HI:H2S = 1.75:1 ratio. The other takes place at lower H2S concentrations involving water (H2O) as a reactant as per the overall reaction H2S + 3I2 + 2H2O → SO2 + 6HI, resulting in a higher yield of HI according to a HI:H2S = 6.00:1 ratio. Our quantum chemistry and kinetics calculations indicate that both mechanisms are kinetically and thermodynamically hindered, especially the latter with a higher standard Gibbs free energy barrier height. Our pH measurements in a reactor at different H2S concentrations, H2S:I2 ratios, and temperatures were used to monitor the H+ and H+/H2S mole profiles as a function of time. The resulting profiles indicate a transition between two mechanisms whose relative prominence is controlled by an interplay between the investigated initial conditions and that may correspond to those characterized by our calculations. Our newly proposed mechanisms in the aqueous phase differ from those previously proposed by other authors, and the gathered fundamental mechanistic details may facilitate the design of catalyzed processes that would make this H2S-I2 thermochemical cycle more competitive for relevant industrial processes such as H2 production and H2S removal.The work reported in this publication was supported by Saudi Aramco Company (project 5168) and the Supercomputer Laboratory at King Abdullah University of Science and Technology (KAUST)
CO₂ Mineralization Potential of the Zeolite Scolecite: Experimental and Modelling Approaches
No full textAtmospheric CO₂ levels have increased significantly over the past century. Furthermore, and as demonstrated in many works, CO₂ is a key cause of the current global warming, and there is an urgent need to investigate the pathways to achieve net-zero emissions. Carbon capture and storage (CCS) has been identified as one of the primary pathways to achieving net-zero emissions. Within CCS, the geological storage of CO₂ plays a crucial role, and one of the methods for geological storage is CO₂ mineralization.
In this study, we examine the potential suitability of natural calcium-rich zeolite scolecite for carbon dioxide (CO₂) sequestration through carbonate precipitation in the subsurface.
Batch experiments at 60 °C with solutions of different carbonate and bicarbonate concentration were conducted to assess the reactivity of scolecite, track the fluid evolution, and quantify the carbonation using CHNS analysis and scanning electron microscopy (SEM). In addition, geochemical modelling with PHREEQC was used to approximate the mineral saturation state and compare with experimental observations.
The findings suggest that scolecite is chemically active in alkaline solutions and may induce the precipitation of calcite at elevated pH and carbonate concentrations. SEM analysis showed rhombohedral calcite crystals on treated samples, whereas CHNS data indicated a gradual increase in carbon content over time. These trends were also reflected with the PHREEQC model, where carbonate saturation appeared to be reached under the given conditions. The lack of substantial secondary precipitation of silicate minerals suggests that carbonation might proceed without significant pore-clogging.
While the results of this study point to the possibility that scolecite could contribute to CO₂ mineralization, uncertainties remain regarding its long-term behavior and effectiveness under more complex, dynamic flow conditions. Additional research is needed to evaluate its performance at larger scales and in natural settings
Coverage Analysis of Large-Scale HAPS Networks Using Directional Beams
No full textHigh-altitude platform stations (HAPS) are pivotal in next-generation wireless networks for reducing core network burdens and enabling cost-effective communication. In this article, we propose a spherical stochastic geometry-based analytical framework for the coverage performance evaluation of HAPS networks. Considering the significant influence of directional antenna gain on interference evaluation, we analyze coverage performance under a general channel model that accommodates various beam patterns. Analytical expressions of the uplink and downlink coverage probabilities in cellular and cell-free networks are provided respectively and their accuracy are verified by Monte Carlo simulation. Furthermore, the influences of network-level and physical-level parameters on coverage probability are studied. Finally, several factors that align with the analytical framework of this article are discussed
Explainability and Efficiency in Spatio-Temporal Models: Applications to Traffic Forecasting
No full textTraffic forecasting is a critical component of Intelligent Transportation Systems (ITS), supporting applications such as adaptive traffic signal control, dynamic route planning, and congestion mitigation. Recent advancements in deep learning have led to the development of spatio-temporal models that effectively capture spatial and temporal dependencies in traffic data, thereby enhancing prediction accuracy. Despite these advancements, several challenges persist. Firstly, the increasing complexity of model architectures often results in substantial computational requirements, hindering real-time deployment due to prolonged training and inference times. Secondly, many existing models lack interpretability, making it difficult to understand the underlying factors influencing traffic dynamics. Thirdly, publicly available traffic datasets frequently lack diverse features, such as incident reports and road metadata, limiting the development of models that can account for various factors affecting traffic flow.
To address these challenges, this thesis proposes several approaches and a foundation dataset for traffic analysis that advances both the efficiency and explainability of traffic forecasting. For model efficiency, we develop QuoGNN, a lightweight graph neural network that introduces a context-enhanced similarity graph and quotient-based reasoning to capture spatial-temporal dependencies with reduced computational overhead. To improve interpretability, we present two complementary approaches. The first leverages a multi-task learning framework with a Graph Gate LSTM to model distributed telecommunication traffic, revealing implicit spatial relations across base stations. The second incorpo-
rates expert knowledge and large language models to construct concept-based explanations, enabling scenario-specific interpretation of model predictions. Furthermore, we introduce TraffiDent, a large-scale multimodal traffic dataset that includes three years of time series data, over 1.4 million incident records, and rich road-level metadata. TraffiDent supports a wide range of novel tasks, such as post-incident forecasting, causal analysis, and incident classification
Effectiveness of combined UV-C and bacteriophage approach over repeated cleaning cycles to alleviate membrane fouling of anaerobic bioreactors
No full textA combined ultraviolet C (UV-C) and bacteriophage treatment has been previously identified as an effective and environmentally sustainable approach to mitigating biofouling in anaerobic membrane bioreactors (AnMBRs) treating municipal wastewater. This study investigated the efficacy of this treatment across multiple cleaning cycles to assess its long-term applicability. The results demonstrated that the combined treatment effectively delayed transmembrane pressure (TMP) regrowth and maintained stable reactor performance over three cleaning cycles. Polysaccharide removal remained consistent throughout, while the efficiency of protein and bacterial cell removal declined progressively with repeated cycles.
Transcriptional analysis revealed an adaptive response among key biofilm-forming bacteria, including Geobacter spp, characterized by increased expression of genes associated with biofilm formation, particularly flagellar proteins. These findings suggest that repeated treatment may induce bacterial resistance, diminishing long-term efficacy. Despite this limitation, the UV-C and bacteriophage treatment presents a promising alternative to chemical cleaning, with the potential to minimize environmental impact and membrane degradation leading to a potential broader adoption of biologically-based cleaning approaches in membrane-based systems.This study is supported by Near-term Grand Challenge (AI) REI/1/5233-01-01 and KAUST-MEWA SPA REP/1/6112-01-01 awarded to P.-Y. Hong. We thank KAUST FM Utilities team for granting access raw wastewater samples
Hydroxypropyl Cellulose-Based Thermochromic Hydrogels for Smart Passive Cooling
No full textVehicles parked under the sun are probably the most affected by overheating from sunlight coming in through clear windows. The drastic rise in temperatures in them poses significant risks, and when combined with excessive air conditioning use, it leads to substantial energy waste. Thermochromic smart windows enable passive cooling by automatically adjusting their transparency in response to temperature changes. This study covers the making of a thermochromic hydrogel film using a dual-network structure that consists of hydroxypropyl cellulose (HPC) and polyacrylamide (PAAM) with calcium chloride for tunable transition temperatures. The optical, thermal, and mechanical characteristics of the developed hydrogels received a great deal of attention through characterization and experimental validation. Autonomic tests showed that these hydrogels have the potential to energy saving cooling systems as the internal temperatures are lowered by up to 10 °C when exposed to direct sunlight. This innovative approach to hydrogel manufacturing is cost-effective, eco-friendly, and advisable for widespread use in building and automotive industries. Moreover, possible improvements and future lines of inquiry are suggested to better adapt the technology for broad sedimentary applications. The study adds to the rich pool of scholarship on energy efficient materials and enhances sustainable cities