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Carbon capture and catalytic conversion to methane is promising for carbon-neutral energy production. Precious metals catalysts are highly efficient; yet they have several significant drawbacks including high cost, scarcity, environmental impact from the mining and intense processing requirements. Previous experimental studies and the current analytical work show that refractory grade chromitites (chromium rich rocks with Al2O3 > 20% and Cr2O3 + Al2O3 > 60%) with certain noble metal concentrations (i.e., Ir: 17-45 ppb, Ru: 73-178 ppb) catalyse Sabatier reactions and produce abiotic methane; a process which has not been investigated at the industrial scale. Thus, a natural source (chromitites) hosting noble metals might be used instead of concentrating noble metals for catalysis. Stochastic machine-learning algorithms show that among the various phases, the noble metal alloys are natural methanation catalysts. Such alloys form when pre-existing platinum group minerals (PGM) are chemically destructed. Chemical destruction of existing PGM results to mass loss forming locally a nano-porous surface. The chromium-rich spinel phases, hosting the PGM inclusions, are subsequently a second-tier support. The current work is the first multi-disciplinary research showing that noble metal alloys within chromium-rich rocks are double-supported, Sabatier catalysts. Thus, such sources could be a promising material in the search of low-cost, sustainable materials for green energy production.

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The basic identification and classification of sedimentary rocks into sandstone and mudstone are important in the study of sedimentology and they are executed by a sedimentologist. However, such manual activity involves countless hours of observation and data collection prior to any interpretation. When such activity is conducted in the field as part of an outcrop study, the sedimentologist is likely to be exposed to challenging conditions such as the weather and their accessibility to the outcrops. This study uses high-resolution photographs which are acquired from a sedimentological study to test an alternative basic multi-rock identification through machine learning. While existing studies have effectively applied deep learning techniques to classify the rock types in field rock images, their approaches only handle a single rock-type classification per image. One study applied deep learning techniques to classify multi-rock types in each image; however, the test was performed on artificially overlaid images of different rock types in a test sample and not of naturally occurring rock surfaces of multiple rock types. To the best of our knowledge, no study has applied semantic segmentation to solve the multi-rock classification problem using digital photographs of multiple rock types. This paper presents the application of two state-of-the-art segmentation models, namely U-Net and LinkNet, to identify multiple rock types in digital photographs by segmenting the sandstone, mudstone, and background classes in a self-collected dataset of 102 images from a field in Brunei Darussalam. Four pre-trained networks, including Resnet34, Inceptionv3, VGG16, and Efficientnetb7 were used as a backbone for both models, and the performances of the individual models and their ensembles were compared. We also investigated the impact of image enhancement and different color representations on the performances of these segmentation models. The experiment results of this study show that among the individual models, LinkNet with Efficientnetb7 as a backbone had the best performance with a mean over intersection (MIoU) value of 0.8135 for all of the classes. While the ensemble of U-Net models (with all four backbones) performed slightly better than the LinkNet with Efficientnetb7 did with an MIoU of 0.8201. When different color representations and image enhancements were explored, the best performance (MIoU = 0.8178) was noticed for the L*a*b* color representation with Efficientnetb7 using U-Net segmentation. For the individual classes of interest (sandstone and mudstone), U-Net with Efficientnetb7 was found to be the best model for the segmentation. Thus, this study presents the potential of semantic segmentation in automating the reservoir characterization process whereby we can extract the patches of interest from the rocks for much deeper study and modeling to be conducted.

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As efforts to achieve Net Zero are intensifying, there is a strong need to identify the technological positioning of green process innovations that can support the green energy transition. A veritable contender to support these efforts is the hydrothermal biomass processing technology. This process innovation comprises diverse techniques that can convert biomass substrates into valuable low-carbon fuels. Coordination across all available conversion approaches is encouraged to propel the application of those that consider the environmental and sustainability impacts. We assessed the innovation intensity for different techniques under this green process innovation through applying natural language processing and deployment of principal component analysis on patent data. We positioned our techniques within four distinctive groups (intense, dormant, emerging, and exploratory). In this way, we tracked which hydrothermal technique currently dominates international applications and which ones are gaining traction in the future.

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This paper presents an automatic ECG signal classification system that applied the Deep Learning (DL) model to classify four types of ECG signals. In the first part of our work, we present the model development. Four different classes of ECG signals from the PhysioNet open-source database were selected and used. This preliminary study used a Deep Learning (DL) technique namely Convolutional Neural Network (CNN) to classify and predict the ECG signals from four different classes: normal, sudden death, arrhythmia, and supraventricular arrhythmia. The classification and prediction process includes pulse extraction, image reshaping, training dataset, and testing process. In general, the training accuracy achieved up to 95% after 100 epochs. However, the prediction of each ECG single type shows a differentiation. Among the four classes, the results show that the predictions for sudden death ECG waveforms are the highest, i.e., 80 out of 80 samples are correct (100% accuracy). In contrast, the lowest is the prediction for normal sinus ECG waveforms, i.e., 74 out of 80 samples are correct (92.5% accuracy). This is due to the image features of normal sinus ECG waveforms being almost similar to the image features of supraventricular arrhythmia ECG waveforms. However, the model has been tuned to achieve an optimal prediction. In the second part, we presented the hardware implementation with the predictive model embedded in an NVIDIA Jetson Nanoprocessor for the online and real-time classification of ECG waveforms.

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This article provides a cross-sectional weighted measurement of noncommunicable diseases (NCDs) and risk factors prevalence among Brunei adult population using WHO STEPS methodology. A 2-staged randomized sampling was conducted during August 2015 to April 2016. Three-step surveillance included (1) interview using standardized questionnaire, (2) blood pressure and anthropometric measurements, and (3) biochemistry tests. Data weighting was applied. A total of 3808 adults aged 18 to 69 years participated in step 1; 2082 completed steps 2 and 3 measurements. Adult smoking prevalence was 19.9%, obesity 28.2%, hypertension 28.0%, diabetes 9.7%, prediabetes 2.1%, and 51.3% had fasting cholesterol level ≥5 mmol/L. Inadequate consumption of fruits and vegetables prevalence was high at 91.7%. Among those aged 40 to 69 years, 8.9% had a 10-year cardiovascular disease (CVD) risk ≥30%, or with existing CVD. Population strategies and targeted group interventions are required to control the NCD risk factors and morbidities.

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