This project investigates the prehistoric coastal site of Kalba on the Gulf of Oman in the context of exchange networks between maritime waterways and land-based caravan routes on the south-eastern Arabian Peninsula. In addition to favourable environmental conditions, raw-material procurement strategies were important for the economy of this multi-crafting community.

A multi-agent deep reinforcement learning (DRL)-based model is presented in this study to reconstruct flow fields from noisy data. A combination of reinforcement learning with pixel-wise rewards, physical constraints represented by the momentum equation and the pressure Poisson equation, and the known boundary conditions is used to build a physics-constrained deep reinforcement learning (PCDRL) model that can be trained without the target training data. In the PCDRL model, each agent corresponds to a point in the flow field and learns an optimal strategy for choosing pre-defined actions. The proposed model is efficient considering the visualisation of the action map and the interpretation of the model operation. The performance of the model is tested by using direct numerical simulation-based synthetic noisy data and experimental data obtained by particle image velocimetry. Qualitative and quantitative results show that the model can reconstruct the flow fields and reproduce the statistics and the spectral content with commendable accuracy. Furthermore, the dominant coherent structures of the flow fields can be recovered by the flow fields obtained from the model when they are analysed using proper orthogonal decomposition and dynamic mode decomposition. This study demonstrates that the combination of DRL-based models and the known physics of the flow fields can potentially help solve complex flow reconstruction problems, which can result in a remarkable reduction in the experimental and computational costs.

Background: Alzheimer’s disease (AD) is an emerging public health crisis and biomarkers are playing a large role in AD research. Magnetic Resonance Imaging (MRI) holds advantages over existing biomarkers for AD. This project aims to measure subfield thickness throughout the hippocampal long axis using HippUnfold, a novel open-source automated hippocampal segmentation software. Methods: High resolution (0.39×0.39×2mm) Hippocampal MR Images [control, n= 16, mild cognitive impairment (MCI, n =16), and AD, (n = 16)] acquired by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were analyzed with an automated segmentation software (HippUnfold) to compute thickness measurements. ADNI data such as Positron Emission Tomography (PET) biomarkers, Cerebrospinal Fluid biomarkers, and cognitive scores such as Mini-Mental State Exam (MMSE), Montreal Cognitive Assessment (MoCA), Alzheimer’s Disease Assessment Scale (ADAS13), and Rey Auditory Verbal Learning Test (RAVLT), were correlated to thickness along the hippocampal long axis using linear regression models. Results: We found significant cluster correlations (p < 0.05) throughout the long axis between hippocampal subfield thickness to MoCA scores, ADAS13 scores, PET phosphorylated tau levels, and PET beta-amyloid levels. Conclusions: Subfield atrophy throughout the hippocampal long axis is associated with disease severity (as measured with existing biomarkers and cognitive testing) in patients with MCI and AD.

This study proposes a newly developed deep-learning-based method to generate turbulent inflow conditions for spatially developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced super-resolution generative adversarial network is utilised to predict velocity fields of a spatially developing TBL at various planes normal to the streamwise direction. Datasets of direct numerical simulation (DNS) of flat plate flow spanning a momentum thickness-based Reynolds number, $Re_\theta = 661.5\unicode{x2013}1502.0$, are used to train and test the model. The model shows a remarkable ability to predict the instantaneous velocity fields with detailed fluctuations and reproduce the turbulence statistics as well as spatial and temporal spectra with commendable accuracy as compared with the DNS results. The proposed model also exhibits a reasonable accuracy for predicting velocity fields at Reynolds numbers that are not used in the training process. With the aid of transfer learning, the computational cost of the proposed model is considered to be effectively low. Furthermore, applying the generated turbulent inflow conditions to an inflow–outflow simulation reveals a negligible development distance for the TBL to reach the target statistics. The results demonstrate for the first time that transformer-based models can be efficient in predicting the dynamics of turbulent flows. They also show that combining these models with generative adversarial networks-based models can be useful in tackling various turbulence-related problems, including the development of efficient synthetic-turbulent inflow generators.

Quaternary environments on the Arabian Peninsula shifted between pronounced arid conditions and phases of increased rainfall, which had a profound impact on Earth surface processes. However, while aeolian sediment dynamics are reasonably well understood, there is a lack of knowledge with regard to variability in the fluvial systems. Presented here are the findings from several locations within wadi drainage systems to the west of the Hajar Mountains (United Arab Emirates). The performance of optically stimulated luminescence dating using a customized standardized growth curve approach is investigated, showing that this approach allows reliable determination of ages by reducing the machine time required. Three main periods of fluvial activity occurred at 160–135, 43–34, and ca. 20 ka. Additional ages fall into the latest Pleistocene and Late Holocene. None of the ages coincides with major wet periods in SE Arabia that have been identified in stalagmites and by the deposition of lake sediments. It is shown that fluvial activity was partly contemporaneous (within the given time resolution) with phases of aeolian deposition and was almost continuous, but likely sporadic, during the Middle to Late Pleistocene. This highlights the need for regionally defined paleoenvironmental records to fully understand the response of dryland systems to long-term climatic change.

In this paper, we propose an efficient method for generating turbulent inflow conditions based on deep neural networks. We utilise the combination of a multiscale convolutional auto-encoder with a subpixel convolution layer (${\rm MSC}_{\rm {SP}}$-AE) and a long short-term memory (LSTM) model. Physical constraints represented by the flow gradient, Reynolds stress tensor and spectral content of the flow are embedded in the loss function of the ${\rm MSC}_{\rm {SP}}$-AE to enable the model to generate realistic turbulent inflow conditions with accurate statistics and spectra, as compared with the ground truth data. Direct numerical simulation (DNS) data of turbulent channel flow at two friction Reynolds numbers $Re_{\tau } = 180$ and 550 are used to assess the performance of the model obtained from the combination of the ${\rm MSC}_{\rm {SP}}$-AE and the LSTM model. The model exhibits a commendable ability to predict instantaneous flow fields with detailed fluctuations and produces turbulence statistics and spectral content similar to those obtained from the DNS. The effects of changing various salient components in the model are thoroughly investigated. Furthermore, the impact of performing transfer learning (TL) using different amounts of training data on the training process and the model performance is examined by using the weights of the model trained on data of the flow at $Re_{\tau } = 180$ to initialise the weights for training the model with data of the flow at $Re_{\tau } = 550$. The results show that by using only 25% of the full training data, the time that is required for successful training can be reduced by a factor of approximately 80% without affecting the performance of the model for the spanwise velocity, wall-normal velocity and pressure, and with an improvement of the model performance for the streamwise velocity. The results also indicate that using physics-guided deep-learning-based models can be efficient in terms of predicting the dynamics of turbulent flows with relatively low computational cost.

There is ongoing debate about whether number is perceived directly. Clarke and Beck suggest that what plagues this debate is a lack of shared understanding about what it means to perceive number in the first place. I agree. I argue that the perception of number is held to a different standard than, say, the perception of objecthood; considering this, I explore what it might mean for the number system to represent rational numbers.

Decreasing heat increment through lowering crude protein (CP) level via supplemental amino acids (AA) have long been suggested as a nutritional practice to alleviate negative heat stress effects on broiler performance. However, there is a considerable body of inconclusive reports on optimisation of such practices, whereby bird performance remains unchanged. The exact mechanism underlying the impaired growth performance in birds fed with reduced-CP diets is not clear yet. Furthermore, adding the environmental temperature factor to the situation may complicate the solution. To date, there is no agreement on the extent of reducing CP level via AA supplementation so that growth performance remains unaffected. Evidence suggests that dietary CP could be reduced safely by 2.3% via essential AA supplementation during later ages, when birds are exposed to an average daily ambient temperature of ≤27.3°C. When Gly was added (a non-essential AA source), the margin of CP reduction could be increased to 5.1% without compromising the growth of broilers subjected to cyclic heat stress. Nonetheless, feeding broilers with a similar Gly-fortified, reduced protein diet failed to support optimal performance under hot and humid tropical climates in 1-21-d-old broilers and had a major impact on growth in broilers reared at 34°C. Regardless of supplemental AA composition or the level of CP reduction, the performance of broilers was negatively affected when birds were subjected to chronic heat stress conditions (≥30°C). These discrepancies can be attributed to a wide range of confounding factors, such as the extent of lowering CP level, types of AA used, age and environmental conditions. Accordingly, the addition of Gly may represent a good approach for reducing dietary CP levels for broilers raised under elevated ambient temperature. Reducing dietary CP is recommended when birds are exposed to moderate but not chronic heat stress conditions.

This article explores the challenges of redefining property rights for land, with application to monarchical Iraq from 1944 to 1958. We apply two processes in the analysis of economic institutions to study history: a puzzle-solving method at the micro level, with broader interest in the role of institutions in development and economic growth at the macro level. Thus, we explore the interaction between demanders and suppliers of land reform in the political market, focusing on the parliamentary influence of big landholders as an interest group. We conclude that despite increasing demand for land reform, politicians were able to supply quantitative change only, consisting of the allocation of newly arable land to landless cultivators, rather than the redistribution of existing assets or qualitative change. We analyse these findings in relation to our concern for the role of institutions in development. Our discussion uncovers key insights into Iraq's political economy and its institutions.

Room temperature Mössbauer and electron-probe measurements of Fe3+/ΣFe in chromite from the mantle section of the Oman ophiolite define two groups of samples: a low Fe3+/ΣFe group (with Fe3+/ΣFe = 0.21–0.36) have cr# = Cr/(Cr + Al) in the range 0.49–0.75, whereas a smaller more geographically localized high Fe3+/ΣFe group (with Fe3+/ΣFe = 0.71–0.78) have a more restricted range of cr# ratios of 0.72–0.75. The low Fe3+/ΣFe chromitites have very variable Fe3+/ΣFe ratios. They are thought to have crystallized from melts that have interacted with depleted mantle and thereby acquired their variable Fe3+/ΣFe ratio. The high Fe3+/ΣFe chromitites are restricted to one small area of the mantle and their high oxidation state is thought to be post magmatic. They are either the product of later heating, related to melt flux or interaction with a later oxidising melt. A difference in oxygen fugacity between the MORB-depleted harzburgite host, which is at the quartz–fayalite–magnetite (QFM) buffer and the later chromite-bearing melts (QFM + 2) implies that there is a real difference in the oxidation state of the MORB and arc-magma sources.

We compared the etiologic organisms of bloodstream infections (BSIs) in cancer patients with central venous catheters (CVCs) between 2 cohorts separated by more than a decade.

Gram-negative organisms have become the predominant etiologic organisms of BSIs (52%); they now contribute to 41% of catheter-related BSIs (CRBSIs).

Infect Control Hosp Epidemiol 2018;39:727–729

We present a real time 3D SLAM system for texture-less scenes using only depth information provided by a low cost RGB-D sensor. The proposed method is based on a novel informative sampling scheme that extracts points carrying the most useful 3D information for registration. The aim of the proposed sampling technique is to informatively sample a point cloud into a subset of points based on their 3D information. The flatness of a point is measured by applying a rank order statistics based robust segmentation method to surface normals in its local vicinity. The extracted keypoints from sequential frames are then matched and a rank order statistics based robust estimator is employed to refine the matches and estimate a rigid-body transformation between the frames. Experimental evaluations show that the proposed keypoint extraction method is highly repeatable and outperforms the state of the art methods in terms of accuracy and repeatability. We show that the performance of the registration algorithm is also comparable to other well-known methods in texture-less environments.