The Link between Hemodynamics and Wall Structure in Cerebral Aneurysms

Intracranial aneurysms (IAs) are pathological enlargements of the walls of cerebral arteries. Rupture of aneurysms causes 80% of subarachnoid hemorrhages. It is generally accepted that the abnormal hemodynamics within the aneurysm sac can lead to a breakdown in the normal process of collagen renewal and remodeling leaving the aneurysm vulnerable to rupture. However, the link between hemodynamics and wall integrity, as well as the underlying mechanisms governing the aneurysm pathophysiology remain poorly understood. To investigate the variability of wall structure and mechanical properties within the human unruptured aneurysms, we performed uniaxial mechanical testing on samples resected from aneurysm walls with simultaneous multiphoton microscopy imaging of collagen structure. Significant variations in collagen architecture and mechanical response were found in unruptured aneurysms. Factor of Safety (FoS) was used to quantitatively assess the structural integrity of aneurysms, and subcategorize the unruptured population. In order to further improve the assessment of the structural integrity of the unruptured population, intramural stresses were obtained from FEA with patient-specific models and used for FoS estimation. In this case study, histological investigation of ECM suggests that aneurysms with high FoS are capable of bearing biaxial loading with collagen fibers in two main directions inside the wall and dispersed on the abluminal side. The robust IAs display a rich cell content that is distributed into distinct layers. The collagen architecture in these layers displays similar functional roles to the medial and adventitial layers of the control basilar artery. To study the connection between aneurysmal hemodynamic conditions and the mechanical properties of the aneurysms wall, we constructed computational fluid dynamics models from 3DRA images. Statistically significant correlations between hemodynamic quantities and failure characteristics and high strain stiffness of the wall were found. In order to assess the correlation between local hemodynamics and local wall structure, we developed a methodology for mapping the resected aneurysm sample onto a reconstruction of the lumen. Local collagen structure was accessed at multiple areas, and was correlated with the local hemodynamics. In the case study used to illustrate this methodology, high wall shear stress was found to be associated with sparse, inhomogeneous fiber architectures

A Data Scientific Approach Towards Predictive Maintenance Application in Manufacturing Industry

Most industries have recently started to harness the power of data to assess their performance and improve their production systems for future competitiveness and sustainability. Therefore, utilization of data for obtaining insights through data-driven approaches is invading every domain of industrial applications. Predictive maintenance (PdM) is one of the highest impacted industrial use cases in data-driven applications due to its ability to predict machine failures by implementing machine learning algorithms. This study aims to propose a systematic data scientific approach to provide valuable insights by analysing industrial alarm and event log data, which might further be used for investigation in root cause understanding and planning of necessary maintenance activities. To do that, a Cross-Industry Standard Process for Data Mining (CRISP-DM) is followed as a reference model in this study. The results are presented by first understanding the relationship between alarms and product types being processed in the selected machines by using exploratory data analysis (EDA). Along with this, the behavior of problematic alarms is identified. Afterward, a predictive analysis formulated as a multi-class classification problem is performed using various Machine Learning (ML) models to predict the category of alarm and generate rules to be used for further investigation in maintenance planning. The performance of the developed models is evaluated based on the different metrics and the decision tree model is selected with the higher accuracy score among them. As a theoretical contribution, this study presents an implementation of predictive modeling in a structured way, which uses a systematic data scientific approach based on industrial alarm and event log data. On the other hand, as a practical contribution, this study provides a set of decision rules that can act as decision support for further exploration of possible in-depth root causes through the other contextual data, and hence it gives an initial foundation towards PdM application in the case company

A CsI hodoscope on CSHINE for Bremsstrahlung {\gamma}-rays in Heavy Ion Reactions

Bremsstrahlung $\gamma$ production in heavy ion reactions at Fermi energies carries important physical information including the nuclear symmetry energy at supra-saturation densities. In order to detect the high energy Bremsstrahlung $\gamma$ rays, a hodoscope consisting of 15 CsI(Tl) crystal read out by photo multiplier tubes has been built, tested and operated in experiment. The resolution, efficiency and linear response of the units to $\gamma$ rays have been studied using radioactive source and $({\rm p},\gamma)$ reactions. The inherent energy resolution of $1.6\%+2\%/E_{\gamma}^{1/2}$ is obtained. Reconstruction method has been established through Geant 4 simulations, reproducing the experimental results where comparison can be made. Using the reconstruction method developed, the whole efficiency of the hodoscope is about $2.6\times 10^{-4}$ against the $4\pi$ emissions at the target position, exhibiting insignificant dependence on the energy of incident $\gamma$ rays above 20 MeV. The hodoscope is operated in the experiment of $^{86}$Kr + $^{124}$Sn at 25 MeV/u, and a full $\gamma$ energy spectrum up to 80 MeV has been obtained.Comment: 9 pages, 19 figure

The benefits of mobile device management in enterprise environment

In the past decade, the shift to mobility was witnessed as the main focus of information technology (IT) development. Mobile devices, represented by smartphones and tablets, have been rapidly adopted in enterprise environments. Bring-Your-Own-Device (BYOD) and mobile applications are also gaining their prevalence. However, such mobility trend creates challenges for different stakeholders in enterprise environments. Security risks and increasing costs are major concerns for enterprises; for IT management, the diversity and complexity of mobile devices escalate both workload and difficulty; the lack of efficient and centralized enterprise resources access approach limits employees to better utilize mobile devices for work. Therefore, mobile device management (MDM), a set of technologies enforcing enterprise security policies and enabling enterprises to deploy and support corporate resources to mobile devices, is designed to overcome the challenges. Nevertheless, the benefits of MDM in enterprise environment are not fully recognized and there is little research about the measurement of the value of MDM. It has hindered the further adoption of MOM. In this study, by interviewing four companies from different industries: Nokia, OP-Pohjola, Orion, and IBM, the benefits of MDM are analysed from different perspectives, and an economic model is proposed for value quantification. The study result reveals that MDM can theoretically benefit enterprises in multiple aspects, and it is a primary and necessary solution for modem enterprise mobility. Moreover, some open issues in the practical implementation of MDM and its future development are also discussed

F2F-NN: A Field-to-Field Wind Speed Retrieval Method of Microwave Radiometer Data Based on Deep Learning

In this paper, we present a method for retrieving sea surface wind speed (SSWS) from Fengyun-3D (FY-3D) microwave radiation imager (MWRI) data. In contrast to the conventional point-to-point (P2P) retrieval methods, we propose a field-to-field (F2F) SSWS retrieval method based on the basic framework of a Convolutional Neural Network (CNN). Considering the spatial continuity and consistency characteristics of wind fields within a certain range, we construct the model based on the basic framework of CNN, which is suitable for retrieving various wind speed intervals, and then synchronously obtaining the smooth and continuous wind field. The retrieval results show that: (1) Comparing the retrieval results with the label data, the root-mean-square error (RMSE) of wind speed is about 0.26 m/s, the F2F-NN model is highly efficient in training and has a strong fitting ability to label data. Comparing the retrieval results with the buoys (NDBC and TAO) data, the RMSE of F2F-NN wind speed is less than 0.91 m/s, the retrieval accuracy is better than the wind field products involved in the comparison. (2) In the hurricane (Sam) area, the F2F-NN model greatly improves the accuracy of wind speed in the FY-3D wind field. Comparing five wind field products with the Stepped-Frequency Microwave Radiometer (SFMR) data, the overall accuracy of the F2F-NN wind data is the highest. Comparing the five wind field products with the International Best Track Archive for Climate Stewardship (IBTrACS) data, the F2F-NN wind field is superior to the other products in terms of maximum wind speed and maximum wind speed radius. The structure of the wind field retrieved by F2F-NN is complete and accurate, and the wind speed changes smoothly and continuously

A More Accurate Field-to-Field Method towards the Wind Retrieval of HY-2B Scatterometer

In this paper, we present a method for retrieving sea surface wind field (SSWF) from HaiYang-2B (HY-2B) scatterometer data. In contrast to the conventional algorithm, i.e., using a point-to-point (P2P) method based on geophysical model functions (GMF) to retrieve SSWF by spaceborne scatterometer, we introduce a more accurate field-to-field (F2F) retrieval method based on convolutional neural network (CNN). We fully consider the spatial correlation and continuity between adjacent observation points, and input the observation data of continuous wind field within a certain range into the neural network to construct the neural network model, and then synchronously obtain the wind field within the range. The wind field obtained by our retrieval method maintains its continuity and solves the problem of ambiguity removal in traditional wind direction retrieval methods. Comparing the retrieval results with the buoy data, the results show that the root mean square errors (RMSE) of wind direction and wind speed are less than 0.18 rad (10.31°) and 0.75 m/s, respectively. The retrieval accuracy is better than the L2B product of HY-2B

Increasing Impact of Precipitation on Alpine-Grassland Productivity over Last Two Decades on the Tibetan Plateau

Understanding the importance of temperature and precipitation on plant productivity is beneficial, to reveal the potential impact of climate change on vegetation growth. Although some studies have quantified the response of vegetation productivity to climate change at local, regional, and global scales, changes in climatic constraints on vegetation productivity over time are not well understood. This study combines the normalized difference vegetation index (NDVI) and the net primary production (NPP) modeled by CASA during the plant-growing season, to quantify the interplay of climatic (growing-season temperature and precipitation, GST and GSP) constraints on alpine-grassland productivity on the Tibetan Plateau, as well as the temporal dynamics of these constraints. The results showed that (1) 42.2% and 36.3% of grassland NDVI and NPP on the Tibetan Plateau increased significantly from 2000 to 2019. GSP controlled grassland growth in dryland regions, while humid grasslands were controlled by the GST. (2) The response strength of the NDVI and NPP to precipitation (partial correlation coefficient RNDVI-GSP and RNPP-GSP) increased substantially between 2000 and 2019. Especially, the RNDVI-GSP and RNPP-GSP increased from 0.14 and 0.01 in the first 10year period (2000–2009) to 0.83 and 0.78 in the second 10-year period (2010–2019), respectively. As a result, the controlling factor for alpine-grassland productivity variations shifted from temperature during 2000–2009 to precipitation during 2010–2019. (3) The increase in precipitation constraints was mainly distributed in dryland regions of the plateau. This study highlights that the climatic constraints on alpine-grassland productivity might change under ongoing climate change, which helps the understanding of the ecological responses and helps predict how vegetation productivity changes in the future

Increasing Impact of Precipitation on Alpine-Grassland Productivity over Last Two Decades on the Tibetan Plateau

Understanding the importance of temperature and precipitation on plant productivity is beneficial, to reveal the potential impact of climate change on vegetation growth. Although some studies have quantified the response of vegetation productivity to climate change at local, regional, and global scales, changes in climatic constraints on vegetation productivity over time are not well understood. This study combines the normalized difference vegetation index (NDVI) and the net primary production (NPP) modeled by CASA during the plant-growing season, to quantify the interplay of climatic (growing-season temperature and precipitation, GST and GSP) constraints on alpine-grassland productivity on the Tibetan Plateau, as well as the temporal dynamics of these constraints. The results showed that (1) 42.2% and 36.3% of grassland NDVI and NPP on the Tibetan Plateau increased significantly from 2000 to 2019. GSP controlled grassland growth in dryland regions, while humid grasslands were controlled by the GST. (2) The response strength of the NDVI and NPP to precipitation (partial correlation coefficient RNDVI-GSP and RNPP-GSP) increased substantially between 2000 and 2019. Especially, the RNDVI-GSP and RNPP-GSP increased from 0.14 and 0.01 in the first 10year period (2000–2009) to 0.83 and 0.78 in the second 10-year period (2010–2019), respectively. As a result, the controlling factor for alpine-grassland productivity variations shifted from temperature during 2000–2009 to precipitation during 2010–2019. (3) The increase in precipitation constraints was mainly distributed in dryland regions of the plateau. This study highlights that the climatic constraints on alpine-grassland productivity might change under ongoing climate change, which helps the understanding of the ecological responses and helps predict how vegetation productivity changes in the future

An Adaptive Learning Approach for Tropical Cyclone Intensity Correction

Tropical cyclones (TCs) are dangerous weather events; accurate monitoring and forecasting can provide significant early warning to reduce loss of life and property. However, the study of tropical cyclone intensity remains challenging, both in terms of theory and forecasting. ERA5 reanalysis is a benchmark data set for tropical cyclone studies, yet the maximum wind speed error is very large (68 kts) and is still 19 kts after simple linear correction, even in the better sampled North Atlantic. Here, we develop an adaptive learning approach to correct the intensity in the ERA5 reanalysis, by optimising the inputs to overcome the problems caused by the poor data quality and updating the features to improve the generalisability of the deep learning-based model. Specifically, we use understanding of TC properties to increase the representativeness of the inputs so that the general features can be learned with deep neural networks in the sample space, and then use domain adaptation to update the general features from the known domain with historical storms to the specific features for the unknown domain of new storms. This approach can reduce the error to only 6 kts which is within the uncertainty of the best track data in the international best track archive for climate stewardship (IBTrACS) in the North Atlantic. The method may have wide applicability, such as when extending it to the correction of intensity estimation from satellite imagery and intensity prediction from dynamical models