Information Difference of Transfer Entropies between Head Motion and Eye Movement Indicates a Proxy of Driving.

Visual scanning is achieved via head motion and gaze movement for visual information acquisition and cognitive processing, which plays a critical role in undertaking common sensorimotor tasks such as driving. The coordination of the head and eyes is an important human behavior to make a key contribution to goal-directed visual scanning and sensorimotor driving. In this paper, we basically investigate the two most common patterns in eye-head coordination: "head motion earlier than eye movement" and "eye movement earlier than head motion". We utilize bidirectional transfer entropies between head motion and eye movements to determine the existence of these two eye-head coordination patterns. Furthermore, we propose a unidirectional information difference to assess which pattern predominates in head-eye coordination. Additionally, we have discovered a significant correlation between the normalized unidirectional information difference and driving performance. This result not only indicates the influence of eye-head coordination on driving behavior from a computational perspective but also validates the practical significance of our approach utilizing transfer entropy for quantifying eye-head coordination.

A Layer-Wise Surface Deformation Defect Detection by Convolutional Neural Networks in Laser Powder-Bed Fusion Images.

Surface deformation is a multi-factor, laser powder-bed fusion (LPBF) defect that cannot be avoided entirely using current monitoring systems. Distortion and warping, if left unchecked, can compromise the mechanical and physical properties resulting in a build with an undesired geometry. Increasing dwell time, pre-heating the substrate, and selecting appropriate values for the printing parameters are common ways to combat surface deformation. However, the absence of real-time detection and correction of surface deformation is a crucial LPBF problem. In this work, we propose a novel approach to identifying surface deformation problems from powder-bed images in real time by employing a convolutional neural network-based solution. Identifying surface deformation from powder-bed images is a significant step toward real-time monitoring of LPBF. Thirteen bars, with overhangs, were printed to simulate surface deformation defects naturally. The carefully chosen geometric design overcomes problems relating to unlabelled data by providing both normal and defective examples for the model to train. To improve the quality and robustness of the model, we employed several deep learning techniques such as data augmentation and various model evaluation criteria. Our model is 99% accurate in identifying the surface distortion from powder-bed images.

Analysis of security and privacy challenges for DNA-genomics applications and databases.

DNA technology is rapidly moving towards digitization. Scientists use software tools and applications for sequencing, synthesizing, analyzing and sharing of DNA and genomic data, operate lab equipment and store genetic information in shared datastores. Using cutting-edge computing methods and techniques, researchers have decoded human genome, created organisms with new capabilities, automated drug development and transformed food safety. Such software applications are typically developed to progress scientific understanding and as such cyber security is never a concern for these applications. However, with the increasing commercialisation of DNA technologies, coupled with the sensitivity of DNA data, there is a need to adopt a security-by-design approach. In this paper we investigate bio-cyber security threats to genomic-DNA data and software applications making use of such data to advance scientific research. Specifically, we adopt an empirical approach to analyse and identify vulnerabilities within genomic-DNA databases and bioinformatics software applications that can lead to cyber-attacks affecting the confidentiality, integrity and availability of such sensitive data. We present a detailed analysis of these threats and highlight potential protection mechanisms to help researchers pursue these research directions.

Global resource potential of seasonal pumped hydropower storage for energy and water storage.

Seasonal mismatches between electricity supply and demand is increasing due to expanded use of wind, solar and hydropower resources, which in turn raises the interest on low-cost seasonal energy storage options. Seasonal pumped hydropower storage (SPHS) can provide long-term energy storage at a relatively low-cost and co-benefits in the form of freshwater storage capacity. We present the first estimate of the global assessment of SPHS potential, using a novel plant-siting methodology based on high-resolution topographical and hydrological data. Here we show that SPHS costs vary from 0.007 to 0.2 US$ m-1 of water stored, 1.8 to 50 US$ MWh-1 of energy stored and 370 to 600 US$ kW-1 of installed power generation. This potential is unevenly distributed with mountainous regions demonstrating significantly more potential. The estimated world energy storage capacity below a cost of 50 US$ MWh-1 is 17.3 PWh, approximately 79% of the world electricity consumption in 2017.

A high-resolution gridded dataset to assess electrification in sub-Saharan Africa.

Spatially explicit data on electricity access and use are essential for effective policy-making and infrastructure planning in low-income, data-scarce regions. We present and validate a 1-km resolution electricity access dataset covering sub-Saharan Africa built on gridded nighttime light, population, and land cover data. Using light radiance probability distributions, we define electricity consumption tiers for urban and rural areas and estimate the by-tier split of consumers living in electrified areas. The approach provides new insight into the spatial distribution and temporal evolution of electricity access, and a measure of its quality beyond binary access. We find our estimates to be broadly consistent with recently published province- and national-level statistics. Moreover, we demonstrate consistency between the estimated electricity access quality indicators and survey-based consumption levels defined in accordance with the World Bank Multi-Tier Framework. The dataset is readily reproduced and updated using an open-access scientific computing framework. The data and approach can be applied for improving the assessment of least-cost electrification options, and examining links between electricity access and other sustainable development objectives.

Correlation and convolution filtering and image processing for pitch evaluation of 2D micro- and nano-scale gratings and lattices.

We have mathematically explicated and experimentally demonstrated how a correlation and convolution filter can dramatically suppress the noise that coexists with the scanned topographic signals of two-dimensional (2D) gratings and lattices with 2D perspectives. To realize pitch evaluation, the true peaks' coordinates have been precisely acquired after detecting the local maxima from the filtered signal, followed by image processing. The combination of 2D filtering, local-maxima detecting, and image processing make up the pitch detection (PD) method. It is elucidated that the pitch average, uniformity, rotation angle, and orthogonal angle can be calculated using the PD method. This has been applied to the pitch evaluation of several 2D gratings and lattices, and the results are compared with the results of using the center-of-gravity (CG) and Fourier-transform-based (FT) method. The differences of pitch averages which are produced using the PD, CG, and FT methods are within 1.5 pixels. Moreover, the PD method has also been applied to detect the dense peaks of Si (111) 7×7 surface and the highly oriented pyrolytic graphite (HOPG) basal plane.

Impacts of Groundwater Constraints on Saudi Arabia's Low-Carbon Electricity Supply Strategy.

Balancing groundwater depletion, socioeconomic development and food security in Saudi Arabia will require policy that promotes expansion of unconventional freshwater supply options, such as wastewater recycling and desalination. As these processes consume more electricity than conventional freshwater supply technologies, Saudi Arabia's electricity system is vulnerable to groundwater conservation policy. This paper examines strategies for adapting to long-term groundwater constraints in Saudi Arabia's freshwater and electricity supply sectors with an integrated modeling framework. The approach combines electricity and freshwater supply planning models across provinces to provide an improved representation of coupled infrastructure systems. The tool is applied to study the interaction between policy aimed at a complete phase-out of nonrenewable groundwater extraction and concurrent policy aimed at achieving deep reductions in electricity sector carbon emissions. We find that transitioning away from nonrenewable groundwater use by the year 2050 could increase electricity demand by more than 40% relative to 2010 conditions, and require investments similar to strategies aimed at transitioning away from fossil fuels in the electricity sector. Higher electricity demands under groundwater constraints reduce flexibility of supply side options in the electricity sector to limit carbon emissions, making it more expensive to fulfill climate sustainability objectives. The results of this analysis underscore the importance of integrated long-term planning approaches for Saudi Arabia's electricity and freshwater supply systems.