HEXITEC 2 × 2 tiled hard X-ray spectroscopic imaging detector system

HEXITEC is a spectroscopic imaging X-ray detector technology developed at the STFC Rutherford Appleton Laboratory for X-ray and γ-ray spectroscopic imaging applications. Each module has 80 × 80 pixels on a 250 μm pixel pitch, and has been implemented successfully in a number of applications. This paper presents the HEXITEC 2 × 2 detector system, a tiled array of 4 HEXITEC modules read out simultaneously, which provides an active area of 16 cm2. Systems have been produced using 1 mm thick Cadmium Telluride (CdTe) and 2 mm thick Cadmium Zinc Telluride (CdZnTe) sensor material. In this paper the system and data processing methods are presented, and the performance of the systems are evaluated. The detectors were energy calibrated using an 241Am sealed source. Three types of charge sharing correction were applied to the data-charge sharing addition (CSA), charge sharing discrimination (CSD), and energy curve correction (ECC) which compensates for energy lost in the inter-pixel region. ECC recovers an additional 34 % of counts in the 59.5 keV peak in CdTe compared to the use of CSD; an important improvement for photon-starved applications. Due to the high frame rate of the camera system (6.3 kHz) an additional End of Frame (EOF) correction was also applied to 6.0 % of events to correct for signals that were readout whilst the signal was still forming. After correction, both detector materials were found to have excellent spectroscopic performance with a mean energy resolution (FWHM) of 1.17 keV and 1.16 keV for CdZnTe and CdTe respectively. These results successfully demonstrate the ability to construct tiled arrays of HEXITEC modules to provide larger imaging areas.

Ion-Pair Compounds of Strychnine for Enhancing Skin Permeability: Influencing the Transdermal Processes In Vitro Based on Molecular Simulation

This research aimed to explore how Strychnine (Str) ion-pair compounds affect the in vitro transdermal process. In order to prevent the influence of different functional groups on skin permeation, seven homologous fatty acids were selected to form ion-pair compounds with Str. The in vitro permeation fluxes of the Str ion-pair compounds were 2.2 to 8.4 times that of Str, and Str-C10 had the highest permeation fluxes of 42.79 ± 19.86 µg/cm2/h. The hydrogen bond of the Str ion-pair compounds was also confirmed by Fourier Transform Infrared (FTIR) Spectroscopy, Nuclear Magnetic Resonance (NMR) Spectroscopy and molecular simulation. In the process of molecular simulation, the intercellular lipid and the viable skin were represented by ceramide, cholesterol and free fatty acid of equal molar ratios and water, respectively. It was found by the binding energy curve that the Str ion-pair compounds had better compatibility with the intercellular lipid and water than Str, which indicated that the affinity of Str ion-pair compounds and skin was better than that of Str and skin. Therefore, it was concluded that Str ion-pair compounds can be distributed from the vehicle to the intercellular lipid and viable skin more easily than Str. These findings broadened our knowledge about how Str ion-pair compounds affect the transdermal process.

Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers

The aim of this work is to schedule the charging of electric vehicles (EVs) at a single charging station such that the temporal availability of each EV as well as the maximum available power at the station are considered. The total costs for charging the vehicles should be minimized w.r.t. time-dependent electricity costs. A particular challenge investigated in this work is that the maximum power at which a vehicle can be charged is dependent on the current state of charge (SOC) of the vehicle. Such a consideration is particularly relevant in the case of fast charging. Considering this aspect for a discretized time horizon is not trivial, as the maximum charging power of an EV may also change in between time steps. To deal with this issue, we instead consider the energy by which an EV can be charged within a time step. For this purpose, we show how to derive the maximum charging energy in an exact as well as an approximate way. Moreover, we propose two methods for solving the scheduling problem. The first is a cutting plane method utilizing a convex hull of the, in general, nonconcave SOC–power curves. The second method is based on a piecewise linearization of the SOC–energy curve and is effectively solved by branch-and-cut. The proposed approaches are evaluated on benchmark instances, which are partly based on real-world data. To deal with EVs arriving at different times as well as charging costs changing over time, a model-based predictive control strategy is usually applied in such cases. Hence, we also experimentally evaluate the performance of our approaches for such a strategy. The results show that optimally solving problems with general piecewise linear maximum power functions requires high computation times. However, problems with concave, piecewise linear maximum charging power functions can efficiently be dealt with by means of linear programming. Approximating an EV’s maximum charging power with a concave function may result in practically infeasible solutions, due to vehicles potentially not reaching their specified target SOC. However, our results show that this error is negligible in practice.

Optimizing electronic structure simulations on a trapped-ion quantum computer using problem decomposition

Quantum computers have the potential to advance material design and drug discovery by performing costly electronic structure calculations. A critical aspect of this application requires optimizing the limited resources of the quantum hardware. Here, we experimentally demonstrate an end-to-end pipeline that focuses on minimizing quantum resources while maintaining accuracy. Using density matrix embedding theory as a problem decomposition technique, and an ion-trap quantum computer, we simulate a ring of 10 hydrogen atoms without freezing any electrons. The originally 20-qubit system is decomposed into 10 two-qubit problems, making it amenable to currently available hardware. Combining this decomposition with a qubit coupled cluster circuit ansatz, circuit optimization, and density matrix purification, we accurately reproduce the potential energy curve in agreement with the full configuration interaction energy in the minimal basis set. Our experimental results are an early demonstration of the potential for problem decomposition to accurately simulate large molecules on quantum hardware.

Remote Sensing Imagery Segmentation: A Hybrid Approach

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.

Simulation of the Uniaxial Mechanical Properties and Crack Evolution of Coal Pillar-artificial Dam in Abandoned Mines

The key to the construction of underground reservoirs in abandoned mines is the construction of coal pillar-artificial dams, and the choice of bonding parameters between the coal pillars and artificial dams is the deciding factor that determines the engineering stability. Based on the analysis of the force state of coal pillar-artificial dams, the influence of the interface angle was analyzed. Seven sets of coal pillar-artificial dam specimens were prepared and a PFC3D numerical model was constructed to carry out the uniaxial compression test without lateral pressure. Based on the strength, deformation, and energy evolution characteristics of the coal pillar-artificial dam, the influence of the angle of the coal pillar-artificial dam interface on the performance of the specimen was analyzed. The PFC3D model was used to investigate crack evolution, particle displacement, and spatial distribution. The research results showed that the force state of the coal pillar-artificial dam can be divided into three types: split bearing, shared bearing, and coordinated bearing, corresponding to three different constitutive models. The composite simulation curve showed obvious post-peak viscosity. The compressive strength, peak strain, and average dissipated energy curves of the coal pillar-artificial dam showed a unimodal trend that first increased and then decreased. The total energy and elastic energy of the coal pillar-artificial dam showed an increasing trend during loading. The dissipation energy curve increased obviously in the early stage, then flattened, and finally, decayed. The simulated initiation stress and damage stress of the coal pillar-artificial dam specimens were intermediate to that of the coal pillars and the artificial dams, which first increased and then decreased with the increase in inclination, reaching the peak at 70°. The failures of the single and combined models were both dominated by monoclinic splitting. As the inclination increased, the position of the main cracks gradually shifted downwards and then upwards.

A Darwinian Differential Evolution Algorithm for Multilevel Image Thresholding

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on differential evolution (DE) search is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to falling into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with DE algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of DE algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Kapur entropy and Type 2 fuzzy entropy are employed to evaluate the capability of the introduced algorithm. Nine different metaheuristic algorithms with Darwinian modes are also implemented and compared with our method. Experimental results manifest that the proposed method is a powerful tool for multilevel thresholding and the obtained results outperform the DE algorithm and other methods.

Potential Energy as Design Criterion in Planar Multistable Mechanisms

Toggle mechanisms are used throughout engineering to accomplish various tasks, for example residential electrical switching. The design of toggle mechanisms can be broken into three categories: determination of a topology, geometric parameterization, and optimization. While topological determination and optimization have well established processes for use in design, geometric parameterization which includes defining link lengths and spring stiffness has largely been left to engineering judgement. This paper presents a design methodology using potential energy graphs which informs the engineering decisions made in choosing mechanism parameters, giving designers higher confidence in the design. A kinematic analysis coupled with Lagrange’s equation determines the relationship between the mechanism parameters and the potential energy curve. Plotting the potential energy with respect to the generalized coordinate yields a graph with a slope that is the generalized force or moment. The relationships between parameters and their effects on the mechanism are difficult to observe in the equations of motion, but potential energy plots readily provide information pertinent to the design of toggle mechanisms and decouple their effects. The plots also allow design by position rather than time which makes the design process faster. The design process is applied to three examples: a simple toggle mechanism, a compliant mechanism, and a reconfigurable mechanism to show the nuances of the approach.

Identifying critical failure information of thermal damaged sandstone through acoustic emission signal

The changes in the acoustic emission signals of sandstone after treatment at different high temperatures are examined in this study. The results show that there is a critical point on the cumulative energy curve of the acoustic emission signals (almost between 60 and 90% of the ratio of the loading time and the total loading time), which can be used to identify the failure of sandstone that has been damaged by exposure to a temperature of 900°C. As the temperature increases, the position of the critical point gradually changes, which indicates that high temperatures increase the plasticity of rock, and this gradually reduces the brittleness. The changes in b-value of acoustic emission shows that the transition behavior of rock from brittleness to plasticity is more obvious at temperatures higher than 600°C, and the large-scale micro cracking takes place at that temperature range, which is the main reason for the weakening and brittleness and the strengthening of plasticity of the sandstone.