Fusion Scenarios Research Articles

Proof-of-concept of real-time electromagnetic guidance for gynecologic interstitial catheters in high dose rate brachytherapy

Background and PurposeThe addition of interstitial needles to intracavitary gynecologic (GYN) high dose rate (HDR) brachytherapy has been shown to improve target coverage and organs-at-risk (OAR) sparing. However, no commercial solution allows real-time guidance of interstitial catheter placement. This phantom study aimed to evaluate the feasibility of an electromagnetic (EM) tracking system guidance workflow for GYN HDR brachytherapy treatment in a magnetic resonance imaging (MRI) and real-time transrectal ultrasound (TRUS) fusion scenario. Materials and MethodsA clinical investigational system combining a treatment planning system and the EM tracking technology was used. The 3D T2 weighted magnetic resonance (MR) image set of a patient treated with intracavitary and interstitial HDR brachytherapy was retrospectively chosen. The MR image set was used to delineate the target and the OARs. A preplan was generated to determine needles positions in advance. The implant was reproduced in a water phantom. A 3D TRUS scan was acquired, and a rigid registration between the MR and the TRUS images was performed. ResultsThe accuracy of the EM tracking system was < 1 mm for both the sagittal and the transverse modes of the TRUS probe. Contours that were delineated on the MRI were propagated on the TRUS images after the rigid registration. Needle insertion was successfully guided in real time with the EM tracking system on the TRUS live image using the MRI contours for guidance. ConclusionBased on this proof-of-concept, real-time EM-guidance of interstitial needle for GYN HDR brachytherapy appears to be feasible.

Multimodal Fusion for Enhanced Semantic Segmentation in Brain Tumor Imaging: Integrating Deep Learning and Guided Filtering Via Advanced 3D Semantic Segmentation Architectures

ABSTRACTBrain tumor segmentation is paramount in medical diagnostics. This study presents a multistage segmentation model consisting of two main steps. First, the fusion of magnetic resonance imaging (MRI) modalities creates new and more effective tumor imaging modalities. Second, the semantic segmentation of the original and fused modalities, utilizing various modified architectures of the U‐Net model. In the first step, a residual network with multi‐scale backbone architecture (Res2Net) and guided filter are employed for pixel‐by‐pixel image fusion tasks without requiring any training or learning process. This method captures both detailed and base elements from the multimodal images to produce better and more informative fused images that significantly enhance the segmentation process. Many fusion scenarios were performed and analyzed, revealing that the best fusion results are attained when combining T2‐weighted (T2) with fluid‐attenuated inversion recovery (FLAIR) and T1‐weighted contrast‐enhanced (T1CE) with FLAIR modalities. In the second step, several models, including the U‐Net and its many modifications (adding attention layers, residual connections, and depthwise separable connections), are trained using both the original and fused modalities. Further, a “Model Selection‐based” fusion of these individual models is also considered for more enhancement. In the preprocessing step, the images are resized by cropping them to decrease the pixel count and minimize background interference. Experiments utilizing the brain tumor segmentation (BraTS) 2020 dataset were performed to verify the efficiency and accuracy of the proposed methodology. The “Model Selection‐based” fusion model achieved an average Dice score of 88.4%, an individual score of 91.1% for the whole tumor (WT) class, an average sensitivity score of 86.26%, and a specificity score of 91.7%. These results prove the robustness and high performance of the proposed methodology compared to other state‐of‐the‐art methods.

Viscous Rayleigh–Taylor instability at a dynamic interface in spherical geometry

In their study, Terrones et al. [“Rayleigh–Taylor instability at spherical interfaces between viscous fluids: The fluid/fluid interface,” Phys. Fluids 32, 094105 (2020)] elucidated that investigations into the viscous Rayleigh–Taylor instability (RTI) in spherical geometry at a quiescent interface yield significant physical insights. Yet, the complexity amplifies when addressing a dynamic spherical interface pertinent to engineering and scientific inquiries. The dynamics of RTI, particularly when influenced by the Bell–Plesset effects at such interfaces, offers a rich tapestry for understanding perturbation growth. The evolution of this instability is describable by a coupled set of equations, allowing numerical resolution to trace the radius evolution and instability characteristics of a bubble akin to the implosion scenario of a fusion pellet in inertial confinement fusion scenarios. The investigation encompasses the impact of viscosity, external pressure, discrete mode, and a surface-tension-like force on the interfacial instability. In general, the oscillation of the bubble radius exhibits a decay rate that diminishes with increasing Reynolds number (Re). It is important to note that the growth of the perturbed amplitude is not only solely determined by the mechanical properties of the fluid but also by the dynamics of the interface. The low-order modal (n&amp;lt;20) disturbance is dominant with relatively high Reynolds numbers. There is a specific mode corresponding the maximum in amplitude of perturbation in the linear phase, and the mode decreases as the Re decreases. The application of external pressure noticeably accelerates the bubble's oscillation and impedes its shrinkage, thereby preventing the bubble from collapsing completely. The increase in external pressure also promotes the transition from the first peak to the trough of the disturbance. At higher-order modes, the fluctuation of the disturbance curve tends to be uniform. The ultrahigh-order modes require a strong enough pressure to be excited. In addition, the smaller Weber number (We) helps to accelerate the bubble oscillation and promote the fluctuation of the disturbance amplitude, but has no significant effect on the time of the disturbance peak. These findings contribute to a deeper understanding of interfacial instabilities in the context of spherical bubbles and, especially, for the dynamics of fusion capsules in inertial confinement fusion.

MCG-SLAM: Tightly coupled SLAM for multi-factor constraint graph optimisation

Simultaneous localization and mapping (SLAM) technology is a core component for achieving high-precision vehicle localization and navigation in the field of autonomous driving. Existing light detection and ranging (LiDAR) SLAM methods typically neglect intersensory constraints when estimating poses based on sensor observations, resulting in reduced accuracy in multi-sensor fusion scenarios. To address this, a tightly coupled SLAM method with multi-factor constraint graph optimisation, referred to as MCG-SLAM, is proposed. The initial odometry information obtained from the extended Kalman filter is used to optimise the state nodes in the multi-factor constraint graph. This is achieved by dynamically adjusting the noise covariance of the odometry factors, global positioning system factors, and loop closure factors, optimising the factor nodes within the multi-factor constraint graph, and continuously updating the multi-factor constraint graph to complete pose optimisation. Furthermore, a loop closure detection method based on the intensity and geometric information of the point cloud is introduced to identify loop closures and incorporate loop closure factors. In this paper, the MCG-SLAM method is comprehensively validated against mainstream LiDAR SLAM methods using the MulRan dataset. The results show that MCG-SLAM outperforms other methods by improving LiDAR SLAM accuracy in localization and mapping.

On the upper bound of non-thermal fusion reactivity with fixed total energy

Fusion reactivity represents the integration of fusion cross-sections and the velocity distributions of two reactants. In this study, we investigate the upper bound of fusion reactivity for a non-thermal reactant coexisting with a thermal Maxwellian background reactant while maintaining a constant total energy. Our optimization approach involves fine-tuning the velocity distribution of the non-thermal reactant. We employ both Lagrange multiplier and Monte Carlo methods to analyze Deuterium–Tritium (D–T) and proton-Boron11 (p-B11) fusion scenarios. Our findings demonstrate that, within the relevant range of fusion energy, the maximum fusion reactivity can often surpass that of the conventional Maxwellian–Maxwellian reactants case by a substantial margin, ranging from 50% to 300%. These enhancements are accompanied by distinctive distribution functions for the non-thermal reactant, characterized by one or multiple beams. These results not only establish an upper limit for fusion reactivity but also provide valuable insights into augmenting fusion reactivity through non-thermal fusion, which holds particular significance in the realm of fusion energy research.

OF-DFN: Optical flow prediction network for different perspective image fusion

Currently, non-decision-level image fusion algorithms require extremely high registration precision of the images to be fused. In the face of different perspective image fusion scenarios, traditional feature registration algorithms and learning-based methods have poor robustness and are unsuitable for large image differences because of the Registration-Fusion separation. In addition, the lack of relevant datasets also hinders the development of different perspective image fusion methods. Given the above problems, we collect 5000 sets of different perspective RGB-MONO datasets in multiple scenes for raw data support. We present an end-to-end learned system for fusing two different perspective photographs into a chosen target view. The cascaded feature extraction based on encoder–decoder structure enables learning optical flow at different feature levels systematically. Then the optical flow module enables the image to be continuously registered and optimized during the fusion process, thus avoiding the deviations introduced by non-end-to-end algorithms. Extensive quantitative and qualitative experiments demonstrate that our proposed system can effectively fuse images from different perspectives in our self-built dataset. Compared with non-end-to-end fusion, our method provides superior performance in several fusion evaluation indicators.

Batik Classification using Microstructure Co-occurrence Histogram

Batik Nitik is a distinctive form of batik originating from the culturally rich region of Yogyakarta, Indonesia. What sets it apart from other batik styles is its remarkable motif similarity, a characteristic that often poses a considerable challenge when attempting to distinguish one design from another. To address this challenge, extensive research has been conducted with the primary objective of classifying Batik Nitik, and this research leverages an innovative approach combining the microstructure histogram and gray level co-occurrence matrix (GLCM) techniques, collectively referred to as the Microstructure Co-occurrence Histogram (MCH).The MCH method offers a multi-faceted approach to feature extraction, simultaneously capturing color, texture, and shape attributes, thereby generating a set of local features that faithfully represent the intricate details found in Batik Nitik imagery. In parallel, the GLCM method excels at extracting robust texture features by employing statistical measures to portray the subtle nuances within these batik patterns. Nevertheless, the mere fusion of microstructure and GLCM features doesn't inherently guarantee superior classification performance. This research paper has meticulously examined many feature fusion scenarios between microstructure and GLCM to pinpoint the optimal configuration that would yield the most accurate results. The dataset used consists of 960 Batik Nitik samples, comprising 60 categories. The classifiers employed in this study are K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), and Linear Discriminant Analysis (LDA). Based on the experimental results, the fusion of microstructure and GLCM features with the (LDA) classifier yields the best performance compared to other scenarios and classifiers.

Research on Data Fusion and Analysis Based on Artificial Intelligence Algorithm in Sensor Networks

This paper discusses the data fusion and analysis technology based on artificial intelligence algorithm in sensor networks. Firstly, the basic concept, composition and structure of sensor network are summarized, and its characteristics and challenges are analyzed. Then, the basic principle of data fusion technology and its application in sensor networks are introduced, and the basic concept, classification and application of artificial intelligence algorithm in data fusion are described in detail. On this basis, this paper constructs a data fusion framework based on artificial intelligence, and proposes a new data fusion method combining deep learning and machine learning algorithm. This method can effectively improve the accuracy and reliability of sensor data and reduce data redundancy and noise interference. In addition, the thesis also conducts in-depth research on the application scenarios of data fusion and analysis in sensor networks, including Environmental monitoring, intelligent transportation, medical health and other fields. The effectiveness and practicability of the proposed method are verified by experiments and case studies. Experimental results show that the data fusion technology based on artificial intelligence algorithm can significantly improve the data processing ability and analysis accuracy of sensor networks, and provide strong support for related applications. Finally, this paper summarizes the main conclusions of the study, and points out the shortcomings of current research and future prospects. In the future, the data fusion method based on artificial intelligence will be further optimized and improved, and its application in more fields will be expanded, which will provide more powerful support for the development and application of sensor network technology.

A Robust Mutual-Reinforcing Framework for 3D Multi-Modal Medical Image Fusion Based on Visual-Semantic Consistency

This work proposes a robust 3D medical image fusion framework to establish a mutual-reinforcing mechanism between visual fusion and lesion segmentation, achieving their double improvement. Specifically, we explore the consistency between vision and semantics by sharing feature fusion modules. Through the coupled optimization of the visual fusion loss and the lesion segmentation loss, visual-related and semantic-related features will be pulled into the same domain, effectively promoting accuracy improvement in a mutual-reinforcing manner. Further, we establish the robustness guarantees by constructing a two-level refinement constraint in the process of feature extraction and reconstruction. Benefiting from full consideration for common degradations in medical images, our framework can not only provide clear visual fusion results for doctor's observation, but also enhance the defense ability of lesion segmentation against these negatives. Extensive evaluations of visual fusion and lesion segmentation scenarios demonstrate the advantages of our method in terms of accuracy and robustness. Moreover, our proposed framework is generic, which can be well-compatible with existing lesion segmentation algorithms and improve their performance. The code is publicly available at https://github.com/HaoZhang1018/RMR-Fusion.

Face, content, and construct validity of a novel VR/AR surgical simulator of a minimally invasive spine operation.

Mixed-reality surgical simulators are seen more objective than conventional training. The simulators' utility in training must be established through validation studies. Establish face-, content-, and construct-validity of a novel mixed-reality surgical simulator developed by McGill University, CAE-Healthcare, and DePuy Synthes. This study, approved by a Research Ethics Board, examined a simulated L4-L5 oblique lateral lumbar interbody fusion (OLLIF) scenario. A 5-point Likert scale questionnaire was used. Chi-square test verified validity consensus. Construct validity investigated 276 surgical performance metrics across three groups, using ANOVA, Welch-ANOVA, or Kruskal-Wallis tests. A post-hoc Dunn's test with a Bonferroni correction was used for further analysis on significant metrics. Musculoskeletal Biomechanics Research Lab, McGill University, Montreal, Canada. DePuy Synthes, Johnson & Johnson Family of Companies, research lab. Thirty-four participants were recruited: spine surgeons, fellows, neurosurgical, and orthopedic residents. Only seven surgeons out of the 34 were recruited in a side-by-side cadaver trial, where participants completed an OLLIF surgery first on a cadaver and then immediately on the simulator. Participants were separated a priori into three groups: post-, senior-, and junior-residents. Post-residents rated validity, median > 3, for 13/20 face-validity and 9/25 content-validity statements. Seven face-validity and 12 content-validity statements were rated neutral. Chi-square test indicated agreeability between group responses. Construct validity found eight metrics with significant differences (p < 0.05) between the three groups. Validity was established. Most face-validity statements were positively rated, with few neutrally rated pertaining to the simulation's graphics. Although fewer content-validity statements were validated, most were rated neutral (only four were negatively rated). The findings underscored the importance of using realistic physics-based forces in surgical simulations. Construct validity demonstrated the simulator's capacity to differentiate surgical expertise.

Automatic calibration and association for roadside radar and camera based on fluctuating traffic volume

Accurate perception of the movement and appearance of vehicles depends on the robustness and reliability of the extrinsic parameters calibration in a multi-sensor fusion scenario. However, conventional calibration methods require manual acquisition of prior information, leading to high labor costs and low calibration accuracy. Therefore, we proposed an automatic coarse-to-fine calibration method for roadside radar and camera sensors to lower costs and improve accuracy. Next, an association strategy based on fluctuating traffic volumes was also developed to assist in robust target matching during the coarse-to-fine calibration process. Finally, extrinsic parameters between the radar coordinate system and camera coordinate system were calibrated through double rotations of the position vectors obtained from each system. To verify the proposed method, an experiment was conducted on a pedestrian bridge using an uncalibrated 4D millimeter-wave radar and a traffic monocular camera. The results showed that our proposed method reduced the interquartile range of the roll angle by 41.5% compared to a state-of-the-art neural network method. It also outperformed the manual calibration method by 2.47% in terms of the average reprojection error.

Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds

Three-dimensional semantic segmentation is the foundation for automatically creating enriched Digital Twin Cities (DTCs) and their updates. For this task, prior-level fusion approaches show more promising results than other fusion levels. This article proposes a new approach by developing and benchmarking three prior-level fusion scenarios to enhance the outcomes of point cloud-enriched semantic segmentation. The latter were compared with a baseline approach that used the point cloud only. In each scenario, specific prior knowledge (geometric features, classified images, or classified geometric information) and aerial images were fused into the neural network’s learning pipeline with the point cloud data. The goal was to identify the one that most profoundly enhanced the neural network’s knowledge. Two deep learning techniques, “RandLaNet” and “KPConv”, were adopted, and their parameters were modified for different scenarios. Efficient feature engineering and selection for the fusion step facilitated the learning process and improved the semantic segmentation results. Our contribution provides a good solution for addressing some challenges, particularly for more accurate extraction of semantically rich objects from the urban environment. The experimental results have demonstrated that Scenario 1 has higher precision (88%) on the SensatUrban dataset compared to the baseline approach (71%), the Scenario 2 approach (85%), and the Scenario 3 approach (84%). Furthermore, the qualitative results obtained by the first scenario are close to the ground truth. Therefore, it was identified as the efficient fusion approach for point cloud-enriched semantic segmentation, which we have named the efficient prior-level fusion (Efficient-PLF) approach.

Resonance in radio frequency sheath admittance and enhanced impurity emission near the ion cyclotron frequency

Ion cyclotron resonance heating (ICRH) is of considerable interest among all auxiliary heating techniques, because it transfers power directly to ions, targets the high-density core, and involves the cheapest radio-frequency (RF) components. During ICRH operation, RF sheaths form on the ICRH antenna itself, nearby hardware, and far-field surfaces. These sheaths are associated with large hot-spot formation and impurity emissions. This work presents high-resolution numerical modelling of RF sheaths in nuclear fusion scenarios using hPIC2, a Debye-scale particle-in-cell code. The modelling reveals a new RF sheath phenomenon which occurs when the RF is resonant with a species ion cyclotron frequency or harmonic in an oblique magnetic field. This resonance of the RF sheath causes modifications of the energy-angle distributions of the ions impacting on the walls, with consequent increase in wall impurity emission. Due to the 1/R scaling of the magnetic field in a tokamak, such cases are possible on divertor or vacuum chamber surfaces, depending on the geometry of the tokamak and the RF heating scenario. A simple physics interpretation using a driven damped harmonic oscillator is proposed, where the ion sheath transit time plays the role of damping the RF sheath admittance. Critically, the resonance leads to increased ion heat flux at the wall as well as increased physical sputtering, despite a lack of increase in RF rectified sheath potential.

A Comparison of Different Data Fusion Strategies’ Effects on Maize Leaf Area Index Prediction Using Multisource Data from Unmanned Aerial Vehicles (UAVs)

The leaf area index (LAI) is an important indicator for crop growth monitoring. This study aims to analyze the effects of different data fusion strategies on the performance of LAI prediction models, using multisource images from unmanned aerial vehicles (UAVs). For this purpose, maize field experiments were conducted to obtain plants with different growth status. LAI and corresponding multispectral (MS) and RGB images were collected at different maize growth stages. Based on these data, different model design scenarios, including single-source image scenarios, pixel-level multisource data fusion scenarios, and feature-level multisource data fusion scenarios, were created. Then, stepwise multiple linear regression (SMLR) was used to design LAI prediction models. The performance of models were compared and the results showed that (i) combining spectral and texture features to predict LAI performs better than using only spectral or texture information; (ii) compared with using single-source images, using a multisource data fusion strategy can improve the performance of the model to predict LAI; and (iii) among the different multisource data fusion strategies, the feature-level data fusion strategy performed better than the pixel-level fusion strategy in the LAI prediction models. Thus, a feature-level data fusion strategy is recommended for the creation of maize LAI prediction models using multisource UAV images.

Blockchain based trusted execution environment architecture analysis for multi - source data fusion scenario

Multi-source data fusion techniques are widely applied in dynamic target detection scenarios, such as target situational awareness, radar signal resolution, and feature fusion labeling. Currently, techniques including clustering, neural networks, Bayesian analysis, and machine learning have been applied to improve the success rate of multi-source data fusion in terms of interference data noise reduction. The research on data tampering prevention of multiple data sources is mainly based on the data distributed authentication technology. The research on performing data fusion process in a trusted execution environment is mainly based on cryptography and codec technology. This paper focuses on the technical application architecture that can effectively improve the comprehensive efficiency of multi-source data fusion processing under the constraints of business scenarios. Accordingly, this paper proposes a trusted execution environment architecture based on blockchain technology for multi-source data fusion scenarios. It integrates the strategy of trusted data source data verification in blockchain smart contracts into the typical multi-source data fusion application architecture. After comparison tests in a simulation environment, the trusted execution environment architecture based on blockchain technology has shown considerable improvements in fusion success rate with limited performance cost.

In-Situ GNSS-R and Radiometer Fusion Soil Moisture Retrieval Model Based on LSTM

Global navigation satellite system reflectometry (GNSS-R) is a remote sensing technology of soil moisture measurement using signals of opportunity from GNSS, which has the advantages of low cost, all-weather detection, and multi-platform application. An in situ GNSS-R and radiometer fusion soil moisture retrieval model based on LSTM (long–short term memory) is proposed to improve accuracy and robustness as to the impacts of vegetation cover and soil surface roughness. The Oceanpal GNSS-R data obtained from the experimental campaign at the Valencia Anchor Station are used as the main input data, and the TB (brightness temperature) and TR (soil roughness and vegetation integrated attenuation coefficient) outputs of the ELBARA-II radiometer are used as auxiliary input data, while field measurements with a Delta-T ML2x ThetaProbe soil moisture sensor were used for reference and validation. The results show that the LSTM model can be used to retrieve soil moisture, and that it performs better in the data fusion scenario with GNSS-R and radiometer. The STD of the multi-satellite fusion model is 0.013. Among the single-satellite models, PRN13, 20, and 32 gave the best retrieval results with STD = 0.011, 0.012, and 0.007, respectively.

Recent progress of plasma exhaust concepts and divertor designs for tokamak DEMO reactors

The power exhaust concept and an appropriate divertor design are common critical issues for tokamak DEMO design activities which have been carried out in Europe, Japan, China, Korea and the USA. Conventional divertor concepts and power exhaust studies for recent DEMO designs (Pfusion = 1 – 2 GW, Rp = 7 – 9 m) are reviewed from the viewpoints of the plasma physics issues and the divertor engineering design. Radiative cooling is a common approach for the power fusion scenario. Requirements on the main plasma radiation fraction (fradmain = Pradmain/Pheat) and the plasma performance constrain the divertor design concept. Different challenges contribute to optimizing the future DEMO designs: for example, (i) increasing the main plasma radiation fraction for ITER-level Psep/Rp designs and simplifying the divertor geometry, and (ii) extending ITER divertor geometry with increasing divertor radiation (Praddiv) for larger Psep/Rp ≥ 25MWm−1 designs. Power exhaust simulations with large Psep = 150 – 300 MW have been performed using integrated divertor codes considering an ITER-based divertor geometry with longer leg length (1.6 – 1.7 m), as in a common baseline design. Geometry effects (ITER like geometry or more open one without baffle) on the plasma detachment profile and the required divertor radiation fraction (fraddiv = Praddiv/Psep) were key aspects of these studies. All simulations showed that the divertor plasma detachment were extended widely across the target plate with a reduction in the peak heat load of qtarget ≤ 10 MWm−2 for the large fraddiv = 0.7 – 0.8, while the peak qtarget location and value were noticeably different in the partially detached divertor. Simulation results also demonstrated that radial diffusion coefficients of the heat and particle fluxes were critical parameters for DEMO divertor design, and that effects of plasma drifts on outboard-enhanced asymmetry of the heat flux, suggested the need for longer divertor leg to ensure the existence of a detached divertor operation with qtarget ≤ 10 MWm−2.Integrated design of the water cooled divertor target, cassette body (CB) and cooling pipe routing has been developed for each DEMO concept, based on the ITER-like tungsten monoblock (W-MB) with Cu-alloy cooling pipes. Engineering design adequate under higher neutron irradiation condition was required. Therefore, inlet coolant temperature (Tcool) was increased. In current designs, it still shows a large potential variation between 70 °C and 200 °C. The influence of thermal softening on the Cu-alloy (CuCrZr) pipe was fostered near the strike-point when the high qtarget of ∼10 MWm−2 was studied. Improved technologies for high heat flux components based on the ITER W-MB unit have been developed for EU-DEMO. Different coolant conditions (low- and high-Tcool) were provided for Cu-alloy and reduced activation ferritic martensitic (RAFM) steel heat sink units, respectively. The high-Tcool coolant was also considered for the CB and supporting structures. Appropriate conditions for the high-Tcool coolant, i.e. 180 °C/ 5 MPa (EU-DEMO) and 290 °C/ 15 MPa (JA-DEMO, CFETR and K-DEMO), will be determined in the future optimizations of the divertor and DEMO design.

Optioneering of transportation systems in nuclear facilities

The transportation operations that are required to perform during the lifetime of a nuclear reactor are not feasible by in-situ human interaction. To address this subject, remote handling is the current leading solution in the nuclear industry, as well as other challenging environment-related industries.This paper addresses the current market’s availability of mobile robots that can handle the transportation of activated heavy loads inside nuclear fusion scenarios. An analysis and selection method (ASM), the Optioneering Analytic Hierarchy Process (OAHP) is addressed to perform a comparative evaluation and determine not only the best option among the selected ones, but also to point out the best features that a transportation system should have to accomplish all the required tasks of transportation.The OAHP is filled with two different data sources. The first OAHP is filled by the first author of the paper, while the second one is produced with the results of a survey. The participants of the survey are experts in the remote handling field. In the end, the results of both OAHPs are compared.Finally, a new ASM is developed combining the OAHP and the Sifting method, entitled Hybrid Selection Method. The Hybrid Selection method is developed to surpass some of the disadvantages found in the OAHP and also in the Sifting method, achieving a simple selection process and the ability to produce the comparison tables automatically. The results are used to analyse and compare these selection methods.

VNbCrMo refractory high-entropy alloy for nuclear applications

Refractory high-entropy alloys (RHEAs) with high melting points and low neutron absorption cross-section are sought for generation-IV fission and fusion reactors. A high throughput computational screening tool, Alloy Search and Predict (ASAP), was used to identify promising RHEA candidates from over 1 million four-element equimolar combinations. The selected VNbCrMo RHEA was further studied by CALPHAD to predict phase formation, which was compared to an experimentally produced ingot aged at 1200 °C. The VNbCrMo RHEA was found to constitute a majority bcc phase, with a 6% area fraction of C15-Laves formed at interdendritic regions, in contrast to the predictions of single-phase. The prediction of the yield strength by a model based upon edge dislocation mechanisms indicated 2.1 GPa at room temperature and 850 MPa at 1000 °C for the equimolar single bcc phase. The hardness of the alloy with C15-Laves was 748 HV (yield strength ∼2.4 GPa). Finally, the macroscopic neutron absorption cross-section was modelled for a wide range of energies. Displacements per atom per year and activation calculations, up to 1000 years after 2 years of continuous operation, in typical fusion and fission reactor scenarios were also performed using the inventory code FISPACT-II. This work gives new insight into the phase stability and performance of the VNbCrMo RHEA, which is compared with a similar design concept alloy, to assess the potential of novel RHEAs for use in advanced nuclear applications.

Evaluating Data Fusion Methods to Improve Income Modeling

Abstract Income is an important economic indicator to measure living standards and individual well-being. In Germany, different data sources yield ambiguous evidence for analyzing the income distribution. The Tax Statistics (TS)—an income register recording the total population of more than 40 million taxpayers in Germany for the year 2014—contains the most reliable income information covering the full income distribution. However, it offers only a limited range of socio-demographic variables essential for income analysis. We tackle this challenge by enriching the tax data with information on education and working time from the Microcensus, a representative 1 percent sample of the German population. We examine two types of data fusion methods well suited to the specific data fusion scenario of the TS and the Microcensus: missing-data methods and performant prediction models. We conduct a simulation study and provide an empirical application comparing the proposed data fusion methods, and our results indicate that Multinomial Regression and Random Forest are the most suitable methods for our data fusion scenario.