Establishing exhaustive metasurface robustness against fabrication uncertainties through deep learning

Photonic engineered materials have benefitted in recent years from exciting developments in computational electromagnetics and inverse-design tools. However, a commonly encountered issue is that highly performant and structurally complex functional materials found through inverse-design can lose significant performance upon being fabricated. This work introduces a method using deep learning (DL) to exhaustively analyze how structural issues affect the robustness of metasurface supercells, and we show how systems can be designed to guarantee significantly better performance. Moreover, we show that an exhaustive study of structural error is required to make strong guarantees about the performance of engineered materials. The introduction of DL into the inverse-design process makes this problem tractable, enabling optimization runtimes to be measurable in days rather than months and allowing designers to establish exhaustive metasurface robustness guarantees.

Hippocampal Malrotation: A Genetic Developmental Anomaly Related to Epilepsy?

Hippocampal malrotation (HIMAL) is an increasingly recognized neuroimaging feature but the clinical correlation and significance in epilepsies remain under debate. It is characterized by rounded hippocampal shape, deep collateral, or occipitotemporal sulcus, and medial localization of the hippocampus. In this review, we describe the embryonic development of the hippocampus and HIMAL, the qualitative and quantitative diagnosis issues, and the pathological findings of HIMAL. HIMAL can be bilateral or unilateral and more on the left side. Furthermore, the relevance of HIMAL diagnosis in clinical practice, including its role in epileptogenesis and the impact on the pre-surgical decision are reviewed. Finally, the relationship between HIMAL and hippocampal sclerosis (HS) and the possible role of genetics in the etiology of HIMAL are discussed. The evidence so far suggested that HIMAL does not have a significant role in epileptogenesis or surgical decision. HIMAL could be a genetic developmental imaging feature that represents a more diffuse but subtle structural error during brain development. Many questions remain to be explored, such as possible cognitive alteration associated with HIMAL and whether HIMAL predisposes to the development of HS. Further studies using high-quality MRI, unified consensus qualitative and quantitative diagnostic criteria, and comprehensive cognitive assessment are recommended.

Analyzing suspension upright of a Formula Society of Automotive Engineers style vehicle.

This project was aimed at modelling the stress and deformation profile of a 6061-T6 aluminium suspension upright of a formula society of automotive engineers style vehicle with a double wishbone suspension under the loading conditions of a 1.5G corner. With these results, it would need to be determined whether the design is fit for use. Using remote displacement boundary conditions for the upper and lower wishbone connections and the control arm connection with a remote force at the centre of the wheel patch acting on the bearing surfaces the maximum stress, overall stress profile and maximum deformation of the upright was calculated. These results after, undertaking a verification and validation study, were a maximum equivalent von-Mises stress of 87.358MPa and a maximum bearing surface deflection of 0.21 mm. The maximum von-Mises stress calculated was less than the fatigue limit of 90MPa signalling infinite life and also less than the yield stress of 240MPa signalling a safe design. Verification and validation techniques were used to ensure the final result was accurate and reflected the real – life system. Structural error was used to verify the results where it was found that maximum structural error in the upright was 0.052mJ and at the location of maximum stress was between 0.0058-1.0782e-8 mJ. Validation of the model was achieved by comparing the reaction forces calculated in ANSYS to theoretical values and was found that the magnitudes were within 2.5% of the theoretical values, thus the model was considered valid.

Quantifying input uncertainty in the calibration of water quality models: reshuffling errors via the secant method

Uncertainty in inputs can significantly impair parameter estimation in water quality modeling, necessitating accurate quantification of input errors. However, decomposing input error from model residual error is still challenging. This study develops a new algorithm, referred to as Bayesian error analysis with reshuffling (BEAR), to address this problem. The basic approach requires sampling errors from a pre-estimated error distribution and then reshuffling them with their inferred ranks via the secant method. This approach is demonstrated in the case of total suspended solids (TSS) simulation via a conceptual water quality model. Based on case studies using synthetic data, the BEAR method successfully isolates the input error and parameter error. The results of a real case study demonstrate that even with the presence of model structural error and output data error, the BEAR method can approximate the true input and bring a better model fit through an effective input modification. However, its effectiveness is limited by the assumption that the input uncertainty should be dominant and that the prior information of the input error model can be estimated. The application of the BEAR method in TSS simulation is effective for understanding a range of water quality conditions and the further developed algorithm can be extended to other water quality predictions.

Accuracy calibration of a 3-CRU translational parallel robot based on the subset of error measurements

Robot accuracy calibration is an effective method to improve its kinematic accuracy. However, most of the existing calibration methods need to measure the complete set of 6-dimensional pose errors of the end-effector, which makes the calibration process especially complicated. In this paper, an accuracy calibration method for a 3-CRU translational parallel robot is proposed based on the subset of error measurements. The process is implemented by four steps: 1) the error model is established based on matrix method. Then the structural errors to be identified are separated. 2) part of pose errors of the end-effector are measured by laser tracker and used to form the subset of error measurements. 3) the minimum structural error linear combination affecting robot accuracy is determined according to the minimum parameter error linear combination theorem. After that, the structural errors can be identified based on the subset of error measurements. 4) error compensation based on the identification results. This method can not only ensure the identifiability of the structural errors, but also can realize error identification based on the subset of error measurements, which will significantly reduce the calibration workload and improve the calibration efficiency. Experiments are carried out to prove the effectiveness of the calibration method.

Macro-systems role of marketing: Do we trade environment for welfare?

The purpose of this research is to advance understanding of the macro-systems role of marketing. The author augments the equivocal principle of marketing (EPM) with the hypothesis that marketing has a negative indirect impact on societal welfare. The estimation of a structural error correction model in the context of the U.S. marketing system confirms that there exists a negative long-run relationship between environmental entropy and sustainable welfare with marketing positively associated with environmental entropy. This fact invalidates the assumptions behind the trade-off conjecture, which could only be supported if one is willing to accept the economic welfare myth. © SAGE Publications 2011.

Macro-systems role of marketing: Do we trade environment for welfare?

The purpose of this research is to advance understanding of the macro-systems role of marketing. The author augments the equivocal principle of marketing (EPM) with the hypothesis that marketing has a negative indirect impact on societal welfare. The estimation of a structural error correction model in the context of the U.S. marketing system confirms that there exists a negative long-run relationship between environmental entropy and sustainable welfare with marketing positively associated with environmental entropy. This fact invalidates the assumptions behind the trade-off conjecture, which could only be supported if one is willing to accept the economic welfare myth. © SAGE Publications 2011.

The emergence of community models in hydrology

<p>Hydrological models (HMs) are essential tools to explore terrestrial water dynamics and to anticipate future hydrological events. Since their inception, HMs have been developed in parallel by different institutions. There is now a plethora of HMs, yet a relative absence of cross-model developments (code is almost never portable between models) and of guidance on model selection (modellers typically stick to the model they are most familiar with). Furthermore, traditional HMs, developed over the last decades by successive code additions, are rarely adapted to modern hydrological challenges, principally because they lack modularity. These HMs typically rely on a single model structure (most processes are simulated by a single set of equations), which make it difficult to i) understand differences between models, ii) run a large ensemble of models, iii) capture the spatial variability of hydrological processes and iv) develop and improve hydrological models in a coordinated fashion across the community.</p><p>These limitations can be overcome by modular modelling frameworks (MMFs), which are master templates for model generation. MMFs offer several options for each important modelling decision. They also allow users to add functionalities when they are required, by loading libraries developed and maintained by the community. This presentation uses FUSE (Framework for Understanding Structural Error) as an example of MMF for hydrology. FUSE enables the generation of a myriad of conceptual HMs by recombining elements from four commonly-used models. This presentation will summarize the development of FUSE version 2 (FUSE2), which was created with users in mind and significantly increases the usability and range of applicability of the original FUSE. In FUSE2, NetCDF output files contain a detailed description of the modelling decisions (e.g., selected modules, numerical scheme, parameter values), which improves reproducibility. FUSE2 also makes code re-usable, as modules can be used across the community and are not limited to a single model structure. After decades of siloed model development, we argue that MMFs are essential to develop and improve hydrological models in a coordinated fashion across the community.</p>

An Analysis of Grammatical Errors made by Senegalese English Majors

This study identifies, classifies, and interprets the grammatical errors made by 30 second-year students at the English Department of Cheikh Anta Diop University of Dakar (Senegal) in their grammar translation tests. The said students took a course on Grammar during the academic year 2018-2019. The exercises consist of translating some French sentences into English. The errors made by these participants can be classified into seven categories. There are errors in the use of tenses, auxiliaries, articles, relative adverbs. In addition, morphological and structural error types and other errors related to the choice of verbs (i.e., tell/say) have also been listed. The results show that the participants made both inter-lingual errors (due to the influence of French language) and intra-lingual errors (over generalization of rules, addition or omission of items, etc.).