The atomic structure and chemistry of Fe-rich steps on antiphase boundaries in Ti-doped Bi_0.9Nd_0.15FeO3

Stepped antiphase boundaries are frequently observed in Ti-doped Bi<sub>0.85</sub>Nd<sub>0.15</sub>FeO<sub>3</sub>, related to the novel planar antiphase boundaries reported recently. The atomic structure and chemistry of these steps are determined by a combination of high angle annular dark field and bright field scanning transmission electron microscopy imaging, together with electron energy loss spectroscopy. The core of these steps is found to consist of 4 edge-sharing FeO<sub>6</sub> octahedra. The structure is confirmed by image simulations using a frozen phonon multislice approach. The steps are also found to be negatively charged and, like the planar boundaries studied previously, result in polarisation of the surrounding perovskite matrix

ANALYZING VERTICAL MARKET STRUCTURE AND ITS IMPLICATIONS FOR TRADE LIBERALIZATION AND MARKET ACCESS

International Relations/Trade,

Experimental investigation of cutting vibration during micro-end-milling of the straight groove

Micro-end-milling is a cutting technology that removes redundant material from machined workpieces by small-diameter end mills, and is widely used to manufacture miniature complex parts. During micro-end-milling, the cutting vibration caused by weak tool rigidity and high spindle speed is known as a key factor for decreasing machined quality and accelerating tool failure. This study reports on experiments of micro-end-milling of the straight groove for AISI 1045 steel. The waveform characteristics of acceleration vibration were revealed, the relationship between acceleration and milling parameters were analyzed and two types of relationship models were developed. The results show that, during micro-end-milling of the straight groove, the components of acceleration vibration from largest to smallest are in turn the transverse acceleration αY, the feed acceleration αX and the axial acceleration αZ. Compared with feed velocity vf and axial depth of cut ap, the spindle speed n has the highest influence on cutting vibration. The response surface model of acceleration vibration was shown to have a higher prediction accuracy compared to the power function model and is more suitable for the prediction and control of cutting vibration during micro-end-milling

Mobile agent path planning under uncertain environment using reinforcement learning and probabilistic model checking

The major challenge in mobile agent path planning, within an uncertain environment, is effectively determining an optimal control model to discover the target location as quickly as possible and evaluating the control system's reliability. To address this challenge, we introduce a learning-verification integrated mobile agent path planning method to achieve both the effectiveness and the reliability. More specifically, we first propose a modified Q-learning algorithm (a popular reinforcement learning algorithm), called Q EA−learning algorithm, to find the best Q-table in the environment. We then determine the location transition probability matrix, and establish a probability model using the assumption that the agent selects a location with a higher Q-value. Secondly, the learnt behaviour of the mobile agent based on Q EA−learning algorithm, is formalized as a Discrete-time Markov Chain (DTMC) model. Thirdly, the required reliability requirements of the mobile agent control system are specified using Probabilistic Computation Tree Logic (PCTL). In addition, the DTMC model and the specified properties are taken as the input of the Probabilistic Model Checker PRISM for automatic verification. This is preformed to evaluate and verify the control system's reliability. Finally, a case study of a mobile agent walking in a grids map is used to illustrate the proposed learning algorithm. Here we have a special focus on the modelling approach demonstrating how PRISM can be used to analyse and evaluate the reliability of the mobile agent control system learnt via the proposed algorithm. The results show that the path identified using the proposed integrated method yields the largest expected reward.</p

Why are listeners sometimes (but not always) egocentric?:Making inferences about using others' perspective in referential communication

Theory of Mind (ToM) is the ability to understand others' mental states, and that these mental states can differ from our own. Although healthy adults have little trouble passing conceptual tests of ToM (e.g., the false belief task [1]), they do not always succeed in using ToM [2,3]. In order to be successful in referential communication, listeners need to correctly infer the way in which a speaker's perspective constrains reference and inhibit their own perspective accordingly. However, listeners may require prompts to take these effortful inferential steps. The current study investigated the possibility of embedding prompts in the instructions for listeners to make inference about using a speaker's perspective. Experiment 1 showed that provision of a clear introductory example of the full chain of inferences resulted in large improvement in performance. Residual egocentric errors suggested that the improvement was not simply due to superior comprehension of the instructions. Experiment 2 further dissociated the effect by placing selective emphasis on making inference about inhibiting listeners' own perspective versus using the speaker's perspective. Results showed that only the latter had a significant effect on successful performance. The current findings clearly demonstrated that listeners do not readily make inferences about using speakers' perspectives, but can do so when prompted.</p

Rational design and characterization of nitric oxide biosensors in E. coli Nissle 1917 and Mini SimCells

Nitric oxide (NO) is an important disease biomarker found in many chronic inflammatory diseases and cancers. A well-characterized nitric sensing system is useful to aid the rapid development of bacteria therapy and synthetic biology. In this work, we engineered a set of NO-responsive biosensors based on the PnorV promoter and its NorR regulator in the norRVW operon; the circuits were characterized and optimized in probiotic Escherichia coli Nissle 1917 and mini SimCells (minicells containing designed gene circuits for specific tasks). Interestingly, the expression level of NorR displayed an inverse correlation to the PnorV promoter activation, as a strong expression of the NorR regulator resulted in a low amplitude of NO-inducible gene expression. This could be explained by a competitive binding mechanism where the activated and inactivated NorR competitively bind to the same site on the PnorV promoter. To overcome such issues, the NO induction performance was further improved by making a positive feedback loop that fine-tuned the level of NorR. In addition, by examining two integration host factor (IHF) binding sites of the PnorV promoter, we demonstrated that the deletion of the second IHF site increased the maximum signal output by 25% (500 μM DETA/NO) with no notable increase in the basal expression level. The optimized NO-sensing gene circuit in anucleate mini SimCells exhibited increased robustness against external fluctuation in medium composition. The NO detection limit of the optimized gene circuit pPnorVβ was also improved from 25.6 to 1.3 nM in mini SimCells. Moreover, lyophilized mini SimCells can maintain function for over 2 months. Hence, SimCell-based NO biosensors could be used as safe sensor chassis for synthetic biology

Faithful rule extraction for differentiable rule learning models

There is increasing interest in methods for extracting interpretable rules from ML models trained to solve a wide range of tasks over knowledge graphs (KGs), such as KG completion, node classification, question answering and recommendation. Many such approaches, however, lack formal guarantees establishing the precise relationship between the model and the extracted rules, and this lack of assurance becomes especially problematic when the extracted rules are applied in safety-critical contexts or to ensure compliance with legal requirements. Recent research has examined whether the rules derived from the influential Neural-LP model exhibit soundness (or completeness), which means that the results obtained by applying the model to any dataset always contain (or are contained in) the results obtained by applying the rules to the same dataset. In this paper, we extend this analysis to the context of DRUM, an approach that has demonstrated superior practical performance. After observing that the rules currently extracted from a DRUM model can be unsound and/or incomplete, we propose a novel algorithm where the output rules, expressed in an extension of Datalog, ensure both soundness and completeness. This algorithm, however, can be inefficient in practice and hence we propose additional constraints to DRUM models facilitating rule extraction, albeit at the expense of reduced expressive power