Robust DC and efficient time-domain fast fault simulation

Purpose – Imperfections in manufacturing processes may cause unwanted connections (faults) that are added to the nominal, "golden", design of an electronic circuit. By fault simulation one simulates all situations. Normally this leads to a large list of simulations in which for each defect a steady-state (DC) solution is determined followed by a transient simulation. We improve the robustness and the e¿iciency of these simulations. Design/methodology/approach – Determining the DC solution can be very hard. For this we present an adaptive time domain source stepping procedure that can deal with controlled sources. The method can easily be combined with existing pseudo-transient procedures. The method is robust and e¿cient. In the subsequent transient simulation the solution of a fault is compared to a golden, fault-free, solution. A strategy is developed to e¿ciently simulate the faulty solutions until their moment of detection. Finding – We fully exploit the hierarchical structure the circuit in the simulation process to bypass parts of the circuit that appear to be una¿ected by the fault. Accurate prediction and e¿cient solution procedures lead to fast fault simulation. Originality/value – Our fast fault simulation helps to store a database with detectable deviations for each fault. If such a detectable output "matches" a result of a product that has been returned because of malfunctioning it helps to identify the subcircuit that may contain the real fault. One aims to detect as much as possible candidate faults. Because of the many options the simulations must be very e¿cient

Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations

T-distributed stochastic neighbour embedding (t-SNE) is a widely used data visualisation technique. It differs from its predecessor SNE by the low-dimensional similarity kernel: the Gaussian kernel was replaced by the heavy-tailed Cauchy kernel, solving the "crowding problem" of SNE. Here, we develop an efficient implementation of t-SNE for a $t$-distribution kernel with an arbitrary degree of freedom $\nu$, with $\nu\to\infty$ corresponding to SNE and $\nu=1$ corresponding to the standard t-SNE. Using theoretical analysis and toy examples, we show that $\nu<1$ can further reduce the crowding problem and reveal finer cluster structure that is invisible in standard t-SNE. We further demonstrate the striking effect of heavier-tailed kernels on large real-life data sets such as MNIST, single-cell RNA-sequencing data, and the HathiTrust library. We use domain knowledge to confirm that the revealed clusters are meaningful. Overall, we argue that modifying the tail heaviness of the t-SNE kernel can yield additional insight into the cluster structure of the data

A pedagogical model for effective online teacher professional development—findings from the Teacher Academy initiative of the European Commission

During their careers, teachers experience change in education policy, societal trends, and cultural shifts in pedagogical thought, requiring continual adaptation and innovation of their practices. Coupled with this is an assumed intrinsic desire to progress, whether as part of their own subject expertise, or with a view to taking on a role as leader in school management or a specialist area. Effective support and opportunities for teachers to develop and apply their competences is crucial for maintaining both motivation and high standards in the profession. However, many teachers across Europe claim to struggle to have access to effective forms of continued professional development coupled with the numerous demands already made on their work. On-site courses with opportunities for peer learning remain popular but demand time and are not financially cost-effective in reaching a large number of teachers, nor are they viable during pandemic restrictions. By exploring the pedagogical model of the online courses of the European Commission's Teacher Academy in the context of these challenges, this article discusses how an effective, collaborative approach to online continued professional development can be developed as a way of addressing both teacher and education system needs

Mapping contacts between regulatory domains of skeletal muscle TnC and Tnl by analyses of a single-chain chimeras.

The troponin (Tn) complex is formed by TnC, TnI and TnT and is responsible for the calcium-dependent inhibition of muscle contraction. TnC and TnI interact in an antiparallel fashion in which the N domain of TnC binds in a calcium-dependent manner to the C domain of TnI, releasing the inhibitory effect of the latter on the actomyosin interaction. While the crystal structure of the core cardiac muscle troponin complex has been determined, very little high resolution information is available regarding the skeletal muscle TnITnC complex. With the aim of obtaining structural information regarding specific contacts between skeletal muscle TnC and TnI regulatory domains, we have constructed two recombinant chimeric proteins composed of the residues 191 of TnC linked to residues 98182 or 98147 of TnI. The polypeptides were capable of binding to the thin filament in a calcium-dependent manner and to regulate the ATPase reaction of actomyosin. Small angle X-ray scattering results showed that these chimeras fold into compact structures in which the inhibitory plus the C domain of TnI, with the exception of residues 148182, were in close contact with the N-terminal domain of TnC. CD and fluorescence analysis were consistent with the view that the last residues of TnI (148182) are not well folded in the complex. MS analysis of fragments produced by limited trypsinolysis showed that the whole TnC N domain was resistant to proteolysis, both in the presence and in the absence of calcium. On the other hand the TnI inhibitory and C-terminal domains were completely digested by trypsin in the absence of calcium while the addition of calcium results in the protection of only residues 114137