Abstract: Our scientific and technological worldviews are largely dominated by the concepts of entropy and complexity. Originating in 19th-century thermodynamics, the concept of entropy merged with information in the last century, leading to definitions of entropy and complexity by Kolmogorov, Shannon and others. In its simplest form, this worldview is an application of the normal rules of arithmetic. In this worldview, when tossing a coin, a million heads or tails in a row is theoretically possible, but impossible in practice and in real life. On this basis, the impossible (in the binary case, the outermost entries of Pascal's triangle xn and yn for large values of n) can be safely neglected, and one can concentrate fully on what is common and what conforms to the law of large numbers, in fields ranging from physics to sociology and everything in between. However, in recent decades it has been shown that what is most improbable tends to be the rule in nature. Indeed, if one combines the outermost entries xn and yn with the normal rules of arithmetic, either addition or multiplication, one obtains Lamé curves and power laws respectively. In this article, some of these correspondences are highlighted, leading to a double conclusion. First, Gabriel Lamé's geometric footprint in mathematics and the sciences is enormous. Second, conic sections are at the core once more. Whereas mathematics so far has been exclusively the language of patterns in the sciences, the door is opened for mathematics to also become the language of the individual. The probabilistic worldview and Lamé's footprint can be seen as dual methods. In this context, it is to be expected that the notions of information, complexity, simplicity and redundancy benefit from this different viewpoint.

Abstract: Generalized Möbius-Listing surfaces and bodies generalize Möbius bands, and this research was motivated originally by solutions of boundary value problems. Analogous to cutting of the original Möbius band, for this class of surfaces and bodies, results have been obtained when cutting such bodies or surfaces. In general, cutting leads to interlinked and intertwined different surfaces or bodies, resulting in very complex systems. However, under certain conditions, the result of cutting can be a single surface or body, which reduces complexity considerably. These conditions are based on congruence and rotational symmetry of the resulting cross sections after cutting, and on the knife cutting the origin

Abstract: In this theoretical study, we assess the impact of a graphene capping layer on the resistivity of defective, extremely scaled interconnects. We investigate the effect of graphene capping on the electronic transport in ultrascaled interconnects, in the presence of grain boundary defects in the metal layer. We compare the results obtained using our quantum mechanical model to a simple parallel-conductor model and find that the parallel-conductor model does not capture the effect of the graphene cap correctly. At 0.5 nm metal thickness, the parallel-conductor model underestimates the conductivity by 3.0% to 4.0% for single-sided and double sided graphene capping, respectively.

Abstract: Intending to tackle road traffic noise in urban environments, noise barriers have been proven to effectively reduce environmental noise levels, leading to positive effects on noise perception by the exposed population. This work assesses the impacts of replacing an obsolete noise barrier in a site near a highway. The effects of this change were monitored via a combination of field surveys, acoustic measurements and noise maps. The results have shown that even though the barrier replacement led to a 4.1 dB reduction in the LA,eq,(15 min.), the annoyance levels of the respondents increased. Possibly, the expectations regarding the improvement of the noise barrier were not met, after a history of complaints. Additionally, existing exposure-response relationships were not successful in predicting the annoyance levels in this particular case. In this dataset, noise annoyance presented a weak link with reported health problems, while a strong correlation was found with the comfort level to perform activities outdoors. Questions regarding the COVID-19 pandemic showed that even though the respondents were spending more time at home, they were less annoyed due to road traffic noise in the period when circulation restrictions were in place.

Abstract: We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.

Abstract: The fine balance between dipole-field energy and anion drift force defines the switching mechanism during polarization reversal: for the first time we show that only Pbcm mechanism obeys the ferroelectric switching physics, whereas P4(2)/nmc (or any other) mechanism does not. However, with lower energy barrier, it represents an important antiferroelectric mechanism. Constraints relaxation can lead to 90 degrees polarization rotation (domain deactivation). Intrinsically, the Si/VO-doping can switch faster than undoped HfO2 or HfZrOx. Theoretical Arrhenius model / intrinsic material switching (DFT) overestimates the switching speed extracted from experiments.

Abstract: Precipitation is accompanied by the formation and migration of heterophase interfaces. Using the combined approach of advanced imaging and atomistic simulations, we studied the precipitate-matrix interfaces in various aluminium alloy systems, aiming to resolve their detailed atomic structures and illuminate their role in phase transformations.

Abstract: Fiber optic surface plasmon resonance (FO-SPR) has shown its potential for the detection of nucleic acids and more recently the technology has been combined with catalytic active strands such as DNAzymes. In this work, an innovative, generic solid-phase DNA sensor concept is presented, based on FO-SPR and PCR amplified DNAzyme activity. Improved levels of specificity and sensitivity were obtained down to picomolar concentrations. Moreover, the FO-SPR sensor concept enables AuNP amplified DNA target detection, independent of the target sequence length. The FO-SPR sensor was demonstrated for the screening of the mobile colistin resistance (MCR-2) gene, a gene important for the antimicrobial resistance in Gram-negative species such as E. Coli.

Abstract: In this work, a nonlinear dispersive multiphysic model based on Maxwell and asymptotic Smoluchowsky equations has been developed to analyze the electroporation phenomenon induced by pulsed electric field on biological cells. The irregular plasma membrane geometry has been modeled by incorporating in the numerical algorithm the Gielis superformula as well as the dielectric dispersion of the plasma membrane has been modeled using the multi-relaxation Debye-based relationship. The study has been carried out with the aim to compare our model implementing a thin plasma membrane with the simplified model in which the plasma membrane is modeled as a distributed impedance boundary condition. The numerical analysis has been performed exposing the cell to external electric pulses having rectangular shapes. By an inspection of the obtained results, significant differences can be highlighted between the two models confirming the need to incorporate the effective thin membrane into the numerical algorithm to well predict the cell response to the pulsed electric fields in terms of transmembrane voltages and pore densities, especially when the cell is exposed to external nanosecond pulses.

Abstract: Non-charge-based logic devices are promising candidates for future logic circuits. Interest in studying and developing these devices has grown dramatically in the past decade as they possess key advantages over conventional CMOS technology. Due to their novel designs, a large number of micromagnetic simulations are required to fully characterize the behavior of these devices. The number and complexity of these simulations place large computational requirements on device development. We use state-of-the-art machine learning techniques to expedite identification of their behavior. Several intelligent sampling strategies are combined with machine learning multi-class classification models. These techniques are applied to a recently developed exchange-driven magnetic logic scheme that utilizes direct exchange coupling as the main driver.

Abstract: uIn this work, we discuss three novel Ti (germano-)silicidation techniques featuring respectively the pre-contact amorphization implantation (PCAI), the TiSi co-deposition, and Ti atomic layer deposition (ALD). All three techniques form TiSix(Ge-y) contacts with ultralow contact resistivity (rho(c)) of (1-3)x10(-9) Omega.cm(2) on both highly doped n-Si and p-SiGe substrates: these techniques meet rho(c) requirement of 5-14 nm CMOS technology and feature unified CMOS contact solutions. We further discuss the compatibility of these techniques to the realistic CMOS transistor fabrication.

Abstract: For the first time, we demonstrate, using an atomistic description of a 30nm diameter spin-transfer-torque magnetic random access memories (STT-MRAM), that the difference in mechanical properties of its sub-nanometer layers induces a high compressive strain in the magnetic tunnel junction (MTJ) and leads to a detrimental magnetostrictive effect. Our model explains the issues met in engineering the electrical and magnetic performances in scaled STT-MRAM devices. The resulting high compressive strain built in the stack, particularly in the MgO tunnel barrier (t-MgO), and its associated non-uniform atomic displacements, impacts on the quality of the MTJ interface and leads to strain relieve mechanisms such as surface roughness and adhesion issues. We illustrate that the strain gradient induced by the different materials and their thicknesses in the stacks has a negative impact on the tunnel magneto-resistance (TMR), on the magnetic nucleation process and on the STT-MRAM performance.