The accuracy of three-dimensional prediction of soft tissue changes following the surgical correction of facial asymmetry: an innovative concept

The accuracy of three-dimensional (3D) predictions of soft tissue changes in the surgical correction of facial asymmetry was evaluated in this study. Preoperative (T1) and 6–12-month postoperative (T2) cone beam computed tomography scans of 13 patients were studied. All patients underwent surgical correction of facial asymmetry as part of a multidisciplinary treatment protocol. The magnitude of the surgical movement was measured; virtual surgery was performed on the preoperative scans using Maxilim software. The predicted soft tissue changes were compared to the actual postoperative appearance (T2). Mean (signed) distances and mean (absolute) distances between the predicted and actual 3D surface meshes for each region were calculated. The one-sample t-test was applied to test the alternative hypothesis that the mean absolute distances had a value of <2.0 mm. A novel directional analysis was applied to analyse the accuracy of the prediction of soft tissue changes. The results showed that the distances between the predicted and actual postoperative soft tissue changes were less than 2.0 mm in all regions. The predicted facial morphology was narrower than the actual surgical changes in the cheek regions. 3D soft tissue prediction using Maxilim software in patients undergoing the correction of facial asymmetry is clinically acceptable

Analysis of Reinforced Concrete Columns Subjected to Combined Axial, Flexure, Shear, and Torsional Loads

This paper describes the implementation of a 3-dimensional concrete constitutive model for fiber-based analysis of reinforced concrete members subjected to combined loadings including torsion. The proposed model is formulated to address the interaction between the axial force, bidirectional shear, biaxial bending, and torsion. The shear mechanism along the beam is modeled using a Timoshenko beam approach with three dimensional (3-D) frame elements with arbitrary cross-section geometry. The model considers the 3D equilibrium, compatibility, and constitutive laws of materials at the section and structural level. The concrete constitutive law follows the Softened Membrane Model (SMM) with a tangent-stiffness formulation. The emphasis of the paper is on evaluation of the effect of the different stress states on the global and local behavior of the member. The ability of the model to assess the ultimate strength, stiffness, energy dissipation, failure modes under 3-dimensional loading is evaluated by correlation of analytical results with experimental tests of RC specimens

Non-linear analysis of reinforced concrete walls under three-dimensional loading

Analytical studies are conducted to develop an effective analytical model to simulate the non-linear response of reinforced concrete (RC) walls subjected to three-dimensional (3D) loads. The interaction between the concrete and steel is taken into account with consideration of the smeared behaviour of steel and tension stiffening of concrete. The proposed model is formulated to address the interaction between the axial force, shear, bending and torsion loads. The shear mechanism along the beam is modelled by adopting a Timoshenko beam approach for 3D frame elements with arbitrary cross-section geometry. The non-linear behaviour of the composite element is derived entirely from the constitutive laws of concrete and steel. The concrete constitutive model follows the softened membrane model that predicts the tensile cracking, compression crushing, strain softening, steel yielding and material damage under combined loadings. The validity of the model is established through a correlation study of experimentally tested RC shear walls subjected to monotonic loading conditions

Behavioral thermoregulation in the American lobster Homarus americanus

It is generally accepted that water temperature has a strong influence on the behavior of the American lobster Homarus americanus. However, there is surprisingly little behavioral evidence to support this view. To haracterize the behavioral responses of lobsters to thermal gradients, three different experiments were conducted. In the first, 40 lobsters acclimated to summer water temperatures (summer-acclimated, 15.5±0.2 °C, mean ±S.E.M.) were placed individually in an experimental shelter, and the temperature in the shelter was gradually raised until the lobster moved out. Lobsters avoided water warmer than 23.5±0.4 °C, which was an increase of 8.0±0.4 °C from ambient summer temperatures. When this experiment was repeated with lobsters acclimated to winter temperatures (winter-acclimated, 4.3±0.1 °C), the lobsters (N=30) did not find temperature increases of the same magnitude (∆T=8.0±0.4 °C) aversive. The second experiment was designed to allow individual summer-acclimated lobsters (N=22) to select one of five shelters, ranging in temperature from 8.5 to 25.5 °C. After 24 h, 68 % of the lobsters occupied the 12.5 °C shelter, which was slightly above the ambient temperature (approximately 11 °C). In a similar experiment, winter-acclimated lobsters (N=30) were given a choice between two shelters, one at ambient temperature (4.6±0.2 °C) and one at a higher temperature (9.7±0.3 °C). Winter-acclimated lobsters showed a strong preference (90 %) for the heated shelter. In the final experiment, summer-acclimated lobsters (N=9) were allowed to move freely in a tank having a thermal gradient of approximately 10 °C from one end to the other. Lobsters preferred a thermal niche of 16.5±0.4 °C and avoided water that was warmer than 19 °C or colder than 13 °C. When standardized for acclimation temperature, lobsters preferred water 1.2±0.4 °C above their previous ambient temperature. Collectively, the results of these studies indicate that lobsters are capable of sensing water temperature and use this information to thermoregulate behaviorally. The implications of these findings for lobster behavior and distribution in their natural habitat are discussed

Earth-like sand fluxes on Mars

Strong and sustained winds on Mars have been considered rare, on the basis of surface meteorology measurements and global circulation models, raising the question of whether the abundant dunes and evidence for wind erosion seen on the planet are a current process. Recent studies showed sand activity, but could not determine whether entire dunes were moving—implying large sand fluxes—or whether more localized and surficial changes had occurred. Here we present measurements of the migration rate of sand ripples and dune lee fronts at the Nili Patera dune field. We show that the dunes are near steady state, with their entire volumes composed of mobile sand. The dunes have unexpectedly high sand fluxes, similar, for example, to those in Victoria Valley, Antarctica, implying that rates of landscape modification on Mars and Earth are similar

Wavelength dependent light tunable resistive switching graphene oxide nonvolatile memory devices

This paper reports on the first optically tunable graphene oxide memristor device. Modulation of resistive switching memory by light opens the route to new optoelectronic devices that can be switched optically and read electronically. Applications include integrated circuits with memory elements switchable by light and optically reconfigurable and tunable synaptic circuits for neuromorphic computing and brain-inspired, artificial intelligence systems. In this report, planar and vertical structured optical resistive switching memristors based on graphene oxide are reported. The device is switchable by either optical or electronic means, or by a combination of both. In addition the devices exhibit a unique wavelength dependence that produces reversible and irreversible properties depending on whether the irradiation is long or short wavelength light, respectively. For long wavelength light, the reversible photoconductance effect permits short-term dynamic modulation of the resistive switching properties of the light, which has application as short-term memory in neuromorphic computing. In contrast, short wavelength light induces both the reversible photoconductance effect and an irreversible change in the memristance due to reduction of the graphene oxide. This has important application in the fabrication of cloned neural networks with factory defined weights, enabling the fast replication of artificial intelligent chips with pre-trained information

Nanoscale junctions for single molecule electronics fabricated using bilayer nanoimprint lithography combined with feedback controlled electromigration

Nanoimprint lithography (NIL) is a fast, simple and high throughput technique that allows fabrication of structures with nanometre precision features at low cost. We present an advanced bilayer nanoimprint lithography approach to fabricate four terminal nanojunction devices for use in single molecule electronic studies. In the first part of this work, we demonstrate a NIL lift-off process using a bilayer resist technique that negates problems associated with metal side-wall tearing during lift-off. In addition to precise nanoscale feature replication, we show that it is possible to imprint micron-sized features while still maintaining a bilayer structure enabling an undercut resist structure to be formed. This is accomplished by choosing suitable imprint parameters as well as residual layer etching depth and development time. We then use a feedback controlled electromigration procedure, to produce room-temperature stable nanogap electrodes with sizes below 2 nm. This approach facilitates the integration of molecules in stable, solid-state molecular electronic devices as demonstrated by incorporating benzenethiol as molecular bridges between the electrodes and characterizing its electronics properties through current-voltage measurements. The observation of molecular transport signatures, showing current suppression in the I-V behaviour at low voltage, which is then lifted at high voltage, signifying on- and off-resonant transport through molecular levels as a function of voltage, is confirmed in repeated I-V sweeps. The large conductance, symmetry of the I-V sweep and small value of the voltage minimum in transition voltage spectroscopy indicates the bridging of the two benzenethiol molecules is by π-stacking

Federated-Learning-Assisted Failure-Cause Identification in Microwave Networks

Machine Learning (ML) adoption for automated failure management is becoming pervasive in today's communication networks. However, ML-based failure management typically requires that monitoring data is exchanged between network devices, where data is collected, and centralized locations, e.g., servers in data centers, where data is processed. ML algorithms in this centralized location are then trained to learn mappings between collected data and desired outputs, e.g., whether a failure exists, its cause, location, etc. This paradigm poses several challenges to network operators in terms of privacy as well as in terms of computational and communication resource usage, as a massive amount of sensible failure data is transmitted over the network. To overcome such limitations, Federated Learning (FL) can be adopted, which consists of training multiple distributed ML models at multiple decentralized locations (called 'clients') using a limited amount of locally-collected data, and of sharing these trained models to a centralized location (called 'server'), where these models are aggregated and shared again with clients. FL reduces data exchange between clients and a server and improves algorithms' performance thanks to sharing knowledge among different domains (i.e., clients), leveraging different sources of local information in a collaborative environment. In this paper, we focus on applying FL to perform failure-cause identification in microwave networks. The problem is modeled as a multi-class ML classification problem with six pre-defined failure causes. Specifically, using real failure data from an operational microwave network composed of more than 10000 microwave links, we emulate a multi-operator scenario in which one operator has partial knowledge of failure causes during the training phase. Thanks to knowledge sharing, numerical results show that FL achieves up to 72% precision in identifying an unknown particular class concerning traditional ML (non- FL) approaches where training is performed without knowledge sharing

Method to reduce the formation of crystallites in ZnO nanorod thin-films grown via ultra-fast microwave heating

© 2018 This paper discusses the nucleation and growth mechanisms of ZnO nanorod thin-films and larger sized crystallites that form within the solution and on surfaces during an ultra-fast microwave heating growth process. In particular, the work focusses on the elimination of crystallites as this is necessary to improve thin-film uniformity and to prevent electrical short circuits between electrodes in device applications. High microwave power during the early stages of ZnO deposition was found to be a key factor in the formation of unwanted crystallites on substrate surfaces. Once formed, the crystallites, grow at a much faster rate than the nanorods and quickly dominate the thin-film structure. A new two-step microwave heating method was developed that eliminates the onset of crystallite formation, allowing the deposition of large-area nanorod thin-films that are free from crystallites. A dissolution-recrystallization mechanism is proposed to explain why this procedure is successful and we demonstrate the importance of the work in the fabrication of low-cost memristor devices