Background and aims Vegetation can be used to stabilise slopes with regard to shallow landslides, but the optimal plant architecture for conferring resistance is not known. This study aims at ...identifying root morphological traits which confer the most resistance to soil during shearing. Methods Three species used for slope stabilisation (Ricinus communis L., Jatwpha curcas L. and Rhus chinensis Mill.) were grown for 10 months in large shear boxes filled with silty clay similar to that found in Yunnan, China. Direct shear tests were then performed and compared to fallow soil. Root systems were excavated and a large number of traits measured. Results Shear strength and deformation energy were enhanced by the presence of roots. Regardless of confining pressure, R. communis conferred most resistance due to its taprooted system with many vertical roots. J. curcas possessed oblique and vertical roots which created fragile zones throughout the soil profile. The least efficient root system was R. chinensis which possessed many horizontal lateral roots. Soil mechanical properties were most influenced by (i) density of roots crossing the shear plane, (ii) branching density throughout the soil profile, (iii) total length of coarse roots above the shear plane and (iv) total volume of coarse roots and fine root density below the shear plane. During failure, fine, short and branched roots slipped through soil rather than breaking. Conclusion Root morphological traits such as density, branching, length, volume, inclination and orientation influence significantly soil mechanical properties.
The past few decades have seen substantial growth in Additive Manufacturing (AM) technologies. However, this growth has mainly been process-driven. The evolution of engineering design to take ...advantage of the possibilities afforded by AM and to manage the constraints associated with the technology has lagged behind. This paper presents the major opportunities, constraints, and economic considerations for Design for Additive Manufacturing. It explores issues related to design and redesign for direct and indirect AM production. It also highlights key industrial applications, outlines future challenges, and identifies promising directions for research and the exploitation of AM's full potential in industry.
In 1958, Anderson predicted the localization of electronic wavefunctions in disordered crystals and the resulting absence of diffusion. It is now recognized that Anderson localization is ubiquitous ...in wave physics because it originates from the interference between multiple scattering paths. Experimentally, localization has been reported for light waves, microwaves, sound waves and electron gases. However, there has been no direct observation of exponential spatial localization of matter waves of any type. Here we observe exponential localization of a Bose-Einstein condensate released into a one-dimensional waveguide in the presence of a controlled disorder created by laser speckle. We operate in a regime of pure Anderson localization, that is, with weak disorder-such that localization results from many quantum reflections of low amplitude-and an atomic density low enough to render interactions negligible. We directly image the atomic density profiles as a function of time, and find that weak disorder can stop the expansion and lead to the formation of a stationary, exponentially localized wavefunction-a direct signature of Anderson localization. We extract the localization length by fitting the exponential wings of the profiles, and compare it to theoretical calculations. The power spectrum of the one-dimensional speckle potentials has a high spatial frequency cutoff, causing exponential localization to occur only when the de Broglie wavelengths of the atoms in the expanding condensate are greater than an effective mobility edge corresponding to that cutoff. In the opposite case, we find that the density profiles decay algebraically, as predicted in ref. 13. The method presented here can be extended to localization of atomic quantum gases in higher dimensions, and with controlled interactions.
In national hospital databases, certain prognostic factors cannot be taken into account. The main objective was to estimate the performance of two models based on two databases: the Epithor clinical ...database and the French hospital database. For each of the two databases, we randomly sampled a training dataset with 70% of the data and a validation dataset with 30%. The performance of the models was assessed with the Brier score, the area under the receiver operating characteristic (AUC ROC) curve and the calibration of the model. For Epithor and the hospital database, the training dataset included 10,516 patients (with resp. 227 (2.16%) and 283 (2.7%) deaths) and the validation dataset included 4507 patients (with resp. 93 (2%) and 119 (2.64%) deaths). A total of 15 predictors were selected in the models (including FEV1, body mass index, ASA score and TNM stage for Epithor). The Brier score values were similar in the models of the two databases. For validation data, the AUC ROC curve was 0.73 0.68-0.78 for Epithor and 0.8 0.76-0.84 for the hospital database. The slope of the calibration plot was less than 1 for the two databases. This work showed that the performance of a model developed from a national hospital database is nearly as good as a performance obtained with Epithor, but it lacks crucial clinical variables such as FEV1, ASA score, or TNM stage.
The longtime productivity of an industrial machine is improved by condition-based maintenance strategies. To do this, the integration of sensors and other cyber-physical devices is necessary in order ...to capture and analyze a machine's condition through its lifespan. Thus, choosing the best sensor is a critical step to ensure the efficiency of the maintenance process. Indeed, considering the variety of sensors, and their features and performance, a formal classification of a sensor's domain knowledge is crucial. This classification facilitates the search for and reuse of solutions during the design of a new maintenance service. Following a Knowledge Management methodology, the paper proposes and develops a new sensor ontology that structures the domain knowledge, covering both theoretical and experimental sensor attributes. An industrial case study is conducted to validate the proposed ontology and to demonstrate its utility as a guideline to ease the search of suitable sensors. Based on the ontology, the final solution will be implemented in a shared repository connected to legacy CAD (computer-aided design) systems. The selection of the best sensor is, firstly, obtained by the matching of application requirements and sensor specifications (that are proposed by this sensor repository). Then, it is refined from the experimentation results. The achieved solution is recorded in the sensor repository for future reuse. As a result, the time and cost of the design process of new condition-based maintenance services is reduced.
Support structures are necessary for many AM (additive manufacturing) processes to maintain the overhanging areas or resist deformation caused by thermal stress in printing. The design of support ...structures affects not only the printing quality but also material consumption and post-processing time. Current research had proposed many support structure designs and optimization methods to meet varying optimization requirements. However, no research has investigated yet how to determine the support points, or contact points for optimal support design. The number and position of support points will directly affect the support structure’s performance and volume, and therefore the final printing quality. To fill this gap, this paper proposes a determination method, which integrates overhang detection, analysis of periodic support point patterns and AM constraints to optimize support point distribution with ensured manufacturability. It is particularly critical for complex and porous structures in medical applications. The proposed method is illustrated and validated through a complex dental component. It can be used with support structure design methods to further improve the support structure performance and reduce support volume in printing, especially for metallic AM processes.
Objective Our objective was to analyze the time trend variation of 30-day mortality after lung cancer surgery, and to quantify the impact of surgeon and hospital volumes over a 5-year period in ...France. Methods We used Epithor, the French national thoracic database and benchmark tool, which catalogues more than 180,000 procedures of 89 private and public hospitals in France. From January 2005 to December 2010, 19,556 patients who underwent major lung resection (lobectomy, bilobectomy, pneumonectomy) were included in our study. Multilevel logistic models were designed to investigate the relationship between 30-day mortality and surgeon (model 1) or hospital (model 2) volumes. The 3 levels considered were the patient, the surgeon, and the hospital. Results From 2005 to 2007, the 30-day mortality of patients who underwent major lung resection averaged 10%, and then decreased until it reached 3.8% in 2010 ( P < .0001). A significant decrease in 30-day mortality was observed over time ( P = .0046). During the study period, the mean annual number of procedures per surgeon was 46.1 (standard deviation SD = 23.6) and per hospital was 97.9 (SD = 50.8). Model 1 showed that surgeon volume had a significant impact on 30-day mortality ( P = .03), whereas model 2 failed to show that hospital volume influenced 30-day mortality ( P = .75). Conclusions Since 2007, when France's first National Cancer Plan became effective, 30-day mortality of primary lung cancer surgery has decreased and currently measures 3.8%. Low mortality was correlated with higher surgeon volume but was not influenced by hospital volume, which cannot be considered a proxy measure for determining the safety of lung cancer surgery.
Support structure plays an important role on sustaining overhang areas, resistingshape deformation and reducing thermal stress in many additive manufacturing (AM) processes. However, design of ...support structures in the preparation stage and removing of those structures in the post-processing stage are still time-consuming and costly. To reduce support structure volume, post-processing time and improve the printing quality, this paper proposes a novel enhanced bio-inspired generative design method, integrating parametric L-system rules and lattice structure configuration, to generate lightweight, easy-to-remove and heat-diffusion-friendly biomimetic support structures. A complex dental component with freeform geometries and discontinuous support areas is selected as a case study to compare with existing popular support design methods. The comparison results show the proposed method exhibits a good support performance for complex dental overhang areas. Hence it has potential to be adopted for other AM processes where support structures are required.
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