From the Sakai-Sugimoto Model to the Generalized Skyrme Model

We derive the generalized Skyrme model as a low-energy effective model of the Sakai-Sugimoto model. The novelty with the past is the presence of the sextic term equal to the topological charge squared. This term appears when the $\omega$ meson, and the tower of states on top of it, are integrated out. We claim that, in the small 't Hooft coupling limit, the instanton is well described by a Skyrmion arising from the low energy effective Lagrangian of the Sakai-Sugimoto model. The sextic term plays a dominant role in this limit. Moreover, when a pion mass term is added we recover the BPS Skyrme model in the small 't Hooft coupling limit.Comment: 17 pages, 6 figures. v2: minor correction

Determination of forest road surface roughness by kinect depth imaging

Roughness is a dynamic property of the gravel road surface that affects safety, ride comfort as well as vehicle tyre life and maintenance costs. A rapid survey of gravel road condition is fundamental for an effective maintenance planning and definition of the intervention priorities. Different non-contact techniques such as laser scanning, ultrasonic sensors and photogrammetry have recently been proposed to reconstruct three-dimensional topography of road surface and allow extraction of roughness metrics. The application of Microsoft Kinect\u2122 depth camera is proposed and discussed here for collection of 3D data sets from gravel roads, to be implemented in order to allow quantification of surface roughness. The objectives are to: i) verify the applicability of the Kinect sensor for characterization of different forest roads, ii) identify the appropriateness and potential of different roughness parameters and iii) analyse the correlation with vibrations recoded by 3-axis accelerometers installed on different vehicles. The test took advantage of the implementation of the Kinect depth camera for surface roughness determination of 4 different forest gravel roads and one well-maintained asphalt road as reference. Different vehicles (mountain bike, off-road motorcycle, ATV vehicle, 4WD car and compact crossover) were included in the experiment in order to verify the vibration intensity when travelling on different road surface conditions. Correlations between the extracted roughness parameters and vibration levels of the tested vehicles were then verified. Coefficients of determination of between 0.76 and 0.97 were detected between average surface roughness and standard deviation of relative accelerations, with higher values in the case of lighter vehicles

Modelling noise propagation generated by forest operations: a case study in Southern Italy

Noise is defined as an undesired sound that constitutes an unwarranted disturbance potentiality modifying animal behaviour or normal functioning. Forest operations commonly involve the use of equipment and machines that can produce noise and be a potential permanent or temporary disturbance for the wildlife. This study simulates noise propagation in a natural area generated during coppice stand harvesting by direct field noise measurements and the application of a specific GIS model. Two working phases were investigated: felling and yarding operations. Two potential systems were analyzed for the yarding operations: a) yarding by mobile cable yarder and b) skidding by tractor with a three point log grapple. The results are reported in terms of excess noise area for the third-octave bandwidths with the centre in 500 Hz, 1000 Hz and 2000 Hz. Felling by chainsaws presented the largest area where noise exceeds the ambient natural noise, while in the case of yarding, mobile cable yarding operations presented a smaller area of excess noise than skidding by tractor

Tensile force monitoring on large winch-assist forwarders operating in British Columbia

The forest industry around the world is facing common challenges in accessing wood fiber on steep terrain. Fully mechanized harvesting systems based on specialized machines, such as winch-assist forwarders, have been specifically developed for improving the harvesting perfor- mances in steep grounds. While the mechanization process is recognized as a safety benefit, the use of cables for supporting the machine traction needs a proper investigation. Only a few studies have analyzed the cable tensile forces of winch-assist forwarders during real operations, and none of them focused on large machines normally used in North America. Consequently, a preliminary study focused on tensile force analysis of large winch-assist forwarders was conducted in three sites in the interior of British Columbia during the fall of 2017. The results report that in 86% of the cycles, the maximum working load of the cable was less than one-third of the minimum breaking load. The tensile force analysis showed an expected pattern of minimum tensile forces while the forwarders were traveling or unloading on the road site and high tensile forces when operating on steep trails, loading or traveling. Further analysis found that the maximum cycle tensile forces occurred most frequently when the machines were moving uphill, independently of whether they were empty or loaded. While the forwarders were operating on the trails, slope, travel direction, and distance of the machines from the anchor resulted statistically significant and able to account for 49% of tensile force variability. However, in the same conditions, the operator settings accounted for 77% of the tensile force variability, suggesting the human factor as the main variable in cable tensile force behavior during winch-assist operations

Towards automated visualisation of scientic literature

Nowadays, an exponential growth in biological data has been recorded, including both structured and unstructured data. One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured textual corpora to effectively support the decision making process. Since the emergence of topic modelling, new and interesting approaches to compactly represent the content of a document collection have been proposed. However, the effective exploitation of the proposed strategies requires a lot of expertise. This paper presents a new scalable and exploratory data visualisation engine, named ACE-HEALTH (AutomatiC Exploration of textual collections for HEALTH-care), whose target is to easily analyse medical document collections through the Latent Dirichlet Allocation. To streamline the analytics process and enhance the effectiveness of data and knowledge exploration, a variety of data visualisation techniques have been integrated in the engine to provide navigable informative dashboards without requiring any a-priori knowledge on the analytics techniques. Preliminary results obtained on a real PubMed collection show the effectiveness of ACE-HEALTH in correctly capturing the high-level overview of textual medical collections through innovative visualisation techniques

Automating concept-drift detection by self-evaluating predictive model degradation

A key aspect of automating predictive machine learning entails the capability of properly triggering the update of the trained model. To this aim, suitable automatic solutions to self-assess the prediction quality and the data distribution drift between the original training set and the new data have to be devised. In this paper, we propose a novel methodology to automatically detect prediction-quality degradation of machine learning models due to class-based concept drift, i.e., when new data contains samples that do not fit the set of class labels known by the currently-trained predictive model. Experiments on synthetic and real-world public datasets show the effectiveness of the proposed methodology in automatically detecting and describing concept drift caused by changes in the class-label data distributions.Comment: 5 pages, 4 figure

Useful ToPIC: Self-tuning strategies to enhance Latent Dirichlet Allocation

ToPIC (Tuning of Parameters for Inference of Concepts) is a distributed self-tuning engine whose aim is to cluster collections of textual data into correlated groups of documents through a topic modeling methodology (i.e., LDA). ToPIC includes automatic strategies to relieve the end-user of the burden of selecting proper values for the overall analytics process. ToPIC's current implementation runs on Apache Spark, a state-of-the-art distributed computing framework. As a case study, ToPIC has been validated on three real collections of textual documents characterized by different distributions. The experimental results show the effectiveness and efficiency of the proposed solution in analyzing collections of documents without tuning algorithm parameters and in discovering cohesive and well-separated groups of documents with a similar topic

First Enantioselective Synthesis of Tetracyclic Intermediates en route to Madangamine D

The enantioselective synthesis of advanced tetracyclic precursors of madangamine D, bearing rings ABCD of this alkaloid, is reported. The saturated 14-membered ring is assembled from functionalized diazatricyclic intermediates 10 following either ring-closing metathesis or macrolactamization strategies

Total Synthesis of (+)-Madangamine D

Madangamines are a group of bioactive marine sponge alkaloids, embodying an unprecedented diazapentacyclic skeletal type. The enantioselective total synthesis of madangamine D has been accomplished, and represents the first total synthesis of an alkaloid of the madangamine group. It involves the stereoselective construction of the diazatricyclic ABC core using a phenylglycinol-derived lactam as the starting enantiomeric scaffold and the subsequent assembly of the peripheral macrocyclic rings. The synthesis provides, for the first time, a pure sample of madangamine D and confirms the absolute configuration of this alkaloid family