Endoscopic sclerotherapy is useful in Dieulafoy''s disease

1.5% Capsaicin (Cap) or Vehicle was respectively used to treat the right or left sciatic nerve in 20 Sprague-Dawley rats. On the seventh day, the 20 rats were at random divided into electroacupuncture (EA) group and non-EA group, the spinal cord corresponding to the afferent segments of sciatic nerve was taken out for observing the changes of acetylcholinesterase (AChE) activity and [3H]-quinuelidinylbenzylate (QNB) binding sites in the spinal dorsal horn (SDH). The results were as follows: (1) EA "Huantiao" could enhance AChE activity in the SDH and decrease [3H]-QNB binding sites; (2) Cap treating sciatice nerve could weaken AChE activity in the SDH and merease [3H]-QNB binding sites; (3) Cap treatment could inhibit or partially inhibit the actions of EA as above. The results indicated that ACh participated in the primary afferent of acupuncture information and might exist in Cap-sensitive neurons.     

Therapeutic strategies in acute myocardial infarction. Results of STIM 93 registry

A registry was set up by the national college of cardiologists practicing in general hospitals in February 1993. The data concerned mode of admission, demographic details, initial clinical and haemodynamic evaluation and hospital outcome. Special attention was given to the electrocardiographic changes before and, in patients receiving thrombolytic therapy, after treatment. An analysis of predictive factors for mortality was performed both in the group of patients "revascularised" and in the group treated conventionally. One thousand and twenty three cases from 327 centres were analysed. There were 1292 men and 531 women, with an average age of 67.9 years. The average time interval from onset of symptoms to hospital admission was 5 h 30 min, 56.8% of patients arriving within 6 hours. 36.4% of the population underwent thrombolysis or angioplasty, 75% of patients under 75 years of age admitted before the 5th hours underwent a procedure of myocardial revascularisation. The hospital mortality was 14%, 8.7% in those revascularised and 17% in patients treated conventionally. Factors predictive of mortality were age, female gender, Killip Classes III or IV, systolic blood pressure of less than 100 mmHg, heart rate of more than 100/min and contraindications of thrombolysis. The maximum ST depression, the sum of ST depression, the sum of ST elevation and depression, were also significant predictive factors of a fatal hospital outcome in the whole population group and in patients treated conventionally. In the reperfused group, only the initial sum of ST elevation and depression was predictive of mortality: 120 minutes after the beginning of thrombolysis, the sum of ST elevations and of elevations and depressions was predictive of twice the mortality when the values exceeded 0.6 mv and 1.4 mv respectively.     

Complexity issues in color-preserving graph embeddings

In the context of comparative analysis of protein–protein interaction graphs, we use a graph-based formalism to detect the preservation of a given protein complex (pattern graph) in the protein–protein interaction graph (target graph) of another species with respect to (w.r.t.) orthologous proteins. We give an efficient exponential-time randomized algorithm in case the occurrence of the pattern graph in the target graph is required to be exact. For approximate occurrences, we prove a tight inapproximability result and give four approximation algorithms that deal with bounded degree graphs, small ortholog numbers, linear forests and very simple yet hard instances, respectively.     

A proposal for student modeling based on ontologies and diagnosis rules

The advances in the educational field and the high complexity of student modeling have provoked it to be one of the aspects more investigated in Intelligent Tutoring Systems (ITSs). The Student Models (SMs) should not only represent the student’s knowledge, but rather they should reflect, as faithfully as possible, the student’s reasoning process. To facilitate this goal, in this article a new approach to student modeling is proposed that benefits from the advantages of Ontological Engineering, advancing in the pursue of a more granular and complete knowledge representation. It’s focused, mainly, on the SM cognitive diagnosis process, and we present a method providing a rich diagnosis about the student’s knowledge state – especially, about the state of learning objectives reached or not. The main goal is to achieve SMs with a good adaptability to the student’s features and a high flexibility for its integration in varied ITSs.     

Digital Elevation Model generation for historical landscape analysis based on LiDAR data,a case study in Flanders (Belgium)

This paper discusses the generation of a high precision DEM (Digital Elevation Model) based on high density airborne LiDAR (Light Detection and Ranging) data for an interdisciplinary landscape archaeological study concerning the settlement history and environment in Sandy Flanders, a region to the north of Ghent (Belgium). The objective was to create a detailed topographical surface free of artificial features and topographical artefacts, in the form of a DEM, visualizing the natural and current topography through the implementation of true ground points only. The semi-automatical removal of these features and artefacts was based on topographical vector data, visual interpretations and slope analysis. Ultimately two DEM’s were constructed (1) a TIN (Triangulated Irregular Network) model, whereby the inherent large file format restricts the usability to large scale and (2) a grid model which can be used for small-, medium- and large-scale applications. Both datasets were used as an image that is interpreted using ancillary data from historical sources. Its usefulness is illustrated in a case of field pattern and microfield topography. Starting from this DEM, the approach of this landscape historical study is mainly retrogressive, i.e. starting from the landscape structures and elements that are still present in the contemporary landscape and moving into the past.     

Infrared target-flare discrimination using a ZISC hardware neural network

The last generation of infrared imaging aircraft seekers and trackers uses pattern recognition algorithms to find and keep a lock on an aircraft in the presence of decoy flares. These algorithms identify targets, based on the features of the various objects in the missile’s field of view. Because modern both aircrafts and missiles fly faster than sound, speed of operation of the target identifier is critical. In this article, we propose a target recognition system that respects this time constraint. It is based on an artificial neural network implemented in hardware, as a set of parallel processors on a commercially available silicon chip called a ZISC, for zero instruction set computer. This chip would be integrated in the infrared missile seeker and tracker. We describe the characteristics of the images that the image processing module of this seeker and tracker extracts from the infrared video frames and show how to construct from these translation and rotation invariant features that can be used as input to the neural network. We determine the individual discriminating power of these features by constructing their histograms, which allows us to eliminate some as not being useful for our purpose. Finally, by testing our system on real data, we show that it has a 90% success rate in aircraft-flare identification, and a processing time that during this time, the aircrafts and missiles will have traveled only a few millimeters. Most of the images on which the neural network makes its mistakes are seen to be hard to recognize even by a human expert.     

Multi-contact vertical ladder climbing with an HRP-2 humanoid

We describe the research and the integration methods we developed to make the HRP-2 humanoid robot climb vertical industrial-norm ladders. We use our multi-contact planner and multi-objective closed-loop control formulated as a QP (quadratic program). First, a set of contacts to climb the ladder is planned off-line (automatically or by the user). These contacts are provided as an input for a finite state machine. The latter builds supplementary tasks that account for geometric uncertainties and specific grasps procedures to be added to the QP controller. The latter provides instant desired states in terms of joint accelerations and contact forces to be tracked by the embedded low-level motor controllers. Our trials revealed that hardware changes are necessary, and parts of software must be made more robust. Yet, we confirmed that HRP-2 has the kinematic and power capabilities to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturing factories (e.g. aircraft, shipyard) and construction sites.     

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.