When will we get a new class of analgesic agent based on animal study data?

Background: Tramadol is an analgesic agent utilized in the treatment of different types of pain. Recently it was found that a few cases of tramadol poisoning were observed in hospitalized children. In all cases, it happened due to accidental administration of tramadol suppository. Objective: The aim of this study was to determine the clinical manifestations of tramadol suppository poisoning in children and to create public awareness about the prevention of this suppository poisoning. Materials & Methods: This was an observational, retrospective, single-center caseseries study of hospitalized children in Dhaka Shishu Hospital during the periods of July 2016 to March 2017. Eleven patients with history of accidental intake of tramadol suppositories were enrolled in this study. Data regarding risk factors and clinical features were recorded and analyzed. Results: In this study, 7/11(64%) cases were male and 4/11(36%) female. Mean age was 7.8 month and mean duration of hospital stay was 46.4 hours. Parecetamol in 2/11 (18%) cases and glycerin suppository in 9/11(82%) cases were main drugs that were supposed to be introduced instead of tramadol. Keeping drugs in same containers (11/11), similarities of suppository’s size, shape and color (11/11) were found as causes of mistakes in this study. Main clinical features were drowsiness in 4/11(36%) cases, tachycardia in 3/11(27%) cases and one case died. Conclusions: More cautiousness of parents, dissimilarity among different suppository in color size, shape and using of different containers of drugs may be the preventive way of this poisoning. Bangladesh J Child Health 2019; VOL 43 (3) :157-160

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AGENT-BASED MICRO-SIMULATIONS IN IMMUNOLOGY AND FINANCE

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

10.17770/etr2003vol1.1978 ◽

2006 ◽

Vol 1 ◽

pp. 319

Author(s):

F. Castiglione

Keyword(s):

Message Passing ◽

Degrees Of Freedom ◽

Computational Models ◽

Physical Models ◽

Discrete State ◽

Agent Based ◽

Computer Tools ◽

New Class ◽

Distributed Memory Machines ◽

Tools And Techniques

In the search for computational models that help to understand the dynamics of Complex Systems, one can take a great advantage from the impressive acceleration of computer tools and techniques. In fact the very structure of computation on digital computers has inspired the introduction of new class of models (algorithms), where interaction among degrees of freedom are expressed by logical rules acting over a discrete state space – something much closer to "biological language" than to standard (floating point) physical models. Starting from the definitions of spin systems, with little changes we reach a definition a new model that is well suited to describe different simulation systems. Such class of models is can be considered a subclass of the Agent-Based systems in vogue nowadays. Moreover, we shortly describe two microscopic simulators of this type, which are being used to study microscopic phenomena in two completely different fields of application, namely immunology and finance. As a final remark, given the lattice representation of space, such computational-modeling paradigm is well suited for efficient and "relatively simple" parallelization. Indeed, both models have been implemented to run on parallel computers adopting the Message Passing paradigm for Distributed Memory machines.

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Use of Sensors and Analyzers Data for Load Forecasting: A Two Stage Approach

Sensors ◽

10.3390/s20123524 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3524 ◽

Cited By ~ 1

Author(s):

Daniel Ramos ◽

Brigida Teixeira ◽

Pedro Faria ◽

Luis Gomes ◽

Omid Abrishambaf ◽

...

Keyword(s):

Forecast Error ◽

Load Forecasting ◽

Study Data ◽

Sensor Data ◽

Time Data ◽

Time Intervals ◽

Two Stage ◽

K Nearest Neighbors ◽

Agent Based

The increase in sensors in buildings and home automation bring potential information to improve buildings’ energy management. One promissory field is load forecasting, where the inclusion of other sensors’ data in addition to load consumption may improve the forecasting results. However, an adequate selection of sensor parameters to use as input to the load forecasting should be done. In this paper, a methodology is proposed that includes a two-stage approach to improve the use of sensor data for a specific building. As an innovation, in the first stage, the relevant sensor data is selected for each specific building, while in the second stage, the load forecast is updated according to the actual forecast error. When a certain error is reached, the forecasting algorithm (Artificial Neural Network or K-Nearest Neighbors) is trained with the most recent data instead of training the algorithm every time. Data collection is provided by a prototype of agent-based sensors developed by the authors in order to support the proposed methodology. In this case study, data over a period of six months with five-minute time intervals regarding eight types of sensors are used. These data have been adapted from an office building to illustrate the advantages of the proposed methodology.

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