Towards More Realistic Models

This chapter is devoted to the presentation of a more realistic version of the model, Risk and Rumours, which extends the previous, theoretical version (Routes and Rumours) by including additional empirical and experimental information following the process described in Part II of this book. We begin by offering a reflection on the integration of the five elements of the modelling process, followed by a more detailed description of the Risk and Rumours model, and how it differs from the previous version. Subsequently, we present selected results of the uncertainty and sensitivity analysis, enabling us to make further inference on the information gaps and areas for potential data collection. We also present model calibration for an empirically grounded version of the model, Risk and Rumours with Reality. In that way, we can evaluate to what extent the iterative modelling process has enabled a reduction in the uncertainty of the migrant route formation. In the final part of the chapter, we reflect on the model-building process and its implementation.

Measurement and Simulation of Flow in a Section of a Mine Gallery

Research work on the air flow in mine workings frequently utilises computer techniques in the form of numeric simulations. However, it is very often necessary to apply simplifications when building a geometrical model. The assumption of constant model geometry on its entire length is one of the most frequent simplifications. This results in a substantial shortening of the geometrical model building process, and a concomitant shortening of the time of numerical computations; however, it is not known to what extent such simplifications worsen the accuracy of simulation results. The paper presents a new methodology that enables precise reproduction of the studied mine gallery and the obtaining of a satisfactory match between simulation results and in-situ measurements. It utilises the processing of data from laser scanning of a mine gallery, simultaneous multi-point measurements of the velocity field at selected gallery cross-sections, unique for mine conditions, and the SAS turbulence model, recently introduced to engineering analyses of flow issues.

USING INDIVIDUAL-BASED MODELING TO BETTER UNDERSTAND THE HIDDEN CURRICULUM OF ENGINEERING

Engineering culture perpetuates norms that are unwelcoming to minoritized identities, particularlywomen and racialized folks. A theory useful for understanding this is “hidden curriculum” whichdescribes the assumptions and beliefs that are unintentionally and implicitly taught in engineeringeducation. This paper outlines an initial conceptual model for using IBM (individual-based modeling) to better understand the hidden curriculum of engineering. We provide an overview of the driving question behind the model design, the agents and their attributes, the rules andprocesses which change these attributes, and the scale of the model. This overview of the model building process provides insight into the model design for simulating and better understanding the perpetuation of the hidden curriculum within engineering education.

An approach to rapid processing of camera trap images with minimal human input

Point 1: Camera traps have become an extensively utilized tool in ecological research, but the processing of images created by a network of camera traps rapidly becomes an overwhelming task, even for small networks. Point 2: We used transfer training to create convolutional neural network (CNN) models for identification and classification. By utilizing a small dataset with less than 10,000 labeled images the model was able to distinguish between species and remove false triggers. Point 3: We trained the model to detect 17 object classes with individual species identification, reaching an accuracy of 92%. Previous studies have suggested the need for thousands of images of each object class to reach results comparable to those achieved by human observers; however, we show that such accuracy can be achieved with fewer images. Point 4: Additionally, we suggest several alternative metrics common to computer science studies to accurately evaluate the performance of such camera trap image processing models, as well as methods to adapt the model building process to two targeted purposes.

A Review of Methodologies for Automating a Simulation Process

Today’s world is run by technology, wherein every field the primary aim has become to reduce manual intervention and reduce efforts put by humans. This reduction of effort helps in increasing value-added time and decreasing the nonproductive or the non-value added time. A few of the tasks such a creating a simulation model that requires heavy manual efforts has been a common topic for researches to automate and improve. If today the time taken to manually create a model can reduce then this can lead to saving not only time and also knowledge as then the application can be given more importance than the labor in building the model. This paper concentrates on automating the simulation model building process and speaks of the different approaches taken up by various authors to help automate it. The approaches are based on data, common tools or artificial intelligence.

Single Order of Multiple Regression Model of Water Quality Index (WQI) in Manjung River and its Tributaries

This research highlights a multi-variety technique to examine the relationship between dependent and independent variable in predicting the water quality index in Manjung Rivers and its affluents. The model building process been used to analyse and generate the data. There are 63 possible models for single order multiple regressions. The number of possible model started to reduce as we started to eliminate insignificant variable. This model then needs to run under eight selection criteria to identify the best model. The best model will be certified by using Mean absolute percentage error (MAPE) in order to measure the validity of the model.

Payload Based Internet Worm Disclosure using Neural Network

With the capacity of contaminating a huge number of hosts, worms speak to a noteworthy danger to the Internet. The identification against Internet worms is generally an open issue. Web worms represent a genuine danger to PC security. Conventional methodologies utilizing marks to identify worms posture little risk to the zero day assaults. The focal point of this exploration is moving from utilizing mark examples to distinguishing the vindictive conduct showed by the Internet worms. This paper displays an original thought of separating stream level highlights that can distinguish worms from clean projects utilizing information mining method, for example, neural system classifier. Our approach demonstrated 97.90% recognition rate on Internet worms whose information was not utilized as a part of the model building process

COMPARING MODEL-BUILDING PROCESS: A MODEL PROSPECTIVE TEACHERS USED IN INTERPRETING STUDENTS’ MATHEMATICAL THINKING

Mathematical thinking is an important aspect of mathematics education and, therefore, also needs to be understood by prospective teachers. Prospective teachers should have the ability to analyze and interpret students’ mathematical thinking. Comparing model is one of the interpretation models from Wilson, Lee, and Hollebrands. This article will describe the prospective teacher used the model of the building process in interpretation students' mathematical thinking. Subjects selected by considering them in following the students’ strategies in solving the Building Construction Problem. Comparing model is a model of interpretation in which a person interprets student thinking based on student work. There are two types comparing model building process prospective teacher use in interpreting students’ mathematical thinking ie. comparing work and comparing knowledge. In comparing works, prospective teachers use an external representation rubric. This is used to analyze student activities in order to provide an interpretation that is comparing the work of students with their own work. In comparing knowledge, prospective teachers use internal representation rubrics to provide interpretation by comparing the students' work with their knowledge or thought.

‘What is this corpus about?’: using topic modelling to explore a specialised corpus

This paper introduces topic modelling, a machine learning technique that automatically identifies ‘topics’ in a given corpus. The paper illustrates its use in the exploration of a corpus of academic English. It first offers the intuitive explanation of the underlying mechanism of topic modelling and describes the procedure for building a model, including the decisions involved in the model-building process. The paper then explores the model. A topic in topic models is characterised by a set of co-occurring words, and we will demonstrate that such topics bring us rich insights into the nature of a corpus. As exemplary tasks, this paper identifies the prominent topics in different parts of papers, investigates the chronological change of a journal, and reveals different types of papers in the journal. The paper further compares topic modelling to two more traditional techniques in corpus linguistics, semantic annotation and keywords analysis, and highlights the strengths of topic modelling. We believe that topic modelling is particularly useful in the initial exploration of a corpus.