Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Automated Productivity Models for Earthmoving Operations

Earthmoving operations have significant importance, particularly for civil infrastructure projects. The performance of these operations should be monitored regularly to support timely recognition of undesirable productivity variances. Although productivity assessment occupies high importance in earthmoving operations, it does not provide sufficient information to assist project managers in taking the necessary actions in a timely manner. Assessment only is not capable of identifying problems encountered in these operations and their causes. Many studies recognized conditions and related factors that influence productivity of earthmoving operations. These conditions are mainly project-specific and vary from one project to another. Most of reported work in the literature focused on assessment rather than analysis of productivity. This study presents three integrated models that automate productivity measurement and analysis processes with capabilities to detect different adverse conditions that influence the productivity of earthmoving operations. The models exploit innovations in wireless and remote sensing technologies to provide project managers, contractors, and decision makers with a near-real-time automated productivity measurement and analysis. The developed models account for various uncertainties associated with earthmoving projects. The first model introduces a fuzzy-based standardization for customizing the configuration of onsite data acquisition systems for earthmoving operations. While the second model consists of two interrelated modules. The first is a customized automated data acquisition module, where a variety of sensors, smart boards, and microcontrollers are used to automate the data acquisition process. This module encompasses onsite fixed unit and a set of portable units attached to each truck used in the earthmoving fleet. The fixed unit is a communication gateway (Meshlium®), which has integrated MySQL database with data processing capabilities. Each mobile unit consists of a microcontroller equipped with a smart board that hosts a GPS module as well as a number of sensors such as accelerometer, temperature and humidity sensors, load cell and automated weather station. The second is a productivity measurement and analysis module, which processes and analyzes the data collected automatically in the first module. It automates the analysis process using data mining and machine learning techniques; providing a near-real-time web-based visualized representation of measurement and analysis outcomes. Artificial Neural Network (ANN) was used to model productivity losses due to the existence of different influencing conditions. Laboratory and field work was conducted in the development and validation processes of the developed models. The work encompassed field and scaled laboratory experiments. The laboratory experiments were conducted in an open to sky terrace to allow for a reliable access to GPS satellites. Also, to make a direct connection between the data communication gateway (Meshlium®), initially installed on a PC computer to observe the received data latency. The laboratory experiments unitized 1:24 scaled loader and dumping truck to simulate loading, hauling and dumping operations. The truck was instrumented with the microcontroller equipped with an accelerometer, GPS module, load cell, and soil water content sensor. Thirty simulated earthmoving cycles were conducted using the scaled equipment. The collected data was recorded in a micro secure digital (SD) card in a comma separated value (CSV) format. The field work was carried out in the city of Saint-Laurent, Montreal, Quebec, Canada using a passenger vehicle to mimic the hauling truck operational modes. Fifteen Field simulated earthmoving cycles were performed. In this work two roads with different surface conditions, but of equal length (1150 m) represented the haul and return roads. These two roads were selected to validate the developed road condition analysis algorithm and to study the model’s capability in determining the consequences of adverse road conditions on the haul and return durations and thus on the tuck and fleet productivity. The data collected from the lab experiments and field work was used as input for the developed model. The developed model has shown perfect recognition of the state of truck throughout the fifteen field simulated earthmoving cycles. The developed road condition analysis algorithm has demonstrated an accuracy of 83.3% and 82.6% in recognizing road bumps and potholes, respectively. Also, the results indicated tiny variances in measuring the durations compared with actual durations using time laps displayed on a smart cell telephone; with an average invalidity percentage AIP% of 1.89 % and 1.33% for the joint hauling and return duration and total cycle duration, respectively

Concepts and Their Dynamics: A Quantum-Theoretic Modeling of Human Thought

We analyze different aspects of our quantum modeling approach of human concepts, and more specifically focus on the quantum effects of contextuality, interference, entanglement and emergence, illustrating how each of them makes its appearance in specific situations of the dynamics of human concepts and their combinations. We point out the relation of our approach, which is based on an ontology of a concept as an entity in a state changing under influence of a context, with the main traditional concept theories, i.e. prototype theory, exemplar theory and theory theory. We ponder about the question why quantum theory performs so well in its modeling of human concepts, and shed light on this question by analyzing the role of complex amplitudes, showing how they allow to describe interference in the statistics of measurement outcomes, while in the traditional theories statistics of outcomes originates in classical probability weights, without the possibility of interference. The relevance of complex numbers, the appearance of entanglement, and the role of Fock space in explaining contextual emergence, all as unique features of the quantum modeling, are explicitly revealed in this paper by analyzing human concepts and their dynamics.Comment: 31 pages, 5 figure

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Development, test and comparison of two Multiple Criteria Decision Analysis(MCDA) models: A case of healthcare infrastructure location

When planning a new development, location decisions have always been a major issue. This paper examines and compares two modelling methods used to inform a healthcare infrastructure location decision. Two Multiple Criteria Decision Analysis (MCDA) models were developed to support the optimisation of this decision-making process, within a National Health Service (NHS) organisation, in the UK. The proposed model structure is based on seven criteria (environment and safety, size, total cost, accessibility, design, risks and population profile) and 28 sub-criteria. First, Evidential Reasoning (ER) was used to solve the model, then, the processes and results were compared with the Analytical Hierarchy Process (AHP). It was established that using ER or AHP led to the same solutions. However, the scores between the alternatives were significantly different; which impacted the stakeholders‟ decision-making. As the processes differ according to the model selected, ER or AHP, it is relevant to establish the practical and managerial implications for selecting one model or the other and providing evidence of which models best fit this specific environment. To achieve an optimum operational decision it is argued, in this study, that the most transparent and robust framework is achieved by merging ER process with the pair-wise comparison, an element of AHP. This paper makes a defined contribution by developing and examining the use of MCDA models, to rationalise new healthcare infrastructure location, with the proposed model to be used for future decision. Moreover, very few studies comparing different MCDA techniques were found, this study results enable practitioners to consider even further the modelling characteristics to ensure the development of a reliable framework, even if this means applying a hybrid approach

Exploring the mathematics of motion through construction and collaboration

In this paper we give a detailed account of the design principles and construction of activities designed for learning about the relationships between position, velocity and acceleration, and corresponding kinematics graphs. Our approach is model-based, that is, it focuses attention on the idea that students constructed their own models – in the form of programs – to formalise and thus extend their existing knowledge. In these activities, students controlled the movement of objects in a programming environment, recording the motion data and plotting corresponding position-time and velocity-time graphs. They shared their findings on a specially-designed web-based collaboration system, and posted cross-site challenges to which others could react. We present learning episodes that provide evidence of students making discoveries about the relationships between different representations of motion. We conjecture that these discoveries arose from their activity in building models of motion and their participation in classroom and online communities

A Bayesian Hierarchical Model for Comparative Evaluation of Teaching Quality Indicators in Higher Education

The problem motivating the paper is the quantification of students' preferences regarding teaching/coursework quality, under certain numerical restrictions, in order to build a model for identifying, assessing and monitoring the major components of the overall academic quality. After reviewing the strengths and limitations of conjoint analysis and of the random coefficient regression model used in similar problems in the past, we propose a Bayesian beta regression model with a Dirichlet prior on the model coefficients. This approach not only allows for the incorporation of informative prior when it is available but also provides user friendly interfaces and direct probability interpretations for all quantities. Furthermore, it is a natural way to implement the usual constraints for the model weights/coefficients. This model was applied to data collected in 2009 and 2013 from undergraduate students in Panteion University, Athens, Greece and besides the construction of an instrument for the assessment and monitoring of teaching quality, it gave some input for a preliminary discussion on the association of the differences in students preferences between the two time periods with the current Greek economic and financial crisis

Managing multi-cultural engineering teams in Egypt

The exercise of project management altered drastically over the past two decades and is currently driven by the growing demand of standardizing the industry’s practice and the emerging globalization of the industry. According to the literature review, one of the obstacles facing the development of the engineering teams in both developed and developing countries is the poor performances of multi-located and multi-cultural teams. The literature review reflects on the influences of the culture complexity and social diversity on the multi-cultural team’s performances. There have been a number of empirical studies that focus on the performances of the effectiveness of these teams. However most of the studies carried out focused on the individual’s experience within the context of developed countries. Given the global trend towards internationalization, there is a need to understand the parameters that determine the success of the foreign firms operating in the Egyptian construction market. This thesis is intended to explore the influences of the multi-national firms on the designers in the Egyptian construction industry and the various obstacles faced by the formal due to the diversity in their teams. The research employed a qualitative experiment on multi-cultural multi-located teams in one of the foreign firms in Egypt in order to capture the influences of the social and culture diversity on their performances. A quantitative survey was also conducted to gather relevant information about the multi-cultural teams from the Egyptian and foreign leaders in the construction industry. The verdict of the mixed method methodology illustrates the importance of considering the cultural index and various managerial techniques while operating in Egypt. The data gathered is analyzed using the Porter model, which was amended by various scholars to suit the needs of the designers in the construction industry. The data analyzed proposes a framework that includes key parameters to ensure superior performance by the multi-located and multi-cultural design engineering teams. The validation of the framework is conducted by follow-up deliberations with Egyptian and international experience managers from select companies participating in global engineering activities. The participants confirmed the significant of the proposed framework. A verification experiment is conducted on multi-cultural virtual teams, originally involved in the qualitative experiment, to ensure the significance of the framework developed in the real life situation. Following the framework’s establishment, a model is developed to assist the foreign companies’ rate their compatibility with the Egyptian construction industry. The aim of the model is to elevate the performances of the international company from a managerial perspective and provide recommendations in regard to the challenges faced while operating in Egypt. The model is validated through an external validity exercise. The thesis thus aimed at understanding the challenges, motives, benefits of the multi-cultural firms working in the Egyptian design construction industry. The framework and model developed are intended to provide a significant step to launch a successful operation of the multi-cultural and multi-located engineering teams in Egypt. The thesis concludes the importance of considering social and cultural aspects while developing a managerial approach which is to be formulated through an effective straightforward organizational culture. However with the continuous changes in the construction industry, it is highly recommended that future research and experiment be conducted on the multi-cultural teams in Egypt taking various socio-economic variations into consideration

QUANTIFYING THE EFFECT OF CONSTRUCTION SITE FACTORS ON CONCRETE QUALITY, COSTS AND PRODUCTION RATES

Factors affecting concrete can be categorized as structured factors or unstructured factors. The first group of factors consists of those related to the production process of concrete including water-cement ratio, properties of raw materials and mix proportions. Unstructured factors or construction site factors are related to labor skills and local conditions during the construction process of a project. Concrete compressive strength as a quality metric, costs and production rates may be affected significantly by such factors while performing concrete operations at the jobsite. Several prior studies have investigated the effect of structured factors on concrete. However literature is limited regarding the effects of unstructured factors during the construction phase of a facility. This study proposes a systematic methodology to identify and quantify the effects of construction site factors including crew experience, compaction method, mixing time, curing humidity and curing temperature on concrete quality, costs and production rates using fuzzy inference systems. First, the perceived importance of construction-related factors is identified and evaluated through literature review and a survey deployed to construction experts. Then, the theory of design of experiments (DOE) is used to conduct a full 25 factorial experiment consisting of 32 runs and 192 compressive strength tests to identify statistically significant unstructured factors. Fuzzy inference systems (FISs) are proposed for predicting concrete compressive strength, costs and production rate effects through the use of adapted network-based fuzzy inference system (ANFIS). Finally, an optimization model is formulated and tested for managing concrete during the construction process of a facility. Literature review and survey results showed that curing humidity, crew experience, and compaction method are the top three factors perceived by construction experts to affect concrete compressive strength, whereas crew experience, mixing time and compaction method are the top three factors affecting concrete costs and production rates. Additionally, crew experience, compaction and mixing time were found to dominate global ranking of perceived affecting factors through the application of the relative importance index (RII). When conducting designed experiments and analysis of variance (ANOVA), compaction method, mixing time, curing humidity and curing temperature were identified to be statistically significant construction site factors for concrete compressive strength whereas crew experience, compaction method and mixing time were statistically significant factors for cost and production rates. A Sugeno type fuzzy inference system (FIS) for quantifying compressive strength, cost and production rate effects was created by using ANFIS, having correlation coefficients (R-squared values) greater than 93%, indicating that resulting models predict new observations well. Curing temperature (i.e., on-site curing temperature) was identified to be the most affecting condition for concrete compressive strength while mixing time had the biggest impact on concrete cost and production rates. The developed FISs can be used as a decision–support tool that allows for determining desired operating conditions, that ensures specified compressive strength, saves resources and maximizes profits when fabricating, placing and curing concrete