scholarly journals Awareness-related activity in prefrontal and parietal cortices reflects more than superior performance capacity: A blindsight case study

2010 ◽  
Vol 10 (7) ◽  
pp. 897-897 ◽  
Author(s):  
M. Davidson ◽  
N. Persaud ◽  
B. Maniscalco ◽  
D. Mobbs ◽  
R. Passingham ◽  
...  
NeuroImage ◽  
2011 ◽  
Vol 58 (2) ◽  
pp. 605-611 ◽  
Author(s):  
Navindra Persaud ◽  
Matthew Davidson ◽  
Brian Maniscalco ◽  
Dean Mobbs ◽  
Richard E. Passingham ◽  
...  

2019 ◽  
Vol 46 (12) ◽  
pp. 1160-1173 ◽  
Author(s):  
Zinab Abuwarda ◽  
Tarek Hegazy

Fast-tracking is an important process to speed the delivery of construction projects. To support optimum fast-tracking decisions, this paper introduces a generic schedule optimization framework that integrates four schedule acceleration dimensions: linear activity crashing; discrete activity modes of execution; alternative network paths; and flexible activity overlapping. Because excessive schedule compression can lead to space congestion and overstressed workers, the optimization formulation uses specific variables and constraints to prevent simultaneous use of overlapping and crashing at the same activity segment. To handle complex projects with a variety of milestones, resource limits, and constraints, the framework has been implemented using the constraint programming (CP) technique. Comparison with a literature case study and further experimentation demonstrated the flexibility and superior performance of the proposed model. The novelty of the model stems from its integrated multi-dimensional formulation, its CP engine, and its ability to provide alternative fast-track schedules to strictly constrained projects without overstressing the construction workers.


Author(s):  
Shekhar Murthy ◽  
Biswajeet Pattanayak

Purpose The purpose of this study is to adapt the principles of Academagogy (McAuliffe et al., 2009; McAuliffe and Winter, 2014) in Corporate Training. Academagogy is an amalgamation of learning theories, viz. pedagogy (teacher-centered learning); andragogy (student-centered learning); and, heutagogy (self-determined learning). 10;While the original paper by McAuliffe et al. focused on the application of Academagogical principles to higher education in a university, this case study extends Academagogy into corporate training. Design/methodology/approach Academagogy-based learning interventions were implemented in training programs conducted at IIC Academy. The study-sample comprised 144 trainees divided into the experiment and the control groups. Academagogical interventions were applied to the experiment group, whereas the control group followed conventional methods. Controlled quasi-experiment and statistical analyses were used as research tools in this case study. Findings The performance scores of the participants' at entry-level were analyzed through propensity score matching and chi-square tests. The results concluded that the experiment and the control group had no selection bias. 10; 10;The treatment effect applied to the experiment group was studied using scatter-plots and effect size analyses. 10; 10;The results indicate that the academagogical interventions markedly improved behavioral and professional skills among participants of the experiment group, which showed up in their superior performance, both during training and at work. Research limitations/implications This case study has two major limitations, viz. the sample population was homogenous; the batch-size was limited to 15 participants, which facilitated closer monitoring of the course. Originality/value Academagogical interventions were implemented in a corporate setting as opposed to earlier studies which focused on a university setting and academic environs.


Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1554 ◽  
Author(s):  
Kyungwon Kang ◽  
Hesham A. Rakha

Lane changes are complex safety- and throughput-critical driver actions. Most lane-changing models deal with lane-changing maneuvers solely from the merging driver’s standpoint and thus ignore driver interaction. To overcome this shortcoming, we develop a game-theoretical decision-making model and validate the model using empirical merging maneuver data at a freeway on-ramp. Specifically, this paper advances our repeated game model by using updated payoff functions. Validation results using the Next Generation SIMulation (NGSIM) empirical data show that the developed game-theoretical model provides better prediction accuracy compared to previous work, giving correct predictions approximately 86% of the time. In addition, a sensitivity analysis demonstrates the rationality of the model and its sensitivity to variations in various factors. To provide evidence of the benefits of the repeated game approach, which takes into account previous decision-making results, a case study is conducted using an agent-based simulation model. The proposed repeated game model produces superior performance to a one-shot game model when simulating actual freeway merging behaviors. Finally, this lane change model, which captures the collective decision-making between human drivers, can be used to develop automated vehicle driving strategies.


1977 ◽  
Vol 45 (1) ◽  
pp. 179-186 ◽  
Author(s):  
Dolores Geddes

A pair of autistic monozygotic twins were assessed on relevant portions of the Geddes Psychomotor Inventory. Over-all motor development of the 3-yr., 6-mo. old twins was similar and considered a partial consequence of the same genotype and comparable environmental experiences from birth. The twins exhibited poor or unsuccessful performance on tasks requiring abilities in language, communication, and appropriate relationships to objects; superior performance on specific fine manual motor skills, walking balance board, and climbing; at-age performance on tasks which were considered measures of patterned gross movement, balance, postural maintenance, and spatial orientation; and few typical autistic motor characteristics.


2021 ◽  
Author(s):  
◽  
Diego Navarro

<p>For years, understanding the relationship between behaviour and cognition has been a central concern of research conducted in the social sciences. In fields as diverse as anthropology, business, medicine, and education it is widely accepted that the development of practice (as a type of behaviour), depends on a precise understanding of how thought gets carried into action. However, studies investigating the complex interplay between a learner’s cognition (i.e. thoughts, knowledge, beliefs, and feelings about L2 learning) and their behaviour (i.e. language-related activity) are only recently garnering attention. In addition, only few studies have looked at this dynamic process with adult participants beyond the language learning classroom. Framed within the context of naturalistic language learning, this investigation explores the social construction of adult (over 30 years of age) L2 learners’ cognition in an ESOL setting. Specifically it aimed to answer the following research questions:  RQ 1. What are the prior language learning experiences of a group of adult migrant learners living in New Zealand?  RQ 2. How have these prior language learning experiences influenced the construction and development of their beliefs, assumptions, knowledge (BAK) about language learning?  RQ 3. What is their perceived need for English in their current socio-cultural context?  RQ 4. How do adult migrant language learners engage in language related activities beyond the classroom?  RQ 5. How can this language learning behaviour be reflected in a model of language learner cognition?  The study combined a longitudinal, ethnographic approach, with elements of narrative and case study inquiry. Six ‘recently arrived’ (Dunstan, Roz, & Shorland, 2004a) Colombian migrants (five refugees; one immigrant) were asked to talk about and discuss both prior and current experiences learning and using an L2. Through these lengthy in-depth, conversation-like interviews conducted in Spanish (the participants’ L1), told over time, a nuanced picture of the participants’ L2-related cognition emerged. As a result, I was able to more clearly observe the dynamic process in which a language learner’s mental life both impacts and is impacted on by language-related activity throughout their day-to day interactions. The participants are seen engaging in the L2 across a range of settings including at home, the doctor’s office, supermarkets and work. Moreover, in their accounts of this engagement we see change and revision (i.e. development) in their thinking about L2 learning and themselves as language learners, as well as their feelings toward the L2, other L2s and L2 users. A single participant was selected as an exemplary case to examine in detail, and facilitate understanding of this development. A case study approach allowed for a more intricate exploration of how the interplay between thought, emotion, and context impacted on the learner’s approaches to language-related activities. Issues regarding readiness to interact in the L2, intelligibility, language variety, and aversion to the ‘sound of English’ were seen as playing significant roles in the learner’s language development. This analysis resulted in the construction of a framework depicting language learner cognition in action. In terms of implications, this research supports the case for more qualitative research in SLA which centres learners’ perspectives of their L2 related experiences, particularly when so much of what seems to be affecting learning is the learners understanding of themselves and their actions. It also argues that studies in L2 cognition should focus their investigations on the developmental processes involved in the social construction of the mental factors which impact language learning and use. Finally, while belief studies in SLA are expanding the scope of their investigations – by looking to include more emotion and other affective factors, as well as by branching out into self-related constructs such as self-concept and self-efficacy in the foreign language domain – these studies remain limited in their almost microscopic view of learners’ mental lives. The picture of cognition I offer provides a more holistic understanding of this phenomenon which helps account at a macro-level for L2 behaviour. The study also highlights the potential and power of data gathering methods which foreground the participants’ voices and ideas (i.e. in-depth, unstructured interviews told over time) – reminding us that it is important when looking for what drives language learning behaviour to consider what the learners feel and think.</p>


2020 ◽  
Vol 61 (6) ◽  
pp. 22-29
Author(s):  
Hoang Nguyen . ◽  

Blasting is considered as one of the most effective methods for rock fragmentation in open - pit mines. However, its side effects are significant, especially blast - induced ground vibration. Therefore, this study aims to develop and apply artificial intelligence in predicting blast - induced ground vibration in open - pit mines. Indeed, the k - nearest neighbors (KNN) algorithm was taken into account and developed for predicting blast - induced ground vibration at the Deo Nai open - pit coal mine (Vietnam) as a case study. An empirical model (i.e., USBM) was also developed to compare with the developed KNN model aiming to highlight the advantage of the KNN model. Accordingly, 194 blasting events were collected and analyzed for this aim. This database was then divided into two parts, 80% for training and 20% for testing. The MinMax scale and 10 - fold cross - validation techniques were applied to improve the accuracy, as well as avoid overfitting of the KNN model. Root - mean - squared error (RMSE) and determination coefficient (R2) were used as the performance metrics for models’ evaluation and comparison purposes. The results indicated that the KNN model yielded better superior performance than those of the USBM empirical model with an RMSE of 1.157 and R2 of 0.967. In contrast, the USBM model only provided a weak performance with an RMSE of 4.205 and R2 of 0.416. With the obtained results, the KNN can be introduced as a potential artificial intelligence model for predicting and controlling blast - induced ground vibration in practical engineering, especially at the Deo Nai open - pit coal mine.


Author(s):  
Sobhan Sarkar ◽  
Sammangi Vinay ◽  
Chawki Djeddi ◽  
J. Maiti

AbstractClassifying or predicting occupational incidents using both structured and unstructured (text) data are an unexplored area of research. Unstructured texts, i.e., incident narratives are often unutilized or underutilized. Besides the explicit information, there exist a large amount of hidden information present in a dataset, which cannot be explored by the traditional machine learning (ML) algorithms. There is a scarcity of studies that reveal the use of deep neural networks (DNNs) in the domain of incident prediction, and its parameter optimization for achieving better prediction power. To address these issues, initially, key terms are extracted from the unstructured texts using LDA-based topic modeling. Then, these key terms are added with the predictor categories to form the feature vector, which is further processed for noise reduction and fed to the adaptive moment estimation (ADAM)-based DNN (i.e., ADNN) for classification, as ADAM is superior to GD, SGD, and RMSProp. To evaluate the effectiveness of our proposed method, a comparative study has been conducted using some state-of-the-arts on five benchmark datasets. Moreover, a case study of an integrated steel plant in India has been demonstrated for the validation of the proposed model. Experimental results reveal that ADNN produces superior performance than others in terms of accuracy. Therefore, the present study offers a robust methodological guide that enables us to handle the issues of unstructured data and hidden information for developing a predictive model.


2020 ◽  
Vol 10 (23) ◽  
pp. 8412
Author(s):  
Abdulla I. Almazrouee ◽  
Abdullah M. Almeshal ◽  
Abdulrahman S. Almutairi ◽  
Mohammad R. Alenezi ◽  
Saleh N. Alhajeri ◽  
...  

Electrical generation forecasting is essential for management and policymakers due to the crucial data provided for resource planning. This research employs the Prophet model with single and multiple regressors to forecast the electricity generation in Kuwait from 2020 to 2030. In addition, multiple seasonality Holt–Winters models were utilized as a benchmark for comparative analysis. The accuracy, generalization, and robustness of the models were assessed based on different statistical performance metrics. The triple seasonality Holt–Winters model achieved superior performance compared with the other models with R2 = 0.9899 and MAPE = 1.76%, followed by the double seasonality Holt–Winters model with R2 = 0.9893 and MAPE = 1.83%. Moreover, the Prophet model with multiple regressors was the third-best performing model with R2 = 0.9743 and MAPE = 2.77%. The forecasted annual generation in the year 2030 resulted in 92,535,555 kWh according to the best performing model. The study provides an outlook on the medium- and long-term electrical generation. Furthermore, the impact of fuel cost is investigated based on the five forecasting models to provide an insight for Kuwait’s policymakers.


Author(s):  
Iker Gondra

Genetic Algorithms (GA), which are based on the idea of optimizing by simulating the natural processes of evolution, have proven successful in solving complex problems that are not easily solved through conventional methods. This chapter introduces their major steps, operators, theoretical foundations, and problems. A parallel GA is an extension of the classical GA that takes advantage of a GA’s inherent parallelism to improve its time performance and reduce the likelihood of premature convergence. An overview of different models for parallelizing GAs is presented along with a discussion of their main advantages and disadvantages. A case study: A parallel GA for finding Ramsey Numbers is then presented. According to Ramsey Theory, a sufficiently large system (no matter how random) will always contain highly organized subsystems. The role of Ramsey numbers is to quantify some of these existential theorems. Finding Ramsey numbers has proven to be a very difficult task that has led researchers to experiment with different methods of accomplishing this task. The objective of the case study is both to illustrate the typical process of GA development and to verify the superior performance of parallel GAs in solving some of the problems (e.g., premature convergence) of traditional GAs.


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