Neurocomputing has been adjusted effectively in time series forecasting activities, yet the vicinity of exceptions that frequently happens in time arrangement information might contaminate the system preparing information. This is because of its capacity to naturally realise any example without earlier suspicions and loss of sweeping statement. In principle, the most widely recognised calculation for preparing the system is the backpropagation (BP) calculation, which inclines toward minimisation of standard slightest squares (OLS) estimator, particularly the mean squared mistake (MSE). Regardless, this calculation is not by any stretch of the imagination strong when the exceptions are available, and it might prompt bogus expectation of future qualities. In this paper, we exhibit another calculation which controls the firefly algorithm of least median squares (FFA-LMedS) estimator for neural system nonlinear autoregressive moving average (ANN-NARMA) model enhancement to provide betterment for the peripheral issue in time arrangement information. Moreover, execution of the solidified model in correlation with another hearty ANN-NARMA models, utilising M-estimators, Iterative LMedS and Particle Swarm Optimisation on LMedS (PSO-LMedS) with root mean squared blunder (RMSE) qualities, is highlighted in this paper. In the interim, the actual monthly information of Malaysian Aggregate, Sand and Roof Materials value was taken from January 1980 to December 2012 (base year 1980=100) with various levels of anomaly issues. It was found that the robustified ANN-NARMA model utilising FFA-LMedS delivered the best results, with the RMSE values having almost no mistakes at all in all the preparation, testing and acceptance sets for every single distinctive variable. Findings of the studies are hoped to assist the regarded powers including the PFI development tasks to overcome cost overwhelms.
Kedatangan spesies burung hijrah ke Hutan Paya Laut Matang, Perak merupakan suatu fenomena unik dan amat berpotensi untuk dikomersialkan. Namun begitu sejak sepuluh tahun kebelakangan ini kedatangan spesies burung hijrah ke kawasan ini mencatatkan penurunan yang membimbangkan. Pembangunan di sekitar kawasan Hutan Paya Laut Matang dikenal pasti menjadi faktor utama penurunan ini. Selain itu, pengurusan dan pemantauan terhadap spesies burung hijrah yang tidak bersistematik juga menyumbang kepada permasalahan ini. Kaedah pengurusan dan pemantauan burung hijrah secara konvensional masih diamalkan oleh Jabatan Hidupan Liar dan Taman Negara (PERHILITAN) dalam merekod data dan maklumat yang diperoleh. Oleh itu kajian ini dilakukan dengan memperkenalkan pendekatan teknologi Sistem Maklumat Geografi (GIS) dan Penderiaan Jauh sebagai aplikasi utama yang dapat membantu dalam memperbaiki pengurusan dan pemantauan burung hijrah. Aplikasi GIS digunakan dalam memetakan data parameter kajian yang diperoleh menggunakan perisian yang bersesuaian iaitu ArcGIS 10.1 dan Penderiaan Jauh digunakan untuk memodelkan parameter kajian menggunakan aplikasi dalam perisian ERDAS Imagine 8.5. Sebanyak enam parameter kajian digunakan dalam kajian ini iaitu suhu, taburan hujan, makanan, ketinggian, gunatanah dan liputan tanah. Pemodelan setiap parameter kajian ini membolehkan kesan setiap parameter kajian terhadap taburan burung hijrah dalam suatu kawasan ditunjukkan dalam bentuk pemetaan. Hasil pemodelan ini juga membolehkan parameter yang terpenting dan paling mempengaruhi taburan burung hijrah bagi kawasan Hutan Paya Laut Matang dikenal pasti. Maklumat ini membolehkan DWNP lebih memahami keperluan burung hijrah dalam pemilihan habitat mereka. Selain itu, hasil pemodelan ini membantu DWNP dalam mengatur strategi pelaksanaan program pemeliharaan dan pemuliharaan di kawasan sekitar Hutan Paya Laut Matang bagi memastikan kedatangan spesies burung hijrah secara berterusan.
This paper examines the concept of free will, or independent action, in light of recent research in psychology and neuroscience. Reviewing findings in memory, prospection, and mental simulation, as well as the neurological mechanisms underlying behavioral control, planning, and integration, it is suggested in accord with previous arguments (e.g., Wegner, 2003; Harris, 2012) that a folk conception of free will as entirely conscious control over behavior should be rejected. However, it is argued that, when taken together, these findings can also support an alternative conception of free will. The constructive nature of memory and an integrative "default network" provide the means for novel and creative combinations of information, such as the imagining of counterfactual scenarios and alternative courses of action. Considering recent findings of extensive functional connections between these systems and those that subsume motor control and goal maintenance, it is argued that individuals have the capability of producing novel ideas and translating them into actionable goals. Although most of these processes take place beneath conscious awareness, it is argued that they are unique to the individual and thus, can be considered a form of independent control over behavior, or free will.
This paper has evaluated the spatial growth of Bauchi Metropolis from 1976 to 2015
through the application of Remote Sensing and GIS techniques. Various satellite
imageries of the metropolis (Landsat MSS of 1976, TM of 1986, 1996 and ETM+ of 2006
and 2015) were integrated; processed and classified using ERDAS imagine 9.1. The
results showed an increase in area from 11.68km2
in 1976 to 12.51km2
in 1986 to
32.44km2 in 1996, to 49.66km2
in 2006 and finally to 89.23km2 in 2015. It is
recommended that government should provide the required capacities for the use of
Remote Sensing and GIS in planning for the growth of the town.
Storytelling is considered as an interactive social arts that uses word and
gestures to reveal the elements and images of a story while engaging the
listener's imagination. Multimedia based digital storytelling learning
approach provides interesting, interactive, engaging and multisensory
learning experience to children. Children explore new experience and
scenarios as new stories are being told. This study concentrates on
determining the best combination of elements for designing effective digital
storytelling applications specifically for the usage of dyslexic children.
Dyslexic children are known to have a common learning difficulty that can
cause problems with reading, writing, spelling and comprehension. These
applications are design with the objective to help in improving dyslexic
children ability in readings and comprehensions. Four elements were
derived from extensive literature studies. The elements are multimedia
components, multi-sensory instructional approach, emotional design and
games design. The relationship among all the elements were determine
and described in details as it will be used to contribute to the design and
development of the application in further works. The strength of this study
is it models the combinations of technology, psychology and instructional
approach as a support components for developing an effective digital story
telling learning application for dyslexic children.
Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.
The main goal of the present research is to examine socio-ecological hypothesis on apology and compensation. Specifically, we conducted four studies to test the idea that an apology is an effective means to induce reconciliation in a residentially stable community, whereas compensation is an effective means in a residentially mobile community. In Studies 1, 2a, and 2b, American and Japanese participants (national difference in mobility; Study 1) or non-movers and movers (within-nation difference in mobility; Studies 2a and 2b) imagined the situations in which they were hurt by their friends and rated to what extent they would be willing to maintain their friendships upon receipt of apology or compensation. The results showed that compensation was more effective in appeasing residentially mobile people (i.e., Americans and movers) than stable people (i.e., Japanese and non-movers), while apology was slightly more effective appeasing residentially stable people than residentially mobile people (significant in Study 1; not significant in Studies 2a and 2b). In Study 3, by conducting an economics game experiment, we directly tested the hypothesis that mobility would impair the effectiveness of apology and enhance the effectiveness of compensation. The results again partially supported our hypothesis: In the high mobility condition, compensation increased one's willingness to continue the relationship with the offender, when compared to willingness in the low mobility condition. The importance of socio-ecological perspective on the forgiveness literature is discussed.
Stroke is the second foremost cause of death worldwide and is one of the most common causes of disability. Several approaches have been proposed to manage stroke patient rehabilitation such as robotic devices and virtual reality systems, and researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. Therefore, the most challenging tasks with BCI applications involve identifying the best technique(s) that can reveal the neuron stimulus information from the patients' brains and extracting the most effective features from these signals as well. Accordingly, the main novelty of this paper is twofold: propose a new feature fusion method for motor imagery (MI)-based BCI and develop an automatic MI framework to detect the changes pre- and post-rehabilitation. This study investigated the electroencephalography (EEG) dataset from post-stroke patients with upper extremity hemiparesis. All patients performed 25 MI-based BCI sessions with follow up assessment visits to examine the functional changes before and after EEG neurorehabilitation. In the first stage, conventional filters and automatic independent component analysis with wavelet transform (AICA-WT) denoising technique were used. Next, attributes from time, entropy and frequency domains were computed, and the effective features were combined into time-entropy-frequency (TEF) attributes. Consequently, the AICA-WT and the TEF fusion set were utilised to develop an AICA-WT-TEF framework. Then, support vector machine (SVM), k-nearest neighbours (kNN) and random forest (RF) classification technique were tested for MI-based BCI rehabilitation. The proposed AICA-WT-TEF framework with RF classifier achieves the best results compared with other classifiers. Finally, the proposed framework and feature fusion set achieve a significant performance in terms of accuracy measures compared to the state-of-the-art. Therefore, the proposed methods could be crucial for improving the process of automatic MI rehabilitation and are recommended for implementation in real-time applications.
Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic (EEG) sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.
Neurofeedback (NFB) allows subjects to learn self-regulation of neuronal brain activation based on information about the ongoing activation. The implementation of real-time functional magnetic resonance imaging (rt-fMRI) for NFB training now facilitates the investigation into underlying processes. Our study involved 16 control and 16 training right-handed subjects, the latter performing an extensive rt-fMRI NFB training using motor imagery. A previous analysis focused on the targeted primary somato-motor cortex (SMC). The present study extends the analysis to the supplementary motor area (SMA), the next higher brain area within the hierarchy of the motor system. We also examined transfer-related functional connectivity using a whole-volume psycho-physiological interaction (PPI) analysis to reveal brain areas associated with learning. The ROI analysis of the pre- and post-training fMRI data for motor imagery without NFB (transfer) resulted in a significant training-specific increase in the SMA. It could also be shown that the contralateral SMA exhibited a larger increase than the ipsilateral SMA in the training and the transfer runs, and that the right-hand training elicited a larger increase in the transfer runs than the left-hand training. The PPI analysis revealed a training-specific increase in transfer-related functional connectivity between the left SMA and frontal areas as well as the anterior midcingulate cortex (aMCC) for right- and left-hand trainings. Moreover, the transfer success was related with training-specific increase in functional connectivity between the left SMA and the target area SMC. Our study demonstrates that NFB training increases functional connectivity with non-targeted brain areas. These are associated with the training strategy (i.e., SMA) as well as with learning the NFB skill (i.e., aMCC and frontal areas). This detailed description of both the system to be trained and the areas involved in learning can provide valuable information for further optimization of NFB trainings.
Beneficial effects of music on several performance-related aspects of sport have been reported, but the processes involved are not well understood. The purpose of the present study was to investigate effects of relaxing and arousing classical music on physiological indicators and subjective perceptions of arousal during imagery of a sport task. First, appropriate music excerpts were selected. Then, 12 skilled shooters performed shooting imagery while listening to the three preselected music excerpts in randomized order. Participants' galvanic skin response, peripheral temperature, and electromyography were monitored during music played concurrently with imagery. Subjective music ratings and physiological measures showed, as hypothesized, that unfamiliar relaxing music was the most relaxing and unfamiliar arousing music was the most arousing. Researchers should examine the impact of unfamiliar relaxing and arousing music played during imagery on subsequent performance in diverse sports. Practitioners can apply unfamiliar relaxing and arousing music with imagery to manipulate arousal level.
Stereotyping is a pervasive societal problem that impacts not only minority groups but subserves individuals who perpetuate stereotypes, leading to greater distance between groups. Social contact interventions have been shown to reduce prejudice and stereotyping, but optimal contact conditions between groups are often out of reach in day to day life. Therefore, we investigated the effects of a synchronous walking intervention, a non-verbal embodied approach to intergroup contact that may reduce the need for optimal contact conditions. We studied attitude change towards the Roma group in Hungary following actual and imagined walking, both in a coordinated and uncoordinated manner. Results showed that coordinated walking, both imagined and in vivo, led to explicit and implicit reductions in prejudice and stereotyping towards both the Roma individual and the wider Roma social group. This suggests that coordinated movement could be a valuable addition to current approaches towards prejudice reduction.
Building on two decades as a private health professional university, the International Medical University prepares for the third decade, taking stock of the challenges in changing epidemiology and pattern of disease, changing demography and healthcare, as well as explosion in knowledge and information technology. The Global Independent Commission1 provided a framework for instructional and institutional reforms, and the IMU will use its 3 I’s (insight, imagination & innovation) in adopting these measures. Some of the instructional reforms are already in place, others need to be further nurtured and promoted. In its third decade, competency based curriculum, inter-professional learning, IT, global collaboration, educational resources, new professionalism and emphasis on quality improvement will help ensure IMU train and produce competent, caring and ethical health professionals fit to tackle 21st century challenges.
This paper presents a case study of a 22-year-old female client who came for counselling sessions for the purpose of overcoming her pedaphobia. Symptoms, such as, dizziness, nausea, arousal, sweating palm and body shaking were noticed to be visible not only by the presence of a real child, but also by pictures, stories and imaginations of infants. Systematic desensitization technique, a form of Exposure Therapy, was applied to treat the client. It involved relaxation and breathing techniques, and supported by the application of live modelling guided participation. Along with the interventions, Rational Emotive Behaviour Therapy techniques were also applied, such as, thought recording, irrational thoughts identification and disputation, Rational Emotive Imagery (REI), coping self-statement, and forceful self-statement. By the end of the therapy, the client reported to have experienced a decline in the occurrences of symptoms and demonstrated the ability to overcome her phobia.
Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.
Malaysian high school students, 142 boys and 154 girls (M age= 13.3 yr., SD = 0.3) were compared on a talent measure, the Khatena-Morse Multitalent Perception Inventory. Boys obtained significantly higher means on the overall score of Versatility and the talent areas of Artistry, Creative Imagination, Initiative, and Leadership. Further replications involving other age groups and nationalities are recommended.
PURPOSE:
Internet social media offers a rich source for soliciting the public's views on health issues. This qualitative research, using You-Tube as a platform, aimed to explore the public's perspectives on management of dental fear and anxiety (DFA) in pediatric patients.
METHODS:
Using three keywords ("dental fear," "dental phobia," and "dental anxiety"), YouTube videos were searched. Twenty-seven videos related to DFA in children and adolescents were reviewed by three investigators, including a nondental layperson. Inductive thematic analysis was adopted for interpreting the data.
RESULTS:
Several strategies were considered useful for controlling DFA in pediatric patients, including: verbal and nonverbal communication to establish closeness and effective guidance (explanation, permission-seeking, reassurance, and negotiation); desensitization to dental settings and procedures; tell-show-do; positive reinforcement; distraction by imagination and thoughtful designs of clinic; and parental presence and support. Some self-coping strategies adopted by patients alleviated their DFA, such as self-reasoning and trust-building through long-term connection. Dentists' clinical competence, favorable treatment outcomes, and state-of-the-art devices and technologies (dental lasers, intraoral camera, and adapted anaesthesia method) contributed to reducing DFA.
CONCLUSIONS:
Authentic testimonials in YouTube videos endorsed and interpreted a variety of strategies adoptable by patients, parents, and dental professionals for managing children's and adolescents' dental fears and anxieties.