Pengaruh motivasi dan kesannya terhadap prestasi akademik: tinjauan terhadap pelajar Sarjana Muda Kejuruteraan Mekanikal Sesi 1999/2000 KUiTTHO

Laporan Projek Sarjana ini mempersembahkan hasil kajian yang bertajuk 'PENGARUH MOTIVASI DAN KESANNYA TERHADAP PRESTASI AKADEMIK'. Kajian ini bertujuan untuk mengenalpasti hubungan faktor-faktor yang signifikan dalam penentuan prestasi akademik pelajar (faktor dalaman, luaran dan persekitaran) dengan prestasi akademik yang diukur melalui Purata Markah Keseluruhan atau CGPA. Sampel kajian adalah seramai 60 orang pelajar Saijana Muda Kejuruteraan Mekanikal sesi 1999/2000 KUiTTHO. Kajian adalah berbentuk tinjauan yang menggunakan sejenis instrumen kajian dalam mendapatkan data iaitu borang soal selidik. Kesemua data dianalisis dan dikemukakan dalam bentuk analisis statistik secara deskriptif dan secara inferensi. Korelasi Pearson digunakan untuk melihat hubungan antara setiap pembolehubah. Terdapat tiga faktor utama yang dikaji iaitu faktor dalaman(min=3.6), faktor luaran(min=3.7) dan faktor persekitaran (min=2.9). Hasil kajian menunjukkan bahawa ketiga-tiga tiga faktor tersebut mempunyai hubungan yang positif dengan prestasi akademik. Faktor dalaman yang paling memberi hubungan yang signifikan dalam prestasi akademik dengan 0.795, faktor luaran 0.650 dan faktor persekitaran 0. 339. Di akhir kajian ini, pengkaji mencadangkan agar (i) Mengadakan banyak Kem Motivasi, (ii) Peningkatan cara pengajaran pensyarah, (iii) Penyediaan peralatan pembelajaran yang mencukupi, (iv) Sumber rujukan seperti buku dan majalah di Perpustakaan mesti mencukupi

Computational Modelling of Spatio-Temporal EEG Brain Data with Spiking Neural Networks

The research presented in this thesis is aimed at modelling, classification and understanding of functional changes in brain activity that forewarn of the onset and/or the progression of a neurodegenerative process that may result in a number of disorders, including cognitive impairments, opiate addiction, Epilepsy and Alzheimer’s Disease. The study of neural plasticity and disease onset have been the centre of attention for researchers; especially as the population is ageing there is a need to deal with the increase in cognitive decline and the early onset of neurological diseases. As a consequence, large amounts of brain data has been collected and even more is expected to be collected, by means of novel computational techniques and biochemistry measurements. However, brain data is difficult to analyse and understand, especially since many of the traditional statistical and AI techniques are not able to deal with it appropriately. Driven by these issues and aiming to achieve the proposed goals, this study undertook to explore the potential of an evolving spatio-temporal data processing machine called the NeuCube architecture of spiking neurons, to analyse, classify and extract knowledge from electroencephalography spatio-temporal brain data. Firstly, the research undertaken in this thesis proposes a biologically plausible spiking neural network methodology for electroencephalography data classification and analysis. Secondly, it proposes a methodology for understanding functional changes in brain activity generated by the spatio-temporal data in the spiking neural network model. Thirdly, a new unsupervised learning rule is proposed for the investigation of the biological processes responsible for brain synaptic activity with the aim of targeting pharmacological treatments. The research undertaken achieved the following: high accuracy classification of electroencephalography data, even when fewer EEG channels and/or unprocessed data was used; personalised prognosis and early prediction of neurological events; the development of a tool for visualization and analysis of connectivity and spiking activity generated in the computational model; a better understanding of the impact of different drug doses on brain activity; a better understanding of specific neurological events by revealing the area of the brain where they occurred; and the analysis of the impact of biochemical processes on the neuronal synaptic plasticity of the model. Further improvement of the understanding and use of the proposed methodologies would contribute to the advancement of research in the area of prediction of neurological events and understanding of brain data related to neurological disorders, such as Alzheimer’s Disease

Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment

The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects. This analysis is based on the recently proposed NeuCube spiking neural network (SNN) framework and more specifically on the analysis of the connectivity of a NeuCube model trained with electroencephalography (EEG) data. The case study data used to illustrate this method is EEG data collected from three groups-subjects with opiate addiction, patients undertaking methadone maintenance treatment, and non-drug users/healthy control group. The proposed method classifies more accurately the EEG data than traditional statistical and artificial intelligence (AI) methods and can be used to predict response to treatment and dose-related drug effect. But more importantly, the method can be used to compare functional brain activities of different subjects and the changes of these activities as a result of treatment, which is a step towards a better understanding of both the EEG data and the brain processes that generated it. The method can also be used for a wide range of applications, such as a better understanding of disease progression or aging

acellular dermal matrix and heel reconstruction a new prospective

BackgroundHeel reconstruction represents a challenge for all plastic surgeons due to the anatomical and functional features of this weight-bearing area. In the last decade a combined use of acellul..

Cathodal tDCS of the parietal cortex combined with mirror therapy improved hand dexterity in a case of focal dystonia

Background and aims. The role of not-invasive brain stimulation (NIBS) as an add-on treatment to motor training in children suffering from focal dystonia has described in the literature with contrasting results. The study is aimed at describing the clinical and functional outcome observed in a 13-years-old girl suffering from hand dystonia and undergone NIBS combined with mirror therapy. Methods. We report the case of a 13-years-old girl suffering from hand dystonia following right hemisphere lesion in the basal ganglia area, due to cerebrovascular accident occurred in the infancy. At the basal assessment she showed a complete muscle strength recovery, a quite normal gait pattern, but a complete impairment in left hand dexterity due to hand muscle dystonia. She had already undergone several training protocols, including splint wearing and repeated botulinum toxin injections. These last induced a partial resolution of muscle contraction at rest, without any improvement in hand dexterity. We proposed the following treatment protocol: daily sessions (20 minute each) of cathodal tDCS on the right sensorimotor cortex (P4), 1 mA, followed, in the same morning, by 20minutes mirror therapy, for five consecutive days. Functional status was assessed using the Fugl-Meyer upper limb score at baseline (T0), after treatment end (T1) and one month later (T2). Moreover, a fMRI was performed at T0 and T1, in order to look what brain networks were activated during the left and right limb movements. Results. The NIBS was well tolerated. No adverse events were complained for. The Fugl-Meyer score increased from 21/66 (T0) to 29/66 (T1) and up to 30/66 (T2). The fMRI showed a significant reduction of brain activation under active left limb movement after treatment. Conclusions. Parietal cortex inhibition via cathodal tDCS at the lesioned hemisphere was effective at reducing dystonia, improving voluntary movement and inducing the reorganization of brain networks

A fuzzy logic system for the home assessment of freezing of gait in subjects with Parkinsons disease

Gait dysfunctions are pathognomonic, progressive and, generally, continuous in Parkinson’s Disease (PD). The Freezing of Gait (FoG) is an episodic gait disorder involving up to 70% of people with PD, within 10 years of clinical onset, and associated with an increased risk for falls and immobility, which in turn, contributes to greater disability. Automatic and objective monitoring of FoG may help clinicians to understand and treat this phenomenon. In this work, a smartphone app for real-time FoG detection is presented and tested both in a laboratory setting and at patients’ home. The app implements a novel fuzzy logic algorithm that uses important spatio-temporal parameters of gait and is built according to clinical knowledge about FoG. The app includes a gait detection function and the evaluation of two important clinical statistics, i.e. FoG time and FoG number. The app FoG detection performance was assessed against clinicians evaluation and compared with the Moore-Bachlin FoG detection algorithm through ROC analysis, the calculation of confusion matrix, and FoG hit rate. The proposed algorithm achieved better results with respect to the Moore-Bachlin algorithm. Home reports were compared with respect to the FoG Questionnaire and laboratory reports; results indicated significant correlations for both FoG time and FoG number. The results confirm the reliability and accuracy of this app for FoG detection, supporting its wide use for diagnostic and therapeutic purposes

Clinical and Functional Evolution in Subjects with Parkinson’s Disease during SARS-CoV-2 Pandemic

The COVID-19 pandemic has been a stress test for the population, especially for people with chronic disorders such as Parkinson’s disease (PD). In addition to public health restrictions that contrast with PD management recommendations, there were deep changes in health care delivery. This retrospective study evaluates the impact of COVID-19 on the clinical and functional evolution of a cohort of 221 PD patients consecutively referred to the Movement Disorders Center between 2018 and 2021. We analyzed the trend in motor and non-motor symptoms and functional status across years based on the Unified Parkinson’s Disease Rating Scale (UPDRS) and Non-Motor Symptom Scale (NMSS). We also compared the number of emerging complications, neurologic visits, and rehabilitation sessions per subject per year. In 2020, all primary endpoint measures worsened compared to 2019, without age, disease duration, or greater neurologic impairment explaining this outcome. Concurrently, the percentage of patients receiving neurologic visits or rehabilitation sessions reduced by 53% and 58%, respectively. The subgroup analysis of 167 subjects revealed that those who received at least one cycle of rehabilitation sessions in 2020 maintained their independence level. These findings lead to emphasizing the importance of regular monitoring and rehabilitation delivery in people with chronic neurological disorders

Clinical and Functional Evolution in Subjects with Parkinson’s Disease during SARS-CoV-2 Pandemic

The COVID-19 pandemic has been a stress test for the population, especially for people with chronic disorders such as Parkinson’s disease (PD). In addition to public health restrictions that contrast with PD management recommendations, there were deep changes in health care delivery. This retrospective study evaluates the impact of COVID-19 on the clinical and functional evolution of a cohort of 221 PD patients consecutively referred to the Movement Disorders Center between 2018 and 2021. We analyzed the trend in motor and non-motor symptoms and functional status across years based on the Unified Parkinson’s Disease Rating Scale (UPDRS) and Non-Motor Symptom Scale (NMSS). We also compared the number of emerging complications, neurologic visits, and rehabilitation sessions per subject per year. In 2020, all primary endpoint measures worsened compared to 2019, without age, disease duration, or greater neurologic impairment explaining this outcome. Concurrently, the percentage of patients receiving neurologic visits or rehabilitation sessions reduced by 53% and 58%, respectively. The subgroup analysis of 167 subjects revealed that those who received at least one cycle of rehabilitation sessions in 2020 maintained their independence level. These findings lead to emphasizing the importance of regular monitoring and rehabilitation delivery in people with chronic neurological disorders