Challanges of building information modelling (BIM) in project implementation

Building Information Modelling (BIM) is a technology that is currently gaining momentum within the construction industry as interoperability issue is become more and more important in relative to the quality and productivity of the industry. BIM is defined as a modelling technology and associated set of processes to produce, communicate, and analyze building models throughout the entire project's lifecycle. Although there is bound of benefits that gained from the BIM application, the local construction industry still reluctant to deploy the technology in delivery its services. The objectives of the study is to identify the types of challenges and also investigate the effect of challenges to the outcome of BIM if BIM has being adopted in the local construction industry. The survey questionnaires were distributed in the construction field within Kuantan region. The method of data collection is by questionnaire and also interview. The main conclusion drawn from the study are that the high level of ICT usage among the construction professionals has make the industry more readily in emerging BIM and the identified barriers can confined into three main categories: people, technology and process. Furthermore, the research has identified the potential factors that driven the adoption of BIM and also the consequences of mandating BIM adoption in the local industry

Recognition of Facial Action Unit Based on Spatial-Temporal Bayesian Probabilistic Technique

Pengecaman Unit Tindakan muka sering digunakan sebagai kerja-kerja asas untuk mengkaji ekspresi wajah atau aplikasi pergerakan manusia seperti pemantauan video dan pengenalan muka. Unit Tindakan (AUs) bekerja sebagai unit asas dalam Sistem Kod Tindakan Muka (FACS) untuk taksonomi pergerakan muka, yang mengaitkan setiap AU dengan pengaktifan satu atau lebih otot muka khusus. Pembinaan sistem pengecaman Unit Tindakan yang stabil tetap menjadi cabaran bagi penyelidik disebabkan aksi, pencahayaan dan gabungan rumit ekspresi wajah. Sehubungan dengan itu, kerja penyelidikan ini mencadangkan satu pendekatan kebarangkalian yang model hubungan statik dan tempoh antara AUs dari urutan imej menggunakan Dynamic Bayesian Network (DBN). DBN yang menggabungkan imej ukuran kepada model DBN direka untuk menjadi satu struktur umum untuk hubungan AU. Support Vector Machine (SVM) digunakan untuk mendapatkan ukuran AU dari pangkalan data dengan mengklasifikasikan setiap AU daripada ciri imej. Ukuran AU tersebut kemudian digunakan sebagai bukti kepada DBN untuk membuat kesimpulan kewujudan pelbagai AU. Kemuncak penyelidikan ini adalah bahawa parameter AU dalam model DBN dipelajari daripada kaedah data tidak lengkap, dengan nod AU pembolehubah tersembunyi dan disimpulkan daripada ukuran imej secara langsung dan dimodelkan dengan cara kebarangkalian yang dinamik. Kerja penyelidikan ini mencadangkan bahawa setiap AU mempunyai keputusan ambang berbeza kerana sambungan yang berbeza daripada AU dalam model dengan mencari ambang yang terbaik bagi setiap AU. Keputusan eksperimen menunjukkan bahawa dengan membuat kesimpulan AU dari ukuran imej sebagai model terdahulu, model yang dicadangkan mencapai keputusan yang setanding dengan model yang dipelajari sepenuhnya daripada pangkalan data tertentu. Sistem ini mencapai kadar pengiktirafan purata sebanyak 94.78% dengan kadar positif benar sebanyak 70.54% dan kadar penggera palsu 2.31% menggunakan pangkalan data Cohn- Kanade (CK). Pendekatan kebarangkalian yang dicadangkan itu juga telah digunakan untuk cabaran Pengiktirafan Ekspresi Wajah dan Analisis (FERA) yang dianjurkan oleh Pemprosesan Isyarat Rangkaian Sosial (SSPNET) pada tahun 2011. Cabaran ini bertujuan untuk membolehkan perbandingan yang adil di antara sistem dengan mempunyai keperluan untuk prosedur penilaian yang seragam. Cabaran ini digunakan sebagai penanda aras sistem ekspresi wajah di seluruh dunia. Pendekatan kebarangkalian yang dicadangkan itu telah direka bentuk semula dengan mengikut arahan yang diberikan oleh cabaran dan model baru dibina dan dilatih untuk cabaran FERA. Sistem yang dicadangkan mencapai prestasi lebih baik daripada kaedah asas dalam cabaran dan ia telah menunjukkan hasil yang setanding dengan keadaan-keadaan lain dan peserta dalam cabaran tersebut. Metrik prestasi yang digunakan di FERA ialah ukuran F1 dan keputusan keseluruhan mencapai ukuran F1 pada 0.494 mengatasi kerja-kerja lain termasuk satu-satunya pasukan yang menggunakan pendekatan kebarangkalian dalam kerja mereka. Oleh itu, sistem yang dicadangkan telah memenuhi objektif kajian dengan pembelajaran parameter dari kaedah data tidak lengkap, umum kepada pangkalan data yang berbeza serta keadaan yang berbeza untuk bersaing dengan kerja-kerja lain di dunia. _________________________________________________________________________________________________________________________ Facial Action Unit recognition is often used as elementary works for facial expressions analysis or human motions applications such as video surveillance and face identification. Action Units (AUs) are employed as basic unit in Facial Action Coding System (FACS) to taxonomize facial movements; by associating each AUs with the activation of one or more specific facial muscles. A stable Action Unit recognition system still remains a challenge for researchers due to pose, illuminations and complicated combination of facial expression. With this regards, this research work proposes a probabilistic approach which models spatial and temporal relationships of AUs from image sequence using Dynamic Bayesian Network (DBN). The state-ofthe- art DBN, which incorporates AU measurements from images to a DBN model is designed to be a generic structure for AU relationships. Support Vector Machine (SVM) is used to obtain AU measurements from database by classifying each AUs from image features. Such AU measurements are then applied as evidence to the DBN for inferring existence of various AUs. The highlight of this work is that AU parameters in DBN model are learned from incomplete data method, where the AU nodes are hidden variables and directly inferred from image measurements and modeled in dynamic and probabilistic way. This research work proposed that each AUs has different decision threshold due to different connections of AUs in the model by searching the best threshold for each AUs. Experimental results show that by inferring AU from image measurements, the proposed model achieves comparable results to the model that learned completely from specific database. This system achieves average recognition rate at 94.78% with a true positive rate of 70.54% and false alarm rate of 2.31% using Cohn-Kanade (CK) database. The proposed probabilistic approach has also been applied to the Facial Expression Recognition and Analysis (FERA) challenge which was hosted by the Social Signal Processing Network (SSPNET) in 2011. The challenge aims to allow a fair comparison between systems, by having a need for standardized evaluation procedures. This challenge is used as the benchmark of facial expression system around the world. The proposed probabilistic approach has been redesigned to follow the instructions given by the challenge and a new model is built and trained for FERA challenge. The proposed probalisitic approach is proven to be applicable and generalized to different conditions. The proposed system is compared against the baseline system for the challenge provided by the FERA organizers. The proposed system achieved better performance than the baseline system and achieved comparable results with other state-of-the-art and participants in the challenge. The performance metric used in FERA is F1-measure and the overall result achieves 0.494 for F1-measure, outperforming other works including the one and only team which use probabilistic approach in their work. Hence, the proposed system has met the objectives of research by learning parameters from incomplete data method, generalized to different database as well as different conditions to compete with other works in the world

The Contribution of the Neighbourhood Environment to the Relationship Between Neighbourhood Disadvantage and Physical Function Among Middle-Aged to Older Adults

Background With the continuing increases in life expectancies in developed countries, an important public health goal is to ensure successful ageing—morbidity compression, maintenance of physical functioning and active engagement in life. It is well established that the onset of physical function decline begins in mid-life, and functional capacity is critical to maintaining mobility, independence and quality of life. A growing body of literature has found that residents of more disadvantaged neighbourhoods have significantly poorer physical function, independent of individual-level factors. However, the mechanisms through which neighbourhood environments are associated with this relationship remain largely unknown. The overarching aim of this thesis was to investigate the contributions of the neighbourhood environment to the relationship between neighbourhood disadvantage and physical function among middle-aged to older adults: this was accomplished in three studies. First, I examined the relationship between neighbourhood disadvantage and physical function in the Australian context (Study One). Second, I investigated if this relationship is explained by neighbourhood-level perceptions of safety from crime and walking for recreation (Study Two). Third, I examined the contribution of neighbourhood walkability and walking for transport to the relationship between neighbourhood disadvantage and physical function (Study Three). Methods This program of research utilized secondary data from the How Areas in Brisbane Influence HealTh and AcTivity (HABITAT) study. HABITAT is a multilevel longitudinal study underpinned by a social ecological framework. It was conducted in Brisbane among adults aged 45-70 years living in 200 neighbourhoods. HABITAT commenced in 2007 and had subsequent data collection waves in 2009, 2011, 2013 and 2016. For this thesis, the 2013 data were utilised as physical function was first collected in 2013 (n= 6,520). The measure of neighbourhood disadvantage was derived from the Australian Bureau of Statistics’ (ABS) Index of Relative Socioeconomic Disadvantage (IRSD) scores. Physical function was measured using the Physical Function Scale (0 – 100), a component of the Short Form-36 Health Survey, with higher scores indicating better function. In Study Two, participants self-reported their perceptions of safety from crime using items from the Neighbourhood Environment Walkability Scale (NEWS) questionnaire, which were subsequently aggregated to the neighbourhood-level. Walking for recreation (minutes per week) was self-reported by participants. In Study Three, neighbourhood walkability measures (street connectivity, dwelling density and land use mix) was objectively measured and provided by the Brisbane City Council (the local government authority responsible for the jurisdiction covered by the HABITAT study). Walking for transport (minutes per week) was self-reported by participants. The data were analysed using multilevel regression models (linear, binomial or multinomial). In instances where multilevel categorical models are undertaken, Markov chain Monte Carlo (MCMC) simulation will be employed to estimate odds ratio and 95% credible intervals. All data were prepared in STATA SE 13 and analyses were conducted using MLwiN version 2.35. Results Findings from Study One found that residents of more disadvantaged neighbourhoods had significantly poorer physical function. These associations remained significant after adjustment for individual-level socioeconomic position (SEP). Moving forward from the descriptive findings, Study Two found that neighbourhood-level perceptions of safety from crime and walking for recreation partly explained (24% in men and 25% in women) neighbourhood differences in physical function. In Study Three, I found that neighbourhood walkability and walking for transport did not explain the relationship between neighbourhood disadvantage and physical function. Conclusion Given the growing proportion of the ageing population in Australia and the resultant increasing pressure on neighbourhood and city infrastructure in Australia, it is important to understand the contributions of the neighbourhood environment in the relationship between neighbourhood disadvantage and physical function. Despite the complexity in understanding neighbourhood socioeconomic differences in physical function, the findings of this thesis suggest that the neighbourhood in which we live is important to physical function. To reduce neighbourhood inequalities in physical function, attention needs to be given to improve the perceptions of safety from crime in more disadvantaged neighbourhoods to encourage more walking for recreation. Living in a walkable neighbourhood is important to support more walking for transport, but may not be sufficient to reduce neighbourhood inequalities in physical function. A multi-faceted intervention is needed to create a healthy, liveable and equitable community for successful ageing

Template-Assisted Synthesis and Study of One-Dimensional Nanostructures Array

Ph.DDOCTOR OF PHILOSOPH

The sense of No Ending: The Post/Modern Apocalypse in Shojo Manga of the 1990s.

Master'sMASTER OF ART

The prevalence and risk factors of ventilator-associated pneumonia in intensive care units in Hospital Sultanah Bahiyah Kedah Malaysia

Background: Ventilator associated pneumonia (VAP) is the commonest nosocomial infection in intensive care unit (ICU). We conducted a first study to collect local data on prevalence and risk factors of VAP in Hospital Sultanah Bahiyah (HSB), Kedah. Methodology: This prospective cohort surveillance was conducted on patients admitted to an adult medical-surgical ICU of a tertiary hospital from 1st August 2104 to 31st July 2105. VAP was diagnosed using Malaysia Registry of ICU (MRIC) criteria which included clinical manifestation, imaging and investigations. Results: In total, 297 patients were enrolled in this study. The prevalence of VAP was 22.0%. The most common causative pathogen was Acinetobacter sp. (31.8%). Multivariate analysis using simple logistic regression showed that risk factors for VAP were elderly patients (P=0.02; OR 1.02; 95% CI 1.00, 1.04), increase duration of ventilation (P<0.001; OR 1.49; 95% CI 1.35, 1.63), length of stay in ICU (P<0.001; OR 1.45; 95% CI 1.33, 1.59), length of stay in hospital (P<0.001; OR 1.07; 95% CI 1.04, 1.09), respiratory diseases (P=0.02; OR 2.25; 95% CI 1.17, 4.33), lung malignancy (P<0.001; OR 22.35; 95% CI 6.24, 80.09), previous antibiotic within three months (P=0.02; OR 2.25; 95% CI 1.17, 4.33), tracheostomy (P<0.001; OR 18.42; 95% CI 9.36, 36.23), reintubation (P<0.001; OR 25.69; 95% CI 12.73, 51.82), transportation for remote procedure (P<0.001; OR 20.76; 95% CI 9.65, 44.76), central venous line (CVL) insertion (P=0.04; OR 2.22; 95% CI 1.04, 4.76), continuous sedation (P=0.03; OR 1.85; 95% CI 1.04, 3.26) and without venous thromboprophylaxis (P=0.03; OR 2.05; 95% CI 1.09, 3.87). Conclusion: The prevalence and risk factors in our study were comparable to national and international data. We identify one new risk factor which is CVL insertio

Patient-based quality control for glucometers: using the moving sum of positive patient results and moving average

Introduction: The capability of glucometer internal quality control (QC) in detecting varying magnitude of systematic error (bias), and the potential use of moving sum of positive results (MovSum) and moving average (MA) techniques as potential alternatives were evaluated. Materials and methods: The probability of error detection using routine QC and manufacturer’s control limits were investigated using historical data. Moving sum of positive results and MA algorithms were developed and optimized before being evaluated through numerical simulation for false positive rate and probability of error detection. Results: When the manufacturer’s default control limits (that are multiple times higher than the running standard deviation (SD) of the glucometer) was used, they had 0-75% probability of detecting small errors up to 0.8 mmol/L. However, the error detection capability improved to 20-100% when the running SD of the glucometer was used. At a binarization threshold of 6.2 mmol/L and block sizes of 200 to 400, MovSum has a 100% probability of detecting a bias that is greater than 0.5 mmol/L. Compared to MovSum, the MA technique had lower probability of bias detection, especially for smaller bias magnitudes; MA also had higher false positive rates. Conclusions: The MovSum technique is suited for detecting small, but clinically significant biases. Point of care QC should follow conventional practice by setting the control limits according to the running mean and SD to allow proper error detection. The glucometer manufacturers have an active role to play in liberalizing QC settings and also enhancing the middleware to facility patient-based QC practices

Non-Coding RNAs Regulating Morphine Function: With Emphasis on the In vivo and In vitro Functions of miR-190

Non-coding RNAs (ncRNAs), especially microRNAs, are reported to be involved in a variety of biological processes, including several processes related to drug addiction. It has been suggested that the biological functions of opioids, one typical type of addictive drugs, are regulated by ncRNAs. In the current review, we examine a variety of mechanisms through which ncRNAs could regulate μ-opioid receptor (OPRM1) activities and thereby contribute to the development of opioid addiction. Using miR-23b as an example, we present the possible ways in which ncRNA-mediated regulation of OPRM1 expression could impact opioid addiction. Using miR-190 as an example, we demonstrate the critical roles played by ncRNAs in the signal cascade from receptor to systemic responses, including the possible modulation of adult neurogenesis and in vivo contextual memory. After discussing the possible targets of ncRNAs involved in the development of opioid addiction, we summarize the mechanisms underlying the interaction between ncRNAs and opioid addiction and present suggestions for further study

Factors affecting customer loyalty in the telecommunications industry in the Klang Valley, Malaysia

A vital factor in the growth and performance of a company in the current highly competitive telecommunications industry is the development and enhancement of customer loyalty. Although several studies in the past have helped explain the influence of some significant variables for loyalty, not many studies have examined the effects of certain factors such as service quality, customer value and corporate image on the loyalty of subscribers of mobile telecommunication companies or providers. Thus, the aim of this study is to explore the critical factors of service quality, customer value, corporate image and customer satisfaction that generate customer loyalty in the mobile communication service markets in the Klang Valley, Malaysia. Furthermore, this study also attempts to validate the connection between these factors and customer loyalty. This study employed the convenience sampling method to select 100 respondents in the Klang Valley, Malaysia, who are mobile phone users. Their personal information was analysed by means of descriptive analysis, while inferential analysis was used to test the hypotheses. All the hypotheses were found to be supported by the findings of the study, which also showed that the tested variables are significantly related to each other. This illustrates that mobile service providers wanting to build and maintain a competitive edge in the mobile service market should make greater efforts to enhance the quality of their service, provide superior customer value, attain higher customer satisfaction and win customer loyalty

Impact of combining data from multiple instruments on performance of patient-based real-time quality control

It is unclear what is the best strategy for applying patient-based real-time quality control (PBRTQC) algorithm in the presence of multiple instruments. This simulation study compared the error detection capability of applying PBRTQC algorithms for instruments individually and in combination using serum sodium as an example. Four sets of random serum sodium measurements were generated with differing means and standard deviations to represent four simulated instruments. Moving median with winsorization was selected as the PBRTQC algorithm. The PBRTQC parameters (block size and control limits) were optimized and applied to the four simulated laboratory data sets individually and in combination. When the PBRTQC algorithm were individually optimized and applied to the data of the individual simulated instruments, it was able to detect bias several folds faster than when they were combined. Similarly, the individually applied algorithms had perfect error detection rates across different magnitudes of bias, whereas the error detection rates of the algorithm applied on the combined data missed smaller biases. The performance of the individually applied PBRTQC algorithm performed more consistently among the simulated instruments compared to when the data were combined. While combining data from different instruments can increase the data stream and hence, increase the speed of error detection, it may widen the control limits and compromising the probability of error detection. The presence of multiple instruments in the data stream may dilute the effect of the error when it only affects a selected instrument