The approach of this paper is to predict the sand mass distribution in an urban stormwater holding pond at the Stormwater Management And Road Tunnel (SMART) Control Centre, Malaysia, using simulated depth average floodwater velocity diverted into the holding during storm events. Discriminant analysis (DA) was applied to derive the classification function to spatially distinguish areas of relatively high and low sand mass compositions based on the simulated water velocity variations at corresponding locations of gravimetrically measured sand mass composition of surface sediment samples. Three inflow parameter values, 16, 40 and 80 m(3) s(-1), representing diverted floodwater discharge for three storm event conditions were fixed as input parameters of the hydrodynamic model. The sand (grain size > 0.063 mm) mass composition of the surface sediment measured at 29 sampling locations ranges from 3.7 to 45.5%. The sampling locations of the surface sediment were spatially clustered into two groups based on the sand mass composition. The sand mass composition of group 1 is relatively lower (3.69 to 12.20%) compared to group 2 (16.90 to 45.55%). Two Fisher's linear discriminant functions, F 1 and F 2, were generated to predict areas; both consist of relatively higher and lower sand mass compositions based on the relationship between the simulated flow velocity and the measured surface sand composition at corresponding sampling locations. F 1 = -9.405 + 4232.119 × A - 1795.805 × B + 281.224 × C, and F 2 = -2.842 + 2725.137 × A - 1307.688 × B + 231.353 × C. A, B and C represent the simulated flow velocity generated by inflow parameter values of 16, 40 and 80 m(3) s(-1), respectively. The model correctly predicts 88.9 and 100.0% of sampling locations consisting of relatively high and low sand mass percentages, respectively, with the cross-validated classification showing that, overall, 82.8% are correctly classified. The model predicts that 31.4% of the model domain areas consist of high-sand mass composition areas and the remaining 68.6% comprise low-sand mass composition areas.
Fish vocalisation is often a major component of underwater soundscapes. Therefore, interpretation of these soundscapes requires an understanding of the vocalisation characteristics of common soniferous fish species. This study of captive female bluefin gurnard, Chelidonichthys kumu, aims to formally characterise their vocalisation sounds and daily pattern of sound production. Four types of sound were produced and characterised, twice as many as previously reported in this species. These sounds fit two aural categories; grunt and growl, the mean peak frequencies for which ranged between 129 to 215 Hz. This species vocalized throughout the 24 hour period at an average rate of (18.5 ± 2.0 sounds fish-1 h-1) with an increase in vocalization rate at dawn and dusk. Competitive feeding did not elevate vocalisation as has been found in other gurnard species. Bluefin gurnard are common in coastal waters of New Zealand, Australia and Japan and, given their vocalization rate, are likely to be significant contributors to ambient underwater soundscape in these areas.
Background. Fish species may be identified based on their unique otolith shape or contour. Several pattern recognition methods have been proposed to classify fish species through morphological features of the otolith contours. However, there has been no fully-automated species identification model with the accuracy higher than 80%. The purpose of the current study is to develop a fully-automated model, based on the otolith contours, to identify the fish species with the high classification accuracy. Methods. Images of the right sagittal otoliths of 14 fish species from three families namely Sciaenidae, Ariidae, and Engraulidae were used to develop the proposed identification model. Short-time Fourier transform (STFT) was used, for the first time in the area of otolith shape analysis, to extract important features of the otolith contours. Discriminant Analysis (DA), as a classification technique, was used to train and test the model based on the extracted features. Results. Performance of the model was demonstrated using species from three families separately, as well as all species combined. Overall classification accuracy of the model was greater than 90% for all cases. In addition, effects of STFT variables on the performance of the identification model were explored in this study. Conclusions. Short-time Fourier transform could determine important features of the otolith outlines. The fully-automated model proposed in this study (STFT-DA) could predict species of an unknown specimen with acceptable identification accuracy. The model codes can be accessed at http://mybiodiversityontologies.um.edu.my/Otolith/ and https://peerj.com/preprints/1517/. The current model has flexibility to be used for more species and families in future studies.
Ariid catfishes, belong to family Ariidae is considered as one of the taxonomically problematic groups, which is still under review by fish taxonomist globally. Species level identification of some ariids often resulted in species misidentification because of their complex characters and very similar morphological characters within genera. A vigilant and detail observation is very important during the species level identification of ariid species. In these contexts, this study was carried out in order to determine the morphological variations of one of the ariid genera, Plicofollis, which have been giving misleading taxonomic information in the south-east Asian countries. A Truss network technique was used throughout the study period. The study was conducted based on 20 truss measurements using 22 to 23 specimens per species, namely P. argyropleuron, P. nella and P. tenuispinis found in Peninsular Malaysian waters. Morphological variations were determined using a multivariate technique of discriminant function analysis (DFA). The results obtained in this study showed that discriminant analysis using truss network measurements has produced very clear separations of all the species in Plicofollis group. Several important morphological characters have been identified, which represent body depth and caudal regions of the fish. The documentary evidences of these variables could be considered as the constructive functional features, which could enable us to assess more accurately to distinguish the species within this complex Ariidae family.
BACKGROUND: This study was carried out to evaluate the accuracy of sex estimation by discriminant analysis and stepwise discriminant analysis equations generated from metatarsal bones in a Thai population.
MATERIAL AND METHODS: The testing samples utilized in this study consisted of 50 skeletons (25 males and 25 females) obtained from the Khon Kaen University Skeletal Collection, Department of Anatomy, Faculty of Medicine, Khon Kaen University. Seven measurements of metatarsal bones were measured in centimeters, using either a mini-osteometric board (MOB) or a sliding caliper. The values measured from the Khon Kaen Skeletal Collection were used to determine the accuracy and applicability of sex determination, as predicted by Y1-Y6 equations which were generated from a Chiang Mai Skeletal Collection.
RESULTS: The percentage of sex determination accuracies predicted from the Y1-Y6 equations demonstrated accuracy rates of 80-95.6.
CONCLUSIONS: The Chiang Mai sex determination equations, generated from metatarsal bones by discriminant analysis (Y1-Y3) and stepwise discriminant analysis (Y4-Y6), demonstrated high accuracy rates of prediction, suggesting that these equations may be useful for sex determination within the Thai population.
KEYWORDS: Foot; Metatarsal bones; Sex determination; Thailand
Characterization of hydrochemistry changes in Johor Straits within 5 years of monitoring works was successfully carried out. Water quality data sets (27 stations and 19 parameters) collected in this area were interpreted subject to multivariate statistical analysis. Cluster analysis grouped all the stations into four clusters ((Dlink/Dmax) × 100<90) and two clusters ((Dlink/Dmax) × 100<80) for site and period similarities. Principal component analysis rendered six significant components (eigenvalue>1) that explained 82.6% of the total variance of the data set. Classification matrix of discriminant analysis assigned 88.9-92.6% and 83.3-100% correctness in spatial and temporal variability, respectively. Times series analysis then confirmed that only four parameters were not significant over time change. Therefore, it is imperative that the environmental impact of reclamation and dredging works, municipal or industrial discharge, marine aquaculture and shipping activities in this area be effectively controlled and managed.
The metabolite changes in three germplasm accessions of Malaysia Andrographis paniculata (Burm. F.) Nees, viz. 11265 (H), 11341 (P) and 11248 (T), due to their different harvesting ages and times were successfully evaluated by attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy and translated through multivariate data analysis of principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). This present study revealed the feasibility of ATR-FTIR in detecting the trend changes of the major metabolites - andrographolide and neoandrographolide - functional groups in A. paniculata leaves of different accessions. The harvesting parameter was set at three different ages of 120, 150 and 180 days after transplanting (DAT) and at two different time sessions of morning (7:30-10:30 am) and evening (2:30-5.30 pm).
Sex estimation is used in forensic anthropology to assist the identification of individual remains. However, the estimation techniques tend to be unique and applicable only to a certain population. This paper analyzed sex estimation on living individual child below 19 years old using the length of 19 bones of left hand applied for three classification techniques, which were Discriminant Function Analysis (DFA), Support Vector Machine (SVM) and Artificial Neural Network (ANN) multilayer perceptron. These techniques were carried out on X-ray images of the left hand taken from an Asian population data set. All the 19 bones of the left hand were measured using Free Image software, and all the techniques were performed using MATLAB. The group of age "16-19" years old and "7-9" years old were the groups that could be used for sex estimation with as their average of accuracy percentage was above 80%. ANN model was the best classification technique with the highest average of accuracy percentage in the two groups of age compared to other classification techniques. The results show that each classification technique has the best accuracy percentage on each different group of age.
Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.
The study was aimed to determine the variation in taxonomic diversity of Polynemus paradiseus based on morphometric and meristic analyses of samples collected from three coastal rivers of Bangladesh (Payra, Tentulia and Kirtonkhola). A total of 105 individuals ranging at 10-20 cm in total length (TL) and 7.91-60.64 g in body weight (BW) were sampled using Been nets and Kachal and Veshal nets. Significant differences were observed in 24 out of 25 morphometric measurements and 6 out of 10 meristic counts among the populations. In morphometric measurements, the first discriminant function (DF1) was accounted for 78.6% and the second discriminant function (DF2) was accounted for 21.4% of among groups variability, explaining 100% of total among group variability. A dendrogram based on morphometric data showed that the Tentulia and Kirtankhola populations showed high degree of overlapping and these two populations were highly different from Payra river population. The canonical graph also showed that the populations of Tentulia and Kirtankhola rivers were more closely related comparing with Payra river population for isometric condition. These findings may provide useful information for the conservation and sustainable management of this important fish.
In the last decade there has been an increasing awareness about 'quality of life' (QOL) of cancer survivors in developing countries. The study aimed to cross-culturally adapt and validate the FACT-H&N (v4) in Urdu language for Pakistani head and neck cancer patients. In this study the 'same language adaptation method' was used. Cognitive debriefing through in-depth interviews of 25 patients to assess semantic, operational and conceptual equivalence was done. The validation phase included 50 patients to evaluate the psychometric properties. The translated FACT-H&N was easily comprehended (100%). Cronbach's alpha for FACT-G subscales ranged from 0.726 - 0.969. The head and neck subscale and Pakistani questions subscale showed low internal consistency (0.426 and 0.541 respectively). Instrument demonstrated known-group validity in differentiating patients of different clinical stages, treatment status and tumor sites (p < 0.05). Most FACT summary scales correlated strongly with each other (r > 0.75) and showed convergent validity (r > 0.90), with little discriminant validity. Factor analysis revealed 6 factors explaining 85.1% of the total variance with very good (>0.8) Kaiser-Meyer-Olkin and highly significant Bartlett's Test of Sphericity (p < 0.001). The cross-culturally adapted FACT-H&N into Urdu language showed adequate reliability and validity to be incorporated in Pakistani clinical settings for head and neck cancer patients.
Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
Information about the sex of individuals is important for human identification. This study was conducted to quantify classification rates of sex prediction models for Malaysians using odontometric profiles. Mesiodistal (MD) and buccolingual (BL) crown dimensions of the permanent dentition were studied in 400 young adult Malaysians, giving a total of 28 tooth size variables. The sample consisted of three major ethnic groups, the Malays, Chinese and Tamils, since the aim was to assess sex dimorphism in Malaysians as a whole. Results showed that the mesiodistal diameter of the lower canine was the most sexually dimorphic dimension in Malaysian Malays and Tamils. Univariate analyses showed that the magnitude and pattern of sex dimorphism varies between these three ethnic groups, with Malaysian Chinese and Tamils being more dimorphic than the Malaysian Malays. Stepwise discriminant functions were generated bearing in mind their application in practical forensic situations. The range of classification rates was from 70.2% to 78.5% for the composite Malaysian group, and 83.8%, 77.9%, 72.4% for Malaysian Chinese, Malays and Tamils, respectively. The 'Area Under the Receiver Operating Characteristic Curve statistics' indicated good classification rates for three prediction models obtained using a combination of all tooth size variables, mandibular teeth, and mesiodistal dimensions in the composite Malaysian group, and for all tooth size variables in each ethnic group. The present study provides strong support for the value of odontometry as an adjunct scientific method for sex prediction in human identification.
This study aims to test the translated Hausa version of the stroke impact scale SIS (3.0) and further evaluate its psychometric properties. The SIS 3.0 was translated from English into Hausa and was tested for its reliability and validity on a stratified random sample adult stroke survivors attending rehabilitation services at stroke referral hospitals in Kano, Nigeria. Psychometric analysis of the Hausa-SIS 3.0 involved face, content, criterion, and construct validity tests as well as internal and test-retest reliability. In reliability analyses, the Cronbach's alpha values for the items in Strength, Hand function, Mobility, ADL/IADL, Memory and thinking, Communication, Emotion, and Social participation domains were 0.80, 0.92, 0.90, 0.78, 0.84, 0.89, 0.58, and 0.74, respectively. There are 8 domains in stroke impact scale 3.0 in confirmatory factory analysis; some of the items in the Hausa-SIS questionnaire have to be dropped due to lack of discriminate validity. In the final analysis, a parsimonious model was obtained with two items per construct for the 8 constructs (Chi-square/df < 3, TLI and CFI > 0.9, and RMSEA < 0.08). Cross validation with 1000 bootstrap samples gave a satisfactory result (P = 0.011). In conclusion, the shorter 16-item Hausa-SIS seems to measure adequately the QOL outcomes in the 8 domains.
In this paper, a Semantic Web service matchmaker called UltiMatch-NL is presented. UltiMatch-NL applies two filters namely Signature-based and Description-based on different abstraction levels of a service profile to achieve more accurate results. More specifically, the proposed filters rely on semantic knowledge to extract the similarity between a given pair of service descriptions. Thus it is a further step towards fully automated Web service discovery via making this process more semantic-aware. In addition, a new technique is proposed to weight and combine the results of different filters of UltiMatch-NL, automatically. Moreover, an innovative approach is introduced to predict the relevance of requests and Web services and eliminate the need for setting a threshold value of similarity. In order to evaluate UltiMatch-NL, the repository of OWLS-TC is used. The performance evaluation based on standard measures from the information retrieval field shows that semantic matching of OWL-S services can be significantly improved by incorporating designed matching filters.
This paper outlines the application of chemometrics and pattern recognition tools to classify palm oil using Fourier Transform Mid Infrared spectroscopy (FT-MIR). FT-MIR spectroscopy is used as an effective analytical tool in order to categorise the oil into the category of unused palm oil and used palm oil for frying. The samples used in this study consist of 28 types of pure palm oil, and 28 types of frying palm oils. FT-MIR spectral was obtained in absorbance mode at the spectral range from 650 cm -1 to 4000 cm -1 using FT-MIR-ATR sample handling. The aim of this work is to develop fast method in discriminating the palm oils by implementing Partial Least Square Discriminant Analysis (PLS-DA), Learning Vector Quantisation (LVQ) and Support Vector Machine (SVM). Raw FT-MIR spectra were subjected to Savitzky-Golay smoothing and standardized before developing the classification models. The classification model was validated through finding the value of percentage correctly classified by test set for every model in order to show which classifier provided the best classification. In order to improve the performance of the classification model, variable selection method known as t-statistic method was applied. The significant variable in developing classification model was selected through this method. The result revealed that PLSDA classifier of the standardized data with application of t-statistic showed the best performance with highest percentage correctly classified among the classifiers.
Nitrate-nitrogen leaching from agricultural areas is a major cause for groundwater pollution. Polluted groundwater with high levels of nitrate is hazardous and cause adverse health effects. Human consumption of water with elevated levels of NO3-N has been linked to the infant disorder methemoglobinemia and also to non-Hodgkin's disease lymphoma in adults. This research aims to study the temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy soil at Ladang Merdeka Ismail Mulong in Kelantan, Malaysia. The complex data matrix (128 x 16) of nitrate-nitrogen parameters was subjected to multivariate analysis mainly Principal Component Analysis (PCA) and Discriminant Analysis (DA). PCA extracted four principal components from this data set which explained 86.4% of the total variance. The most important contributors were soil physical properties confirmed using Alyuda Forecaster software (R2 = 0.98). Discriminant analysis was used to evaluate the temporal variation in soil nitrate-nitrogen on leaching process. Discriminant analysis gave four parameters (hydraulic head, evapotranspiration, rainfall and temperature) contributing more than 98% correct assignments in temporal analysis. DA allowed reduction in dimensionality of the large data set which defines the four operating parameters most efficient and economical to be monitored for temporal variations. This knowledge is important so as to protect the precious groundwater from contamination with nitrate.
The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.
Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH4). Partial correlation analysis (r p) results showed that there is a strong relationship between salinity and turbidity (r p=0.930, p=0.001) and BOD5 and COD (r p=0.839, p=0.001) controlling for the linear effects of conductivity and NH4, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD5, COD, SS, NH4, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.
Statistical validation is crucial for the clustering of unknown samples. This study aims to demonstrate how statistical techniques can be optimized using simulated heroin samples containing a range of analyte concentrations that are similar to those of the case samples. Eight simulated heroin distribution links consisting of 64 postcut samples were prepared by mixing one of two mixtures of paracetamol-caffeine-dextromethorphan at different proportions with eight precut samples. Analyte contents and compositional variation of the prepared samples were investigated. A number of data pretreatments were evaluated by associating the postcut samples with the corresponding precut samples using principal component analysis and discriminant analysis. Subsequently, combinations of seven linkage methods and five distance measures were explored using hierarchical cluster analysis. In this study, Ward-Manhattan showed better distinctions between unrelated links and was able to cluster all related samples in very close distance under the known links on a dendogram. A similar discriminative outcome was also achieved by 90 unknown case samples when clustered via Ward-Manhattan.