Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms

Human-dominated ecosystems speed up the loss of habitats, populations, and species. Thus, monitoring and managing the Earth’s heritage of biodiversity is a challenge in natural resource management. Mapping protected areas (PAs) is essential in understanding the disturbance that can affect biodiversity and conservation management. Land use-land cover (LULC) maps can be used as a decision making tool by policy makers to ensure sustainable development and understanding of the effect of human activities within and around PAs. However, in Malaysia, the limited updated maps of PAs make the effective management of PAs problematic. Therefore, this study aimed to produce an updated Land LULC map for the PA Krau Wildlife Reserve (KWR) and its surroundings using remote sensing and related geospatial technologies. Three supervised classification algorithms were used and compared. Multidated images from Landsat 8 were utilized, and spectral angle mapper (SAM), support vector machine (SVM), and artificial neural network (ANN) classifiers were applied and evaluated. The approaches of pan-sharpening and cloud patching were used to enhance the accuracy of LULC classification. The images were classified into five classes: dense forest, less dense forest or agriculture, built-up area, bare soil, and water. The overall accuracies of SAM, ANN, and SVM for the 15 m spatial resolution images were 81.96%, 98.22% and 97.40%, respectively. The ANN map produced the highest overall accuracy and was consequently utilized to extract additional information related to disturbance and encroachment within and around the PA. Findings indicated that socioeconomic activities played a major role in altering the environment of KWR

Utilization of sugarcane BagAsse ash for stabilization/solidification of lead-contaminated soils

Recently, many researchers are interested in using agricultural waste as an additive to remediate the contaminated soils. In this study, the effectiveness of sugarcane bagasse ash (SCBA) as the substitution binder to Ordinary Portlant Cement (OPC) content in Stabilization/Solidification (S/S) method was investigated through the physical and chemical characteristics namely the Unconfined Compressive Strength (UCS) and Toxicity Characteristic Leaching Procedure (TCLP). Synthetic contaminated soil was prepared in bulk by mixing soil samples with lead nitrate to achieve the concentration of 500 ppm. The OPC and SCBA varying from 5% to 20% were added to stabilize and solidify the contaminated soils. The cylindrical specimens (D = 38 mm, H = 76 mm), was compacted in five layers with 50 blows each. A further 3, 7, 14 and 28 days were allowed for curing in the temperature 25±2 ?C and humidity > 80%. Results indicate that all samples containing OPC and SCBA satisfy the US EPA strength requirement of 0.35 MPa for S/S sample. The TCLP testing shows that sample containing OPC with SCBA has been successful treated which produced the leachability below US EPA limit for lead of 5 mg/L. In conclusion, the use of SCBA as part of replacement of OPC has been successful in increasing the strength and reducing the leachability compared to untreated sample

Fruit Sorting Based on Machine Vision Technique

Nowadays, most of the industries have started to use the automation system instead of manual operating system. Fruit sorting by machine vision technique is a potential automation system which is able to obtain their target in term of productivity and quality. This paper proposes the techniques to differentiate the type of apple based on size. A set of image processing algorithm is designed in order to classify the apple according to the information provided from an image. The image processing techniques such as image enhancement, morphological, image segmentation and HSV image conversion are used for the purpose of differentiating the type of apple based on size. Meanwhile, an experiment is carried out in order to determine the capability of designed image processing algorithm. There are a plenty of problems found during the experiment due to some technical error such as shadow and object surface reflection. From the errors, different methods such as HSV conversion method are used to optimize the performance of fruit classification. As short, the image processing algorithm is able to detect the size of appl

Perception of tomorrow’s Health-Care connoisseur and front-runners of their educational environment utilizing DREEM inventory in Bahasa Melayu version, the native language of Malaysia

Background There have been a lot of reports throughout the world that medical students were abused during their undergraduate education and clerkship training. Thereafter, calls for intensifying the evaluation of medical and health schools’ curricula based on students’ perceptions of their educational environment. Several studies, methods, and instruments were developed including the Dundee Ready Education Environment Measure (DREEM) inventory, to evaluate the medical educational environment in last five decades. The DREEM inventory has been translated into minimum eight different native tongues namely Arabic, Chinese, Japanese, Persian, Portuguese, Spanish, Swedish, and Turkish. Aims The objective of this study was to assess the educational environment of the UniSZA undergraduate medical program from the students’ perspective utilizing the DREEM inventory translated in Bahasa Melayu. Methods This was a descriptive cross-sectional survey conducted among the medical students of session 2015-2016 to assess educational environment of the Faculty of Medicine, UniSZA. The study was conducted from December 2015 to January 2016. Universal sampling technique was adopted. Results A total of 277 (95.5 per cent) out of 290 students responded to the questionnaire; among them 27.4 per cent were male and 72.6 per cent were female respondents. The overall mean DREEM scores for both preclinical and clinical students were 67.41±24.06. The scores for pre-clinical and clinical were 64.02±25.10 and 69.65±23.15 respectively; however, no statistically significant (p=0.57) differences was observed between two phases. A significant difference was observed between gender of the respondents in students’ perceptions of teachers (p=0.005) and students’ social self-perceptions (p=0.046)

Relationship of Physical Parameters in Pb-Contaminated by Stabilization/Solidification Method

This research was performed to investigate the relationship between compressive strength, density and water absorption of Pb-contaminated soil treated by Ordinary Portland cement (OPC) incorporated with sugarcane bagasse (SCB) using the Stablisation/Solidification (S/S) method. The SCB and OPC varying from 5% to 15% was added to stabilize and solidify the Pb-contaminated soil. The cylindrical samples were prepared and cured at room temperature for 28 days. Unconfined compression test, density test and water absorption test were conducted on these samples. The relationship between these variables were determined using simple statistical method by Analysis of Variance (ANOVA). Results indicate that there is a strong relationship between compressive strength and density with a regression coefficient of 77.4%. There is also a significant relationship between density and water absorption of approximately 70.7%. In addition, compressive strength and water absorption produced a strong relationship which is 73.6% of variability in strength development. In a nutshell, the strong relationship between these three variables represent the actual physical characteristics in S/S application

Relationship of Physical Parameters in Pb-Contaminated by Stabilization/Solidification Method

This research was performed to investigate the relationship between compressive strength, density and water absorption of Pb-contaminated soil treated by Ordinary Portland cement (OPC) incorporated with sugarcane bagasse (SCB) using the Stablisation/Solidification (S/S) method. The SCB and OPC varying from 5% to 15% was added to stabilize and solidify the Pb-contaminated soil. The cylindrical samples were prepared and cured at room temperature for 28 days. Unconfined compression test, density test and water absorption test were conducted on these samples. The relationship between these variables were determined using simple statistical method by Analysis of Variance (ANOVA). Results indicate that there is a strong relationship between compressive strength and density with a regression coefficient of 77.4%. There is also a significant relationship between density and water absorption of approximately 70.7%. In addition, compressive strength and water absorption produced a strong relationship which is 73.6% of variability in strength development. In a nutshell, the strong relationship between these three variables represent the actual physical characteristics in S/S application

ANALYSIS AND RESULTS: CONFIRMATORY FACTOR ANALYSIS THE MALAY VERSION OF DREEM INVENTORY WITH MEDICAL STUDENTS OF UNISZA, KUALA TERENGGANU, MALAYSIA

  Objectives: The DREEM inventory has been universally established as a generic instrument to assess health-related educational programs. There were some apprehensions regarding the psychometric properties of the DREEM raised in last few years. This study evaluated first ever the psychometric properties of the Bahasa Melayu version of the DREEM in a sample of Malaysian medical students.Methods: A cross-sectional study was carried and universal sampling method was applied. Researchers selected 1-5th-year medical students of Universiti Sultan Zainal Abidin, Malaysia, as study subjects. Researchers collected data through a guided self-administered questionnaire during a face-to-face session.Results: Confirmatory factor analysis (CFA) showed that the one factor model of DREEM-M (Model A), consisting 50 items were not fit, indicating it was a multidimensional instrument. On further CFA, it appeared that the proposed five-factor structure was not fit (Model B) as all the goodness-of-fit indices did not signify a model fit.Conclusions: The study findings revealed that the DREEM inventory 50-item inventory failed to achieve a model fit, but it demonstrated a high of internal consistency. The proposed 19-item DREEM-M revealed good model fit