Purpose of this study to be conducted is to identify the risk factor of low back pain amongst port crane operator and to improve the health management program in the company. The objectives of this study are to evaluate the major group of port crane operator that having low back pain problem, to analyse the risk factors that associated to low back pain problem (WBV, Awkward prolonged sitting and shift work-psychological) , individual characteristics (sport activity or hobby), to analyse the associated rate operator’s absence from work (medical leave) and low back pain problem and to propose the basic ergonomic assessment checklist for management to investigate health incident cases and fit-to-work (ergonomics) screening checklist for new recruitment. A survey research design through the distribution of the questionnaire and interview & field observation will
be used for research methodology. The population of this study consists of port crane operatorsRubber Tyred Gantry Operator (RTG). Questionnaire method used to collect all relevant information from correspondence. Interview also will be conducted to gain further details information. Data were analyzed with the usage of Statistical Package for the Social Sciences (SPSS) to make the process of analysis easier. As result, firstly, the study shown that there are association of risk factor for working posture and years of exposure with Low back Pain. The null hypothesis was rejected and there is probability that these risk factors have influence the low back pain. It was also concluded that the null hypothesis was accepted which means there are no correlation of risk factors for heavy physical works, previous job experience, previous accident with low back pain problem. Thirdly, the study
shown there are no correlation of rate operator’s absence from work (medical leave) with low back pain problem as the null hypothesis was accepted with p value <0.05
Keywords: Port, Low Back Pain, Ergonomics, Occupational Safety & Health, Rubber tyre gantry, Back Pain
Nowadays, intelligent vehicles have received a considerable attention among the
researchers to reduce the number of collisions and road accidents. One of the
challenging tasks for these vehicles is road lane detection or road boundaries
detection. In this research, a lane detection algorithm was developed to detect the
right and left lane markers on the road by using two cameras which act as a stereo
vision for the system. It is based on edge detection by using Canny Edge Detection to
reduce unnecessary data on the images and to perform features recognition for the
lane. After the features has been extracted, the algorithm is followed by Hough
Transform method to generate the detected lines on the image obtained from the
stereo vision camera. The algorithm has to work in different environment to be used
in real world applications. The stereo vision algorithm is implemented to generate
disparity map of area. This helps to gain more information on environment, such as the
estimated distance of the lines, the distance of the vehicle to the turns. The experiment
result shows the detection of right and left lane on the road with disparity map to
determine an estimate of the distance of detected lanes from the stereo vision camera.
Computer vision is applied in many software and devices. The detection and
reconstruction of the human skeletal structure is one of area of interest, where the
camera will identify the human parts and construct the joints of the person standing in
front. Three-dimensional pose estimation is solved using various learning approaches,
such as Support Vector Machines and Gaussian processes. However, difficulties in
cluttered scenarios are encountered, and require additional input data, such as
silhouettes, or controlled camera settings. The paper focused on estimating the threedimensional
pose of a person without requiring background information, which is
robust to camera variations. Each of the joint has three-dimensional space position and
matrix orientation with respect to the sensor. Matlab Simulink was utilized to provide
communication tools with depth camera using Kinect device for skeletal detection.
Results on the skeletal detection using Kinect sensor is analysed in measuring the
abilities to detect skeletal structure accurately, and it is shown that the system is able
to detect human skeletal performing non-complex basic motions in daily life.
Nowadays, the applications of tracking moving object are commonly used in various
areas especially in computer vision applications. There are many tracking algorithms
have been introduced and they are divided into three groups which are generative
trackers, discriminative trackers and hybrid trackers. One of the methods is TrackingLearning-Detection
(TLD) framework which is an example of the hybrid trackers where
combination between the generative trackers and the discriminative trackers occur. In
TLD, the detector consists of three stages which are patch variance, ensemble classifier
and KNearest Neighbor classifier. In the second stage, the ensemble classifier depends
on simple pixel comparison hence, it is likely fail to offer a better generalization of the
appearances of the target object in the detection process. In this paper, OnlineSequential
Extreme Learning Machine (OS-ELM) was used to replace the ensemble
classifier in the TLD framework. Besides that, different types of Haar-like features were
used for the feature extraction process instead of using raw pixel value as the features.
The objectives of this study are to improve the classifier in the second stage of detector
in TLD framework by using Haar-like features as an input to the classifier and to get a
more generalized detector in TLD framework by using OS-ELM based detector. The
results showed that the proposed method performs better in Pedestrian 1 in terms of
F-measure and also offers good performance in terms of Precision in four out of six
videos.
Phishing detection is a momentous problem which can be deliberated by many
researchers with numerous advanced approaches. Current anti-phishing mechanisms
such as blacklist-base anti-phishing, Heuristic-based anti-phishing does suffer low
detection accuracy and high false alarm. There is need for efficient mechanism to
protect users from phishing websites. The purpose of this study is to investigate the
capability of 6 machine learning algorithms i.e. Multi-Layer Perceptron (MLP), Support
Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression
(LR) and Naïve Bayes (NB) to classify phishing and non-phishing websites. These
algorithms were trained with two different groups of training in WEKA environment
and then were tested in terms of accuracy, precision, TP rate, and FP rate on a 3
different sets of dataset which contains dissimilar portion of phishing and non-phishing
instances. Results presented that Naïve Bayes classifier has better detection accuracy
between other classifiers for predicting phishing websites while Multi-Layer
Perceptron gave worst result in terms of detection accuracy. The result also showed
that Support Vector machine has better FP rate between other classifier. In addition,
Random Forest, Decision Tree, and Naïve Bayes can classify all phishing websites as
phishing correctly. It means that TP rate is 100% for these classifiers. In conclusion this
paper suggests using NB as the best classifier for predicting phishing and non-phishing
websites.
Energy consumption of Wireless Sensor Networks (WSN) is an important aspect in
the design requirement. This is especially true in a situation where WSN is being
operated in isolated areas and thus relying on batteries due to unavailability of power
infrastructure. Since energy efficiency is the main concern in the deployment of WSN,
the sensor node must keep track of the charge that is left in the battery, commonly
referred as the State of Charge (SoC). To prevent the discontinuation of the operation
of the sensor node from power cut off, it is important to find an analytic model for
the battery’s state of charge. In this paper, an optimized structure of Multi-Layer
Perceptron (MLP) is utilized to obtain a model of the battery state-of-charge in
wireless sensor nodes. Results show the suitability of the method that produces
accurate and simple models, capable of being implemented even in low cost and very
constrained real motes.
Piano technique is one of the main part of piano playing. Some researches had
attempted to unveil the technique of virtuoso pianists using technologies. These
researches employ different types of sensors in order to capture motion data of piano
playing. However, one area in this research had been under-represented, which is
finger position and pressure measurement applied by the musician while playing the
musical instrument. Research that embark on this area faced a common problem, the
sensors used in these research are directly in contact with the pianist, which causes a
change of piano playing experience. Since piano playing consists of very delicate
interaction between the pianist and the piano, such change of experience may affect
the pianist’s performance. These sensors are considered to be intrusive to the piano
playing experience. Concluding the challenges faced by current technologies, a nonintrusive
sensor is proposed and the circuit design of the sensor is discussed in this
paper.
Microbial Fuel Cell (MFC) is a device that generates electricity from the metabolism of
bacteria simultaneously treats wastewater by decolourizing the azo dye in wastewater.
In this work, the effect of different external loads and bacterial loads were examined.
The maximum open circuit voltage generated was 390 mV by using 7 consortia of
bacteria while the maximum current generated was 50 µA using 10 Ω resistor. 97%
decolourization efficiency of 0.1 g/L of azo dye was achieved after 5 days of operation.
Besides, the maximum current density and power density achieved were 17.9 µA/cm2
and 460 µW/cm2
respectively. Polarization curve was plotted and Scanning Electron
Microscope was applied to visualize the bacterial community attachment onto the
graphite felt electrode. Cyclic voltammetry was applied to study the redox properties
of the Azo dye using microorganisms in MFC. Overall, these 7 bacterial strains used in
this work showed the capability in decolourizing the Azo dye simultaneously producing
electricity in MFC.
The probability of the construction accident to happen is high due the nature of
Construction work that involves complex activities, methods, machineries, materials
and hazards. The occupational safety and health (OSH) law and regulations are
mandatory for every construction project to uphold. Responsibilities to ensure the
safety and health at the workplace lies with those who create the risk and with those
who work with the risk. The owner or client of the construction project has the upper
hand in determining the standard of OSH implementation in their project through
contract documents. If the contract documents comprehensively spell out OSH
requirements and cover all OSH cost, then the issues of contractor not implementing
OSH measures could be minimized. The objective of this study is to identify
Occupational Safety and Health requirements (OSH) in the contract document of
selected construction projects. To achieve this objective, a total of seven contract
document was collected from several construction companies. The qualitative analysis
was performed to identify the extent of OSH requirements and costs are being
mentioned in the contract documents. The finding shows that most of the contract
document contains very little emphasis on OSH requirements and budgeting. Only one
contract contains, an appendix that spell out about the safe work practices for
construction works. The visible allocated budget for OSH requirements for all seven
contracts is very minute range from 0.21% to 1.99% of contract value. In order to
ensure that occupational safety and health is properly implemented, safety needs must
be included in the budget because implementation it is not free, this can be achieved
by making it a permanent feature in all bills of quantity of the project.
Due to high energy demand worldwide, finding an alternative renewable and
sustainable energy source is of great interest. Plant microbial fuel cell (P-MFC) is one
of the most promising methods to generate green energy. In P-MFC, a plant is placed
into the anode compartment. Mutual interaction between plant root rhizodeposits
and bacterial community results in the biofilm formation at the vicinity of the
rhizosphere area in plant root could be utilized to generate electricity. Indeed, in PMFC,
bacteria metabolize rhizodeposits into electrons and protons. These electrons
could be then converted into green electricity. The objectives of this research are to
utilize Epipremnum aureum plant collected from Kota Tinggi’s lake to generate
electricity and observe current generation by different resistors, to characterize
immobilized bacteria attached on the anode surface then identify the optimum growth
temperature for isolated bacteria. Five plant microbial fuel cells were constructed in a
H-shape (dual- chambers) configuration in the plastic container. Maximum current
density for 20 days for P-MFC by external resistance of 100k Ω was 0.1 µA/cm2
with
maximum power density of 0.85 µW/cm2 and the open circuit voltage (OCV) was
measured at 195 mV. Besides, fresh biomass averages increased 5g after 20 days of
experiments below and above ground as compared to the initial fresh biomass. Five
isolated bacterial strains from the graphite felt surface found on the anode were
screened by nine biochemical tests such as catalase, TSI (triple sugar iron agar), gelatin
and etc. The immobilized bacteria attached to anode electrode in P-MFC were further
examined with Fast Electron Scanning Electron Microscopy (FESEM). The isolated
bacterial growth curves were determined at two different temperatures of 25 °C and
37 °C. The optimum growth temperature predominantly for them was 37 °C.
In this study, we investigate the ability of the bacterial isolates from an Iraqi oil
reservoir, namely POS and PCO Oil to decolorize commercially used model azo dye Acid
Red-27(AR-27). The effects of inoculation volume and glycerol concentrations were
optimized to develop an economically feasible decolourization process. The isolates
were able to decolourize azo dye (AR27) at the highest decolorization efficiency of 98%
in 10 mL bacterial solution consisted of POS and PCO Oil and in the presence of 6.34
g/L glycerol. An optimized MFC using this bacterial consortium (POS + PCO Oil) and
graphite rod electrodes produced a maximum open circuit voltage (OCV) of 175 mV, in
the presence of potassium ferricyanide as the electron acceptor at the cathode. The
maximum current density of 1.7 μA/cm² and power density of 59.3 μW/cm² were
achieved when an external load of 5 kΩ was applied. Morphological analysis was
performed using Scanning Electron Microscope (SEM) to prove the bacterial
attachment onto the anode surface (graphite rod) in the MFC operation. This work
proposed that the bacterial strains POS and PCO Oil possess the ability to decolorize
Azo dye AR27 and generate electricity in the absence of nitrogen source.
Many nonlinear problems that arise in various science and engineering fields can be
modelled by the Goursat partial differential equations. Modelling these non-linear
problems using the Goursat partial differential equations has not received much
attention especially the theoretical aspect . The proposed scheme of solution is
supported by examining a nonlinear Goursat problem. The verification of the
theoretical results from several series of numerical experiments are discussed. Results
obtained from Taylor series expansion show that the proposed new scheme is
consistent. By using the von Neumann analysis and essence of stability, the proposed
new scheme is found to be unconditionally stable. In addition, the trend of the
numerical results shows that the new scheme is also convergent.
Aggregate planning acts as a blueprint for all operational planning activities. Despite
the substantial amount of research that has been done in determining methods to
improve aggregate planning approaches, the industry is still at a loss when it comes
to working on the tactical planning aspect, especially in aggregate production.
Therefore, this research work aims to present a comprehensive and generalised
framework that will formulate a realistic batch production environment using an
interactive Production Decision Support System. This system consists of an aggregate
planning framework that combines a simulation model and a Pinch Analysis graphical
approach to improve the effectiveness and efficiency of the decision-making process.
The target is to allow operational opportunities to be captured at first sight and thus,
maximise organisational profit. The simplicity and practicality of this new Production
Decision Support System is demonstrated through two illustrative examples where a
total of four heuristics were identified and turned into the new strategies to avoid the
stock-out scenarios.