Sensing and communication coverage are among the most important trade-offs in Wireless Sensor Network (WSN) design. A minimum bound of sensing coverage is vital in scheduling, target tracking and redeployment phases, as well as providing communication coverage. Some methods measure the coverage as a percentage value, but detailed information has been missing. Two scenarios with equal coverage percentage may not have the same Quality of Coverage (QoC). In this paper, we propose a new coverage measurement method using Delaunay Triangulation (DT). This can provide the value for all coverage measurement tools. Moreover, it categorizes sensors as 'fat', 'healthy' or 'thin' to show the dense, optimal and scattered areas. It can also yield the largest empty area of sensors in the field. Simulation results show that the proposed DT method can achieve accurate coverage information, and provides many tools to compare QoC between different scenarios.
A significant proportion of the worldwide population is of the elderly people living with chronic diseases that result in high health-care cost. To provide continuous health monitoring with minimal health-care cost, Wireless Body Sensor Networks (WBSNs) has been recently emerged as a promising technology. Depending on nature of sensory data, WBSNs might require a high level of Quality of Service (QoS) both in terms of delay and reliability during data reporting phase. In this paper, we propose a data-centric routing for intra WBSNs that adapts the routing strategy in accordance with the nature of data, temperature rise issue of the implanted bio-medical sensors due to electromagnetic wave absorption, and high and dynamic path loss caused by postural movement of human body and in-body wireless communication. We consider the network models both with and without relay nodes in our simulations. Due to the multi-facet routing strategy, the proposed data-centric routing achieves better performance in terms of delay, reliability, temperature rise, and energy consumption when compared with other state-of-the-art.
Experimental and numerical investigation was conducted to study the micromechanics of oil palm empty fruit bunch fibres containing silica bodies. The finite viscoelastic-plastic material model called Parallel Rheological Network model was proposed, that fitted well with cyclic and stress relaxation tensile tests of the fibres. Representative volume element and microstructure models were developed using finite element method, where the models information was obtained from microscopy and X-ray micro-tomography analyses. Simulation results showed that difference of the fibres model with silica bodies and those without ones is larger under shear than compression and tension. However, in comparison to geometrical effect (i.e. silica bodies), it is suggested that ultrastructure components of the fibres (modelled using finite viscoelastic-plastic model) is responsible for the complex mechanical behaviour of oil palm fibres. This can be due to cellulose, hemicellulose and lignin components and the interface behaviour, as reported on other lignocellulosic materials.
There is no report in the English literature on the criteria for neuroablation or neuroaugmentation for the treatment of Parkinson's disease in a developing country like Malaysia. A prospective study of patients with Parkinson's disease from the north-eastern peninsular Malaysia was done to assess their suitability of surgery. Age, race, duration of illness and dementia were considered important factors towards the success of such surgical procedures. A mathematical model is suggested for future cases deemed to be suitable for neuroaugmentative or ablative surgery.
The conventional susceptible-infectious-recovered (SIR) model tends to magnify the transmission dynamics of infectious diseases, and thus the estimated total infections and immunized population may be higher than the threshold required for infection control and eradication. The study developed a new SIR framework that allows the transmission rate of infectious diseases to decline along with the reduced risk of contact infection to overcome the limitations of the conventional SIR model. Two new SIR models were formulated to mimic the declining transmission rate of infectious diseases at different stages of transmission. Model A utilized the declining transmission rate along with the reduced risk of contact infection following infection, while Model B incorporated the declining transmission rate following recovery. Both new models and the conventional SIR model were then used to simulate an infectious disease with a basic reproduction number (r0) of 3.0 and a herd immunity threshold (HIT) of 0.667 with and without vaccination. Outcomes of simulations were assessed at the time when the total immunized population reached the level predicted by the HIT, and at the end of simulations. Further, all three models were used to simulate the transmission dynamics of seasonal influenza in the United States and disease burdens were projected and compared with estimates from the Centers for Disease Control and Prevention. For the simulated infectious disease, in the initial phase of the outbreak, all three models performed expectedly when the sizes of infectious and recovered populations were relatively small. As the infectious population increased, the conventional SIR model appeared to overestimate the infections even when the HIT was achieved in all scenarios with and without vaccination. For the same scenario, Model A appeared to attain the level predicted by the HIT and in comparison, Model B projected the infectious disease to be controlled at the level predicted by the HIT only at high vaccination rates. For infectious diseases with high r0, and at low vaccination rates, the level at which the infectious disease was controlled cannot be accurately predicted by the current theorem. Transmission dynamics of infectious diseases with herd immunity can be accurately modelled by allowing the transmission rate of infectious diseases to decline along with the reduction of contact infection risk after recovery or vaccination. Model B provides a credible framework for modelling infectious diseases with herd immunity in a randomly mixed population.
This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon's 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results.
Digital elevation model (DEM) generation from stereo images is an effective and economical method in topography mapping. This paper used the stereo pair methodology to generate the digital elevation model (DEM) from PRISM (Panchromatic Remote-Sensing Instrument Satellite) sensor which is onboard of ALOS (Advanced Land Observing Satellite). The pair of forward-backward is used as stereoscopic imagery in this study. Ten ground control points (GCPs) are collected with residual error 0.49 pixels to generate an absolute DEM. This generated DEM with 2.5 m spatial resolution is then matched with the 90 m spatial resolution of
SRTM (Space Shuttle Radar Topography Mission) DEM to compare the result. Although SRTM-DEM has a much coarser resolution, the positional accuracy of the matching is found. The difference of the height from the mean sea level (MSL) between the SRTM-DEM and the PRISM-DEM is analyzed and the correlation between the two DEMs is R²=0.8083. The accuracy of the DEM generated is given by the RMSE value of 0.8991 meter.
In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.
A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372kg COD/(m(3)day)) and cyclic time (12, 24, and 48h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O&G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44kg COD/(m(3)day), TDS of 78,000mg/L and reaction time (RT) of 40h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100mg/L and met the discharge limits.
Malaysia alone produces more than 49 million m3 palm oil mill effluent per year. Biological treated palm oil mill effluent via ponding system often fails to fulfill the regulatory discharge standards. This is due to remaining of non-biodegradable organics in the treated effluent. Thus, the aim of this study was to resolve such issue by using electro persulphate oxidation process, for the first time, as a post treatment of palm oil mill effluent. Central composite design in response surface methodology was used to analyze and optimize the interaction of operational variables (i.e., current density, contact time, initial pH and persulphate dosage) targeted on maximum treatment efficiency. The significance of quadratic model of each response was determined by analysis of variance, where all models indicated sufficient significance with p-value
A novel porous coordination polymer adsorbent (BTCA-P-Cu-CP) based on a piperazine(P) as a ligand and 1,2,4,5-benzenetetracarboxylic acid (BTCA) as a linker was synthesized and magnetized to form magnetic porous coordination polymer (BTCA-P-Cu-MCP). Fourier transform infrared (FTIR), X-ray diffraction (XRD), vibrating sample magnetometer (VSM), field emission scanning electron microscope(FESEM), energy-dispersive X-ray spectroscopy(EDS), CHN, and Brunauer-Emmett-Teller(BET) analysis were used to characterize the synthesized adsorbent. BTCA-P-Cu-MCP was used for removal and preconcentration of Pb(II) ions from environmental water samples prior to flame atomic absorption spectrometry(FAAS) analysis. The maximum adsorption capacity of BTCA-P-Cu-MCP was 582 mg g-1. Adsorption isotherm, kinetic, and thermodynamic parameters were investigated for Pb(II) ions adsorption. Magnetic solid phase extraction (MSPE) method was used for preconcentration of Pb(II) ions and the parameters influencing the preconcentration process have been examined. The linearity range of proposed method was 0.1-100 μg L-1 with a preconcentration factor of 100. The limits of detection and limits of quantification for lead were 0.03 μg L-1 and 0.11 μg L-1, respectively. The intra-day (n = 7) and inter-day (n = 3) relative standard deviations (RSDs) were 1.54 and 3.43% respectively. The recoveries from 94.75 ± 4 to 100.93 ± 1.9% were obtained for rapid extraction of trace levels of Pb(II) ions in different water samples. The results showed that the BTCA-P-Cu-MCP was steady and effective adsorbent for the decontamination and preconcentration of lead ions from the aqueous environment.
In this paper, an image-based waste collection scheduling involving a node with three waste bins is considered. First, the system locates the three bins and determines the waste level of each bin using four Laws Masks and a set of Support Vector Machine (SVM) classifiers. Next, a Hidden Markov Model (HMM) is used to decide on the number of days remaining before waste is collected from the node. This decision is based on the HMM's previous state and current observations. The HMM waste collection scheduling seeks to maximize the number of days between collection visits while preventing waste contamination due to late collection. The proposed system was trained using 100 training images and then tested on 100 test images. Each test image contains three bins that might be shifted, rotated, occluded or toppled over. The upright bins could be empty, partially full or full of garbage of various shapes and sizes. The method achieves bin detection, waste level classification and collection day scheduling rates of 100%, 99.8% and 100% respectively.
A characteristic of ecosystems is the existence of manifold of independencies which are highly complex. Various mathematical models have made considerable contributions in gaining a better understanding of the predator-prey interactions. The main components of any predator-prey models are, firstly, how the different population classes grow and secondly, how the prey and predator interacts. In this paper, the two populations' growth rates obey the logistic law and the carrying capacity of the predator depends on the available number of prey are considered. Our aim is to clarify the relationship between models and Holling types functional and numerical responses in order to gain insights into predator interferences and to answer an important question how competition is carried out. We consider a predator-prey model and a two-predator one-prey model to explain the idea. The novel approach is explained for the mechanism measurement of predator interference through depending on numerical response. Our approach gives good correspondence between an important real data and computer simulations.
The most common permittivity function models are compared and identifying the best model for further studies is desired. For this study, simulations using several different models and an analytical analysis on a practical surface Plasmon structure were done with an accuracy of ∼ 94.4% with respect to experimental data. Finite element method, combined with dielectric properties extracted from the Brendel-Bormann function model, was utilized, the latter being chosen from a comparative study on four available models.
The objective of this study was to investigate the performance of employing persulfate reagent in the advanced oxidation of ozone to treat stabilized landfill leachate in an ozone reactor. A central composite design (CCD) with response surface methodology (RSM) was applied to evaluate the relationships between operating variables, such as ozone and persulfate dosages, pH, and reaction time, to identify the optimum operating conditions. Quadratic models for the following four responses proved to be significant with very low probabilities (<0.0001): COD, color, NH3-N, and ozone consumption (OC). The obtained optimum conditions included a reaction time of 210 min, 30 g/m(3) ozone, 1g/1g COD0/S2O8(2-) ratio, and pH 10. The experimental results were corresponded well with predicted models (COD, color, and NH3-N removal rates of 72%, 96%, and 76%, respectively, and 0.60 (kg O3/kg COD OC). The results obtained in the stabilized leachate treatment were compared with those from other treatment processes, such as ozone only and persulfate S2O8(2-) only, to evaluate its effectiveness. The combined method (i.e., O3/S2O8(2-)) achieved higher removal efficiencies for COD, color, and NH3-N compared with other studied applications. Furthermore, the new method is more efficient than ozone/Fenton in advanced oxidation process in the treatment of the same studied leachate.
Cable-stayed bridges are one of the most popular types of long-span bridges. The structural behaviour of cable-stayed bridges is sensitive to the load distribution between the girder, pylons, and cables. The determination of pretensioning cable stresses is critical in the cable-stayed bridge design procedure. By finding the optimum stresses in cables, the load and moment distribution of the bridge can be improved. In recent years, different research works have studied iterative and modern methods to find optimum stresses of cables. However, most of the proposed methods have limitations in optimising the structural performance of cable-stayed bridges. This paper presents a multiconstraint optimisation method to specify the optimum cable forces in cable-stayed bridges. The proposed optimisation method produces less bending moments and stresses in the bridge members and requires shorter simulation time than other proposed methods. The results of comparative study show that the proposed method is more successful in restricting the deck and pylon displacements and providing uniform deck moment distribution than unit load method (ULM). The final design of cable-stayed bridges can be optimised considerably through proposed multiconstraint optimisation method.
The research on damages of structures that are supported by deep foundations has been quite intensive in the past decade. Kinematic interaction in soil-pile interaction is evaluated based on the p-y curve approach. Existing p-y curves have considered the effects of relative density on soil-pile interaction in sandy soil. The roughness influence of the surface wall pile on p-y curves has not been emphasized sufficiently. The presented study was performed to develop a series of p-y curves for single piles through comprehensive experimental investigations. Modification factors were studied, namely, the effects of relative density and roughness of the wall surface of pile. The model tests were subjected to lateral load in Johor Bahru sand. The new p-y curves were evaluated based on the experimental data and were compared to the existing p-y curves. The soil-pile reaction for various relative density (from 30% to 75%) was increased in the range of 40-95% for a smooth pile at a small displacement and 90% at a large displacement. For rough pile, the ratio of dense to loose relative density soil-pile reaction was from 2.0 to 3.0 at a small to large displacement. Direct comparison of the developed p-y curve shows significant differences in the magnitude and shapes with the existing load-transfer curves. Good comparison with the experimental and design studies demonstrates the multidisciplinary applications of the present method.
The Malaysian palm oil industry is a major revenue earner and the country is ranked as one of the largest producers in the world. However, growth of the industry is synonymous with a massive production of agro-industrial wastewater. As an environmental protection and public health concern, the highly polluting palm oil mill effluent (POME) has become a major attention-grabber. Hence, the industry is targeting for POME pollution abatement in order to promote a greener image of palm oil and to achieve sustainability. At present, most palm oil mills have adopted the ponding system for treatment. Due to the successful POME pollution abatement experiences, Malaysia is currently planning to revise the effluent quality standards towards a more stringent discharge limits. Hence, the current trend of POME research focuses on developing tertiary treatment or polishing systems for better effluent management. Biotechnologically-advanced POME tertiary (polishing) technologies as well as other physicochemical methods are gaining much attention as these processes are the key players to push the industry towards the goal of environmental sustainability. There are still ongoing treatment technologies being researched and the outcomes maybe available in a while. However, the research completed so far are compiled herein and reported for the first time to acquire a better perspective and insight on the subject with a view of meeting the new standards. To this end, the most feasible technology could be the combination of advanced biological processes (bioreactor systems) with extended aeration, followed by solids separation prior to discharge. Chemical dosing is favoured only if effluent of higher quality is anticipated.
Landfills are a source of groundwater pollution in Gaza Strip. This study focused on Deir Al Balah landfill, which is a unique sanitary landfill site in Gaza Strip (i.e., it has a lining system and a leachate recirculation system). The objective of this article is to assess the generated leachate quantity and percolation to the groundwater aquifer at a specific site, using the approaches of (i) the hydrologic evaluation of landfill performance model (HELP) and (ii) the water balance method (WBM). The results show that when using the HELP model, the average volume of leachate discharged from Deir Al Balah landfill during the period 1997 to 2007 was around, 6800 m3/year. Meanwhile, the average volume of leachate percolated through the clay layer was 550 m3/year, which represents around 8% of the generated leachate. Meanwhile, the WBM indicated that the average volume of leachate discharged from Deir Al Balah landfill during the same period was around 7660 m3/year--about half of which comes from the moisture content of the waste, while the remainder comes from the infiltration of precipitation and re-circulated leachate. Therefore, the estimated quantity of leachate to groundwater by these two methods was very close. However, compared with the measured leachate quantity, these results were overestimated and indicated a dangerous threat to the groundwater aquifer, as there was no separation between municipal, hazardous and industrial wastes, in the area.