Displaying publications 241 - 260 of 761 in total

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  1. Balasbaneh AT, Sher W, Yeoh D, Yasin MN
    Environ Sci Pollut Res Int, 2023 Feb;30(10):26964-26981.
    PMID: 36374387 DOI: 10.1007/s11356-022-24079-1
    The embodied carbon of building materials and the energy consumed during construction have a significant impact on the environmental credentials of buildings. The structural systems of a building present opportunities to reduce environmental emissions and energy. In this regard, mass timber materials have considerable potential as sustainable materials over other alternatives such as steel and concrete. The aim of this investigation was to compare the environment impact, energy consumption, and life cycle cost (LCC) of different wood-based materials in identical single-story residential buildings. The materials compared are laminated veneer lumber (LVL) and glued laminated timber (GLT). GLT has less global warming potential (GWP), ozone layer depletion (OLD), and land use (LU), respectively, by 29%, 37%, and 35% than LVL. Conversely, LVL generally has lower terrestrial acidification potential (TAP), human toxicity potential (HTP), and fossil depletion potential (FDP), respectively, by 30%, 17%, and 27%. The comparative outcomes revealed that using LVL reduces embodied energy by 41%. To identify which of these materials is the best alternative, various environmental categories, embodied energy, and cost criteria require further analysis. Therefore, the multi-criteria decision-making (MCDM) method has been applied to enable robust decision-making. The outcome showed that LVL manufacturing using softwood presents the most sustainable choice. These research findings contribute to the body of knowledge about the use of mass timber in construction.
    Matched MeSH terms: Models, Theoretical
  2. Mubashir Hayat A, Abbas M, Emadifar H, Alzaidi ASM, Nazir T, Aini Abdullah F
    PLoS One, 2024;19(5):e0296909.
    PMID: 38753667 DOI: 10.1371/journal.pone.0296909
    The time fractional Schrödinger equation contributes to our understanding of complex quantum systems, anomalous diffusion processes, and the application of fractional calculus in physics and cubic B-spline is a versatile tool in numerical analysis and computer graphics. This paper introduces a numerical method for solving the time fractional Schrödinger equation using B-spline functions and the Atangana-Baleanu fractional derivative. The proposed method employs a finite difference scheme to discretize the fractional derivative in time, while a θ-weighted scheme is used to discretize the space directions. The efficiency of the method is demonstrated through numerical results, and error norms are examined at various values of the non-integer parameter, temporal directions, and spatial directions.
    Matched MeSH terms: Models, Theoretical
  3. Azmi WFW, Mohamad AQ, Jiann LY, Shafie S
    Sci Rep, 2023 Apr 09;13(1):5799.
    PMID: 37032402 DOI: 10.1038/s41598-023-30129-6
    Nano-cryosurgery is one of the effective ways to treat cancerous cells with minimum harm to healthy adjacent cells. Clinical experimental research consumes time and cost. Thus, developing a mathematical simulation model is useful for time and cost-saving, especially in designing the experiment. Investigating the Casson nanofluid's unsteady flow in an artery with the convective effect is the goal of the current investigation. The nanofluid is considered to flow in the blood arteries. Therefore, the slip velocity effect is concerned. Blood is a base fluid with gold (Au) nanoparticles dispersed in the base fluid. The resultant governing equations are solved by utilising the Laplace transform regarding the time and the finite Hankel transform regarding the radial coordinate. The resulting analytical answers for velocity and temperature are then displayed and visually described. It is found that the temperature enhancement occurred by arising nanoparticles volume fraction and time parameter. The blood velocity increases as the slip velocity, time parameter, thermal Grashof number, and nanoparticles volume fraction increase. Whereas the velocity decreases with the Casson parameter. Thus, by adding Au nanoparticles, the tissue thermal conductivity enhanced which has the consequence of freezing the tissue in nano-cryosurgery treatment significantly.
    Matched MeSH terms: Models, Theoretical
  4. Chinnasamy S, Balasubramanian K, Sampathkumar A, Babu PK, Satchi CS
    Environ Sci Pollut Res Int, 2024 Sep;31(41):53973-53992.
    PMID: 37971583 DOI: 10.1007/s11356-023-30726-y
    Effective utilization and conservation of freshwater is a global concern due to the rapid population growth and industrial usage. To address this challenge, various approaches have been developed and implemented to convert brackish water into freshwater and meet the global water demand. This study introduces hexagram-shaped aluminum fins attached to a powder-coated basin to improve the freshwater production rate of stepped solar still. The experiment involved testing the modified stepped solar still (MSSS) equipped with hexagram fins and the conventional stepped solar still (CSSS) without hexagram fins during summer days at the Sathyamangalam location (11.49° N, 77.27° E). A mathematical model was used to analyze the performance of the solar stills, and the simulation results were validated by comparing CSSS and MSSS in terms of their freshwater production. The results indicate that the productivity of CSSS increased by 40% using hexagram fins, and the MSSS with hexagram fins produced a maximum of 4.45 l/m2 of fresh water daily. The annual performance of MSSS and CSSS in the experimental location reveals a 12.6% reduction in the payback period of the solar still due to the presence of fins. The study recommends using fins in solar stills in hot climates for efficient and cost-effective water desalination applications to achieve sustainable development objectives while reducing carbon emissions.
    Matched MeSH terms: Models, Theoretical
  5. Zhao Y, Wang T, Zhang C, Hamat B, Pang LLL
    Sci Rep, 2024 Sep 04;14(1):20550.
    PMID: 39232124 DOI: 10.1038/s41598-024-71651-5
    With the outbreak and continued spread of the COVID-19 pandemic, people's demand for daily disinfection products has increased rapidly, and its innovative design has received widespread attention. In this context, this study aims to propose a design methodology for home entrance disinfection devices based on AHP-FAST-FBS. Firstly, the design requirements of the home entrance disinfection device were collected and analyzed through in-depth interviews and the KJ method, and a hierarchical model of design demand indicators was constructed. Secondly, the Analytical Hierarchy Process (AHP) was employed to quantify these design demand indicators, and core design demands for home entrance disinfection devices were identified by weight calculations. On this basis, the Functional Analysis System Technique (FAST) method was combined to rationally transform the design demands into product functional indicators, constructing a functional system model for the home entrance disinfection device through systematic decomposition and categorization. Lastly, based on the Function-Behavior-Structure (FBS) theoretical model, the mapping of each function of the product to its structure was realized, the product structure modules were determined, and the comprehensive design and output of the innovative design scheme for the home entrance disinfection device were completed. The results of this study indicate that the design methodology combining AHP-FAST-FBS can effectively improve the scientific rigor and effectiveness of the home entrance disinfection device design, thereby generating an ideal product design scheme. This study provides systematic theoretical guidance and practical reference for designers of subsequent related disinfection products and also offers a new path for improving social health and safety.
    Matched MeSH terms: Models, Theoretical
  6. Jayson T, Bakibillah ASM, Tan CP, Kamal MAS, Monn V, Imura JI
    J Environ Manage, 2024 Sep;368:122245.
    PMID: 39173300 DOI: 10.1016/j.jenvman.2024.122245
    Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin's minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time.
    Matched MeSH terms: Models, Theoretical
  7. Zhao Z, Alli H, Ahmadipour M, Che Me R
    PLoS One, 2024;19(8):e0300266.
    PMID: 39173012 DOI: 10.1371/journal.pone.0300266
    The importance of incorporating an agile approach into creating sustainable products has been widely discussed. This approach can enhance innovation integration, improve adaptability to changing development circumstances, and increase the efficiency and quality of the product development process. While many agile methods have originated in the software development context and have been formulated based on successful software projects, they often fail due to incorrect procedures and a lack of acceptance, preventing deep integration into the process. Additionally, decision-making for market evaluation is often hindered by unclear and subjective information. Therefore, this study introduces an extended TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method for sustainable product development. This method leverages the benefits of cloud model theory to address randomness and uncertainty (intrapersonal uncertainty) and the advantages of rough set theory to flexibly handle market demand uncertainty without requiring extra information. The study proposes an integrated weighting method that considers both subjective and objective weights to determine comprehensive criteria weights. It also presents a new framework, named Sustainable Agility of Product Development (SAPD), which aims to evaluate criteria for assessing sustainable product development. To validate the effectiveness of this proposed method, a case study is conducted on small and medium enterprises in China. The obtained results show that the company needs to conduct product structure research and development to realize new product functions.
    Matched MeSH terms: Models, Theoretical
  8. Suhaizan FS, Mohd Taib A, Taha MR, Hasbollah DZA, Ibrahim A, Dan MFM, et al.
    PLoS One, 2025;20(1):e0316488.
    PMID: 39792898 DOI: 10.1371/journal.pone.0316488
    Rainfall-induced landslides are a frequent geohazard for tropical regions with prevalent residual soils and year-round rainy seasons. The water infiltration into unsaturated soil can be analyzed using the soil-water characteristic curve (SWCC) and permeability function which can be used to monitor and predict incoming landslides, showing the necessity of selecting the appropriate model parameter while fitting the SWCC model. This paper presents a set of data from six different sections of the studied slope at varying depths that are used to test the performance of three SWCC models, the van Genuchten-Mualem (vG-M), Fredlund-Xing (F-X) and Gardner (G). The dataset is obtained from field monitoring of the studied slope, over a duration of 6 months. The study discovered that the van Genuchten-Mualem model provided the best estimation based on RMSE and evaluation metric, R2 followed by Fredlund and Xing, and Gardner, however, the difference between them is minor. The R2 obtained varies as the value at the crest with 1.0 m depth has a mean of 0.44, the lowest among the overall data fitted but it also has the best RMSE value with a mean of 0.00473. Whereas the location mid-section at a depth of 1.0 m has the highest R2 with a mean of 0.97, and an average value of RMSE of 0.0145 which is the middle of the group that was fitted. This indicates that R2 measurement for model performance relies highly on the dispersion of the variables collected. The dispersion of the data set is mainly due to the sensors' inability to detect effectively at exceedingly high matric suction and zero matric suction. The investment in improving the equipment's precision will boost reliability and reduce the number of assumptions as the data is collected from the site rather than laboratory testing.
    Matched MeSH terms: Models, Theoretical
  9. Veettil SK, Schwerer L, Kategeaw W, Toth D, Samore MH, Hutubessy R, et al.
    BMJ Open, 2023 Sep 26;13(9):e071799.
    PMID: 37751952 DOI: 10.1136/bmjopen-2023-071799
    BACKGROUND: Studies assessing the indirect impact of COVID-19 using mathematical models have increased in recent years. This scoping review aims to identify modelling studies assessing the potential impact of disruptions to essential health services caused by COVID-19 and to summarise the characteristics of disruption and the models used to assess the disruptions.

    METHODS: Eligible studies were included if they used any models to assess the impact of COVID-19 disruptions on any health services. Articles published from January 2020 to December 2022 were identified from PubMed, Embase and CINAHL, using detailed searches with key concepts including COVID-19, modelling and healthcare disruptions. Two reviewers independently extracted the data in four domains. A descriptive analysis of the included studies was performed under the format of a narrative report.

    RESULTS: This scoping review has identified a total of 52 modelling studies that employed several models (n=116) to assess the potential impact of disruptions to essential health services. The majority of the models were simulation models (n=86; 74.1%). Studies covered a wide range of health conditions from infectious diseases to non-communicable diseases. COVID-19 has been reported to disrupt supply of health services, demand for health services and social change affecting factors that influence health. The most common outcomes reported in the studies were clinical outcomes such as mortality and morbidity. Twenty-five studies modelled various mitigation strategies; maintaining critical services by ensuring resources and access to services are found to be a priority for reducing the overall impact.

    CONCLUSION: A number of models were used to assess the potential impact of disruptions to essential health services on various outcomes. There is a need for collaboration among stakeholders to enhance the usefulness of any modelling. Future studies should consider disparity issues for more comprehensive findings that could ultimately facilitate policy decision-making to maximise benefits to all.

    Matched MeSH terms: Models, Theoretical
  10. Gololo AA, Veettil SK, Anantachoti P, Taychakhoonavudh S, Patikorn C
    Trop Med Int Health, 2025 Feb;30(2):71-83.
    PMID: 39743841 DOI: 10.1111/tmi.14080
    BACKGROUND: Epidemiological modelling studies in snakebite envenoming research are evolving. Their techniques can be essential in filling the knowledge gap needed to attain the World Health Organization's (WHO) goal of halving the burden of snakebite envenoming by complementing the current data scarcity. Hence, there is a need for a systematic review to summarise epidemiological models used in estimating the burden of snakebite envenoming.

    METHODS: We conducted a systematic review by searching PubMed, EMBASE, and Scopus to identify articles reporting epidemiological models in snakebite envenoming from database inception to 31st December 2023. A narrative synthesis was performed to summarise types of models, methodologies, input parameters, model outputs, and associating factors.

    RESULTS: Thirty-nine modelling studies were included from 2426 retrieved articles, comprising statistical models (76.9%) and mathematical models (23.1%). Most of the studies were conducted in South Asia, (35.9%) and Latin America (35.9%), and only a few (5.1%) were a global burden estimation. The eligible studies constructed 42 epidemiological models, of which 33 were statistical models that included regression, (60.6%) geostatistical (21.2%), and time series, (18.2%) while 9 mathematical models comprised compartmental, (44.4%) agent-based, (22.2%) transmission dynamics, (11.1%) network, (11.1%) and a simple mathematical model (11.1%). The outputs of the models varied across the study objectives. Statistical models analysed the relationship between incidence, (83.3%) mortality, (33.3%) morbidity (16.7%) and prevalence (10.0%) and their associating factors (environmental, [80%] socio-demographic [33.3%] and therapeutic [10.0%]). Mathematical models estimated incidence, (100%) mortality (33.3%), and morbidity (22.2%). Five mathematical modelling studies considered associating factors, including environmental (60%) and socio-demographic factors (40%).

    CONCLUSION: Mathematical and statistical models are crucial for estimating the burden of snakebite envenoming, offering insights into risk prediction and resource allocation. Current challenges include low-quality data and methodological heterogeneity. Modelling studies are needed, and their continued improvement is vital for meeting WHO goals. Future research should emphasise standardised methodologies, high-quality community data, and stakeholder engagement to create accurate, applicable models for prevention and resource optimization in high-burden regions, including Africa and Asia.

    Matched MeSH terms: Models, Theoretical
  11. Al-Towayti FAH, Teh HM, Ma Z, Jae IA, Syamsir A
    PLoS One, 2025;20(2):e0313955.
    PMID: 39899504 DOI: 10.1371/journal.pone.0313955
    Mangrove ecosystems and other coastal protection structures are essential barriers protecting coastal populations from the damaging effects of wave energy and increasing sea levels. This study uses a semicircular breakwater (SBW) model in an effort to develop coastal protection measures. The hydrodynamic characteristics of the SBW under random wave conditions, including the transmission coefficient, reflection coefficient, and energy loss coefficient, were thoroughly investigated using physical model experimentation. The main objectives encompass understanding the behavior of the SBW model, developing empirical equations to estimate hydraulic characteristics, and enhancing coastal protection structures to facilitate the preservation and expansion of mangrove ecosystems. Hydrodynamic features of the SBW model were assessed across a spectrum of wave conditions. Experimental testing in a wave flume encompassed a range of relative water depths (d/h), including d/h = 0.667 for an emerged SBW, d/h = 1.000 for an alternatively submerged SBW, and fully submerged conditions for d/h = 1.333 and 1.667. Wave steepness (Hi/L) varied from 0.02 to 0.06, and wave periods ranged from 0.8 to 2.5 seconds. Notably, analysis of an emerged SBW with d/h = 0.667 revealed superior wave attenuation compared to d/h = 1.000, 1.333, and 1.667 configurations.
    Matched MeSH terms: Models, Theoretical
  12. Ullah N, Shah Z, Jan R, Vrinceanu N, Farhan M, Antonescu E
    Sci Rep, 2025 Feb 20;15(1):6262.
    PMID: 39979382 DOI: 10.1038/s41598-025-90182-1
    Vector-borne infections impose a significant burden on global health systems and economies due to their widespread impact and the substantial resources required for prevention, control, and treatment efforts. In this work, we formulate a mathematical model for the transmission dynamics of a vector-borne infection with the effect of vaccination through the Atangana-Baleanu derivative. The solutions of the model are positive and bounded for positive initial values of the state variable. We presented the basic concept and theory of fractional calculus for the analysis of the model. We determine the threshold parameter, denoted by [Formula: see text], using the next-generation matrix method. The local asymptotic stability of the system at the disease-free equilibrium is analyzed. To establish the existence of solutions for the proposed model, we employ fixed-point theory. A numerical scheme is developed to visualize the system's dynamical behavior under varying input parameters. Numerical simulations are conducted to illustrate how these parameters influence the dynamics of the system. The results highlight key factors affecting the transmission and control of vector-borne diseases, offering insights into strategies for prevention and mitigation.
    Matched MeSH terms: Models, Theoretical
  13. Hlayel M, Mahdin H, Hayajneh M, AlDaajeh SH, Yaacob SS, Rejab MM
    PLoS One, 2024;19(12):e0314691.
    PMID: 39700470 DOI: 10.1371/journal.pone.0314691
    The rapid development of Digital Twin (DT) technology has underlined challenges in resource-constrained mobile devices, especially in the application of extended realities (XR), which includes Augmented Reality (AR) and Virtual Reality (VR). These challenges lead to computational inefficiencies that negatively impact user experience when dealing with sizeable 3D model assets. This article applies multiple lossless compression algorithms to improve the efficiency of digital twin asset delivery in Unity's AssetBundle and Addressable asset management frameworks. In this study, an optimal model will be obtained that reduces both bundle size and time required in visualization, simultaneously reducing CPU and RAM usage on mobile devices. This study has assessed compression methods, such as LZ4, LZMA, Brotli, Fast LZ, and 7-Zip, among others, for their influence on AR performance. This study also creates mathematical models for predicting resource utilization, like RAM and CPU time, required by AR mobile applications. Experimental results show a detailed comparison among these compression algorithms, which can give insights and help choose the best method according to the compression ratio, decompression speed, and resource usage. It finally leads to more efficient implementations of AR digital twins on resource-constrained mobile platforms with greater flexibility in development and a better end-user experience. Our results show that LZ4 and Fast LZ perform best in speed and resource efficiency, especially with RAM caching. At the same time, 7-Zip/LZMA achieves the highest compression ratios at the cost of slower loading. Brotli emerged as a strong option for web-based AR/VR content, striking a balance between compression efficiency and decompression speed, outperforming Gzip in WebGL contexts. The Addressable Asset system with LZ4 offers the most efficient balance for real-time AR applications. This study will deliver practical guidance on optimal compression method selection to improve user experience and scalability for AR digital twin implementations.
    Matched MeSH terms: Models, Theoretical
  14. Govindan SS, Agamuthu P
    Waste Manag Res, 2014 Oct;32(10):1005-14.
    PMID: 25323145 DOI: 10.1177/0734242X14552551
    Waste management can be regarded as a cross-cutting environmental 'mega-issue'. Sound waste management practices support the provision of basic needs for general health, such as clean air, clean water and safe supply of food. In addition, climate change mitigation efforts can be achieved through reduction of greenhouse gas emissions from waste management operations, such as landfills. Landfills generate landfill gas, especially methane, as a result of anaerobic degradation of the degradable components of municipal solid waste. Evaluating the mode of generation and collection of landfill gas has posted a challenge over time. Scientifically, landfill gas generation rates are presently estimated using numerical models. In this study the Intergovernmental Panel on Climate Change's Waste Model is used to estimate the methane generated from a Malaysian sanitary landfill. Key parameters of the model, which are the decay rate and degradable organic carbon, are analysed in two different approaches; the bulk waste approach and waste composition approach. The model is later validated using error function analysis and optimum decay rate, and degradable organic carbon for both approaches were also obtained. The best fitting values for the bulk waste approach are a decay rate of 0.08 y(-1) and degradable organic carbon value of 0.12; and for the waste composition approach the decay rate was found to be 0.09 y(-1) and degradable organic carbon value of 0.08. From this validation exercise, the estimated error was reduced by 81% and 69% for the bulk waste and waste composition approach, respectively. In conclusion, this type of modelling could constitute a sensible starting point for landfills to introduce careful planning for efficient gas recovery in individual landfills.
    Matched MeSH terms: Models, Theoretical*
  15. Chun TS, Malek MA, Ismail AR
    Water Sci Technol, 2015;71(4):524-8.
    PMID: 25746643 DOI: 10.2166/wst.2014.451
    The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.
    Matched MeSH terms: Models, Theoretical*
  16. Noor Rodi NS, Malek MA, Ismail AR, Ting SC, Tang CW
    Water Sci Technol, 2014;70(10):1641-7.
    PMID: 25429452 DOI: 10.2166/wst.2014.420
    This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction.
    Matched MeSH terms: Models, Theoretical*
  17. Megat Hasnan MM, Mohd Sabri MF, Mohd Said S, Nik Ghazali NN
    ScientificWorldJournal, 2014;2014:912683.
    PMID: 25165751 DOI: 10.1155/2014/912683
    This paper presents the design and evaluation of a high force density fishbone shaped electrostatic comb drive actuator. This comb drive actuator has a branched structure similar to a fishbone, which is intended to increase the capacitance of the electrodes and hence increase the electrostatic actuation force. Two-dimensional finite element analysis was used to simulate the motion of the fishbone shaped electrostatic comb drive actuator and compared against the performance of a straight sided electrostatic comb drive actuator. Performances of both designs are evaluated by comparison of displacement and electrostatic force. For both cases, the active area and the minimum gap distance between the two electrodes were constant. An active area of 800 × 300 μm, which contained 16 fingers of fishbone shaped actuators and 40 fingers of straight sided actuators, respectively, was used. Through simulation, improvement of drive force of the fishbone shaped electrostatic comb driver is approximately 485% higher than conventional electrostatic comb driver. These results indicate that the fishbone actuator design provides good potential for applications as high force density electrostatic microactuator in MEMS systems.
    Matched MeSH terms: Models, Theoretical*
  18. Al-Gumaei YA, Noordin KA, Reza AW, Dimyati K
    PLoS One, 2014;9(10):e109077.
    PMID: 25286044 DOI: 10.1371/journal.pone.0109077
    Interference resulting from Cognitive Radios (CRs) is the most important aspect of cognitive radio networks that leads to degradation in Quality of Service (QoS) in both primary and CR systems. Power control is one of the efficient techniques that can be used to reduce interference and satisfy the Signal-to-Interference Ratio (SIR) constraint among CRs. This paper proposes a new distributed power control algorithm based on game theory approach in cognitive radio networks. The proposal focuses on the channel status of cognitive radio users to improve system performance. A new cost function for SIR-based power control via a sigmoid weighting factor is introduced. The existence of Nash Equilibrium and convergence of the algorithm are also proved. The advantage of the proposed algorithm is the possibility to utilize and implement it in a distributed manner. Simulation results show considerable savings on Nash Equilibrium power compared to relevant algorithms while reduction in achieved SIR is insignificant.
    Matched MeSH terms: Models, Theoretical*
  19. Sookhak M, Akhunzada A, Gani A, Khurram Khan M, Anuar NB
    ScientificWorldJournal, 2014;2014:269357.
    PMID: 25121114 DOI: 10.1155/2014/269357
    Cloud computing is a significant shift of computational paradigm where computing as a utility and storing data remotely have a great potential. Enterprise and businesses are now more interested in outsourcing their data to the cloud to lessen the burden of local data storage and maintenance. However, the outsourced data and the computation outcomes are not continuously trustworthy due to the lack of control and physical possession of the data owners. To better streamline this issue, researchers have now focused on designing remote data auditing (RDA) techniques. The majority of these techniques, however, are only applicable for static archive data and are not subject to audit the dynamically updated outsourced data. We propose an effectual RDA technique based on algebraic signature properties for cloud storage system and also present a new data structure capable of efficiently supporting dynamic data operations like append, insert, modify, and delete. Moreover, this data structure empowers our method to be applicable for large-scale data with minimum computation cost. The comparative analysis with the state-of-the-art RDA schemes shows that the proposed scheme is secure and highly efficient in terms of the computation and communication overhead on the auditor and server.
    Matched MeSH terms: Models, Theoretical*
  20. Chun TS, Malek MA, Ismail AR
    Environ Sci Process Impacts, 2014 Sep 20;16(9):2208-14.
    PMID: 25005632 DOI: 10.1039/c4em00282b
    Effluent discharge from septic tanks is affecting the environment in developing countries. The most challenging issue facing these countries is the cost of inadequate sanitation, which includes significant economic, social, and environmental burdens. Although most sanitation facilities are evaluated based on their immediate costs and benefits, their long-term performance should also be investigated. In this study, effluent quality-namely, the biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solid (TSS)-was assessed using a biomimetics engineering approach. A novel immune network algorithm (INA) approach was applied to a septic sludge treatment plant (SSTP) for effluent-removal predictive modelling. The Matang SSTP in the city of Kuching, Sarawak, on the island of Borneo, was selected as a case study. Monthly effluent discharges from 2007 to 2011 were used for training, validating, and testing purposes using MATLAB 7.10. The results showed that the BOD effluent-discharge prediction was less than 50% of the specified standard after the 97(th) month of operation. The COD and TSS effluent removals were simulated at the 85(th) and the 121(st) months, respectively. The study proved that the proposed INA-based SSTP model could be used to achieve an effective SSTP assessment and management technique.
    Matched MeSH terms: Models, Theoretical*
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