The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case-control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.
This study presents the use of a wavelet-based time series model to forecast the daily average particulate matter with an aerodynamic diameter of less than 10 μm (PM10) in Peninsular Malaysia. The highlight of this study is the use of a discrete wavelet transform (DWT) in order to improve the forecast accuracy. The DWT was applied to convert the highly variable PM10 series into more stable approximations and details sub-series, and the ARIMA-GARCH time series models were developed for each sub-series. Two different forecast periods, one was during normal days, while the other was during haze episodes, were designed to justify the usefulness of DWT. The models' performance was evaluated by four indices, namely root mean square error, mean absolute percentage error, probability of detection and false alarm rate. The results showed that the model incorporated with DWT yielded more accurate forecasts than the conventional method without DWT for both the forecast periods, and the improvement was more prominent for the period during the haze episodes.
Transport of fuel is essential to ensure supplies are delivered as per requested by the industrial sites or other demands. Numerous accidents have been reported and recorded in which loss of containment of hazardous chemicals occurred and led to disastrous outcomes. This paper presents the analysis of Boiling Liquid Expanding Vapour Explosion (BLEVE) due to loss of containment for Liquefied Petroleum Gas (LPG) road tankers. The main objective of this paper is to evaluate the potential consequences resulting from overpressure blast and thermal radiation of tankers carrying LPG to the people and the surrounding. The aim is also to compare the outcomes obtained from PHAST software simulator 8.11 with that of established mathematical model. Malaysia North-south Expressway (NSE) was selected as the location of the incident. The volume, weather parameters and properties of LPG were identified. It was found that the effect of BLEVE on people and structures was catastrophic. The results obtained from the mathematical model were similar with that modelled using PHAST software simulator.
Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5-15% and 5-17% compared with the GP model, 12-21% and 10-22% compared with the M5T model, and 7-15% and 5-18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models.
In the present work, supercritical fluid extraction (SFE) with CO2 as solvent and EtOH/water (v/v) as co-solvent was optimised by applying 23 factorial experimental design for the extraction of betacyanins from red pitaya fruit (Hylocereus polyrhizus) peel. Three independent variables of pressure (20-30 MPa), temperature (40-60°C) and co-solvent concentration (10-20%) were chosen for response variables. With the 2 mL/min flow rate of CO2, the dynamic time of extraction was found to be 90 min. The linear effects of main factors and interactions were evaluated. The calculated response surface model for the pressure/temperature was found to be significant for all the dependent variables. At optimal condition of SFE, the response variables were assessed as maximum extraction yield of 4.09 ± 0.69%, total betacyanins content of 25.49 ± 1.54 mg/100 mL, redness (a*) of 58.18 ± 0.82, and IC50 (antioxidant activity) of 1.34 ± 0.12 mg/mL for the experimental peel extracts. The optimal levels of independent variables were validated for the experimental responses as predicted by the mathematical model. The reliability of this method was confirmed as there was no significant difference between experimental and predicted values. The HPLC-MS profile of betacyanins extract comprised of both acylated and non-acylated betacyanins constituents.
The increasing demand for network applications, such as teleconferencing, multimedia messaging and mobile TV, which have diverse requirements, has resulted in the introduction of Long Term Evolution (LTE) by the Third Generation Partnership Project (3GPP). LTE networks implement resource allocation algorithms to distribute radio resource to satisfy the bandwidth and delay requirements of users. However, the scheduling algorithm problem of distributing radio resources to users is not well defined in the LTE standard and thus considerably affects transmission order. Furthermore, the existing radio resource algorithm suffers from performance degradation under prioritised conditions because of the minimum data rate used to determine the transmission order. In this work, a novel downlink resource allocation algorithm that uses quality of service (QoS) requirements and channel conditions to address performance degradation is proposed. The new algorithm is formulated as an optimisation problem where network resources are allocated according to users' priority, whereas the scheduling algorithm decides on the basis of users' channel status to satisfy the demands of QoS. Simulation is used to evaluate the performance of the proposed algorithm, and results demonstrate that it performs better than do all other algorithms according to the measured metrics.
Ascorbic acid was used for the first time to synthesize cellulose nanoparticles (CNP) extracted from okra mucilage. The physical properties of the CNP including their size distribution, and crystalline structures were investigated. The rheological properties of the cellulose nanofluid (CNF) were compared with the bulk okra mucilage and commercial polymer xanthan. The interfacial properties of the CNF at the interface of oil-water (O/W) system were investigated at different concentrations and temperatures. The effects of the interaction between the electrolyte and ultrasonic were determined. Core flooding experiment was conducted at reservoir condition to justify the effect of the flow behaviour and disperse phase behaviour of CNF on additional oil recovery. The performance of the CNF was compared to conventional EOR chemical. The combined method of ultrasonic, weak-acid hydrolysis and nanoprecipitation were effective in producing spherical and polygonal nanoparticles with a mean diameter of 100 nm, increased yield of 51% and preserved crystallinity respectively. The zeta potential result shows that the CNF was stable, and the surface charge signifies long term stability of the fluid when injected into oil field reservoirs. The CNF, okra and xanthan exhibited shear-thinning and pseudoplastic behaviour. The IFT decreased with increase in concentration of CNF, electrolyte and temperature. The pressure drop data confirmed the stability of CNF at 120°C and the formation of oil bank was enough to increase the oil recovery by 20%. CNF was found to be very effective in mobilizing residual oil at high-temperature high-pressure (HTHP) reservoir condition. The energy and cost estimations have shown that investing in ultrasonic-assisted weak-acid hydrolysis is easier, cost-effective, and can reduce energy consumption making the method economically advantageous compared to conventional methods.
The learning curve for doing a good qualitative study is steep because qualitative methodologies are often vague and lack explicit steps. We detail the formulation of the grounded theory approach in a study of patients with type 2 diabetes mellitus who made decisions while strategizing their treatment types. This undertaking is to demonstrate how this systematic and yet flexible methods contributed to the understanding of the issue we were investigating. The process from deciding on research objectives and research questions, follow with systematic process for data collection and analysis allows us to generate a substantive theoretical model. By paying critical attention to theoretical saturation, grounded theory approach enabled us to construct all possible explanatory concepts related to decision making in strategizing diabetes treatment. We also describe the challenges throughout the whole research journey, including getting permission to interview patients, gaining the trust of research participants and staying open to the participants’ views.
In this paper, we present a method to estimate the market parameters modelled by an asymmetric jump diffusion process. The method proposed is based on Kou's jump diffusion model while the market parameters refer to the market drift, the market volatility, the jump intensity on market price, and the rate of jump occurrence in a consistent manner throughout the entire paper. The model captures the asymmetric nature of the price fluctuation during up trend markets and down trend markets. The results are compared to conventional options to observe the impact of jump effects. The results from simulation show that the asymmetric jump diffusion model can estimate the fair prices of European call options and annuity better than the Black-Scholes model and the symmetric jump diffusion model proposed by Kou and Merton.
Introduction: Polymetabolic syndrome is a malady encompassing centralized accumulation of lipids and subsequent resistance to insulin leading towards diabesity. Currently, this condition is perilous threat to public health and also, creating perplexity for medical scientists. There is an intensive need for the development of obese rodent model having close resemblance with human metabolic syndrome (MetS); so that intricate molecular and/or therapeutic
targets can be elucidated. The resultant simulations will be beneficial to explicate not only pathogenesis but also secret conversation of signaling pathways in inducing MetS related complications in other organs. Methods: Currently, there are different methods for the development of rodent models of MetS, for instance, utilizing high lipogenic diet, genetic alterations, induction by chemicals or by combination of high fat diet and few others. In general, combination of cafeteria or western diet and low dose of streptozotocin (STZ) is a fine example of diet induced experimental model. In this model animals are allowed free access to highly palatable, energy dense, unhealthy human food for 12-18 weeks which promotes voluntary hyperphagia resulting in weight gain, increased fat mass and insulin resistance. At the end of feeding period 30mg/kg STZ is given intraperitoneally to mimic human type 2 diabetic condition.
Conclusion: Consumption of cafeteria diet with low dose STZ is considered to be the robust model of diabesity offering an exceptional stage to investigate the genomic, molecular, biochemical and cellular mechanisms of obesity and type 2 diabetes.
Kaempferol is a dietary bioflavonoid ubiquitously found in various types of plant. It possesses a wide range of medicinal properties suggesting its potential clinical utility that requires further investigation. The present review intends to highlight the efficacy of kaempferol and its molecular mechanisms of action in regulating bone metabolism. Many reports have acknowledged the bone-protecting property of kaempferol and kaempferol-containing plants using in vitro and in vivo experimental models. Kaempferol supplementation showed bone-sparing effects in newborn rats, glucocorticoid-induced and ovariectomy-induced osteoporotic models as well as bone fracture models. It achieves the bone-protective effects by inhibiting adipogenesis, inflammation, oxidative stress, osteoclastic autophagy and osteoblastic apoptosis while activating osteoblastic autophagy. The anti-osteoporotic effects of kaempferol are mediated through regulation of estrogen receptor, bone morphogenetic protein-2 (BMP-2), nuclear factor-kappa B (NF-κB), mitogen-activated protein kinase (MAPK) and mammalian target of rapamycin (mTOR) signaling pathways. In summary, kaempferol exhibits beneficial effects on skeleton, thus is potentially effective for the prophylaxis and treatment of osteoporosis.
Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT-FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.
The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.
Vibrational behaviour of symmetric angle-ply layered circular cylindrical shell filled with quiescent fluid is presented. The equations of motion of cylindrical shell in terms of stress and moment resultants are derived from the first order shear deformation theory. Irrotational of inviscid fluid are expressed as the wave equation. These two equations are coupled. Strain-displacement relations and stress-strain relations are adopted into the equations of motion to obtain the differential equations with displacements and rotational functions. A system of ordinary differential equation is obtained in one variable by assuming the functions in separable form. Spline of order three is applied to approximate the displacement and rotational functions, together with boundary conditions, to get a generalised eigenvalue problem. The eigenvalue problem is solved for eigen frequency parameter and associate eigenvectors of spline coefficients. The study of frequency parameters are analysed using the parameters the thickness ratio, length ratio, angle-ply, properties of material and number of layers under different boundary conditions.
Measurements of fragmentation functions for jets associated with an isolated photon are presented for the first time in pp and Pb-Pb collisions. The analysis uses data collected with the CMS detector at the CERN LHC at a nucleon-nucleon center-of-mass energy of 5.02 TeV. Fragmentation functions are obtained for jets with p_{T}^{jet}>30 GeV/c in events containing an isolated photon with p_{T}^{γ}>60 GeV/c, using charged tracks with transverse momentum p_{T}^{trk}>1 GeV/c in a cone around the jet axis. The association with an isolated photon constrains the initial p_{T} and azimuthal angle of the parton whose shower produced the jet. For central Pb-Pb collisions, modifications of the jet fragmentation functions are observed when compared to those measured in pp collisions, while no significant differences are found in the 50% most peripheral collisions. Jets in central Pb-Pb events show an excess (depletion) of low (high) p_{T} particles, with a transition around 3 GeV/c. This measurement shows for the first time the in-medium shower modifications of partons (quark dominated) with well-defined initial kinematics. It constitutes a new well-controlled reference for testing theoretical models of the parton passage through the quark-gluon plasma.
This paper investigated the throughput performance of a secondary user (SU) for a random primary user (PU) activity in a realistic experimental model. This paper proposed a sensing and frame duration of the SU to maximize the SU throughput under the collision probability constraint. The throughput of the SU and the probability of collisions depend on the pattern of PU activities. The pattern of PU activity was obtained and modelled from the experimental data that measure the wireless local area network (WLAN) environment. The WLAN signal has detected the transmission opportunity length (TOL) which was analyzed and clustered into large and small durations in the CTOL model. The performance of the SU is then analyzed and compared with static and dynamic PU models. The results showed that the SU throughput in the CTOL model was higher than the static and dynamic models by almost 45% and 12.2% respectively. Furthermore, the probability of collisions in the network and the SU throughput were influenced by the value of the minimum contention window and the maximum back-off stage. The simulation results revealed that the higher contention window had worsened the SU throughput even though the channel has a higher number of TOLs.
Shyama Kuruvilla and colleagues present findings across 12 country case studies of multisectoral collaboration, showing how diverse sectors intentionally shape new ways of collaborating and learning, using “business not as usual” strategies to transform situations and achieve shared goals
Gallium Nitride (GaN) is considered as the second most popular semiconductor material in industry after silicon. This is due to its wide applications encompassing Light Emitting Diode (LED) and power electronics. In addition, its piezoelectric properties are fascinating to be explored as electromechanical material for the development of diverse microelectromechanical systems (MEMS) application. In this article, we conducted a theoretical study concerning surface mode propagation, especially Rayleigh and Sezawa mode in the layered GaN/sapphire structure with the presence of various guiding layers. It is demonstrated that the increase in thickness of guiding layer will decrease the phase velocities of surface mode depending on the material properties of the layer. In addition, the Q-factor value indicating the resonance properties of surface mode appeared to be affected with the presence of fluid domain, particularly in the Rayleigh mode. Meanwhile, the peak for Sezawa mode shows the highest Q factor and is not altered by the presence of fluid. Based on these theoretical results using the finite element method, it could contribute to the development of a GaN-based device to generate surface acoustic wave, especially in Sezawa mode which could be useful in acoustophoresis, lab on-chip and microfluidics applications.
Three-dimensionally printed constructs are static and do not recapitulate the dynamic nature of tissues. Four-dimensional (4D) bioprinting has emerged to include conformational changes in printed structures in a predetermined fashion using stimuli-responsive biomaterials and/or cells. The ability to make such dynamic constructs would enable an individual to fabricate tissue structures that can undergo morphological changes. Furthermore, other fields (bioactuation, biorobotics, and biosensing) will benefit from developments in 4D bioprinting. Here, the authors discuss stimuli-responsive biomaterials as potential bioinks for 4D bioprinting. Natural cell forces can also be incorporated into 4D bioprinted structures. The authors introduce mathematical modeling to predict the transition and final state of 4D printed constructs. Different potential applications of 4D bioprinting are also described. Finally, the authors highlight future perspectives for this emerging technology in biomedicine.
In recent years, water resources management has become more complicated and controversial due to the impacts of various factors affecting hydrological systems. System Dynamics (SD) has in turn become increasingly popular due to its advantages as a tool for dealing with such complex systems. However, SD also has some limitations. This review contains a comprehensive survey of the existing literature on SD as a potential method to deal with the complexity of system integrated modeling, with a particular focus on the application of SD to the integrated modeling of water resources systems. It discusses the limitations of SD in these contexts, and highlights a number of studies which have applied a combination of SD and other methods to overcome these limitations. Finally, our study makes a number of recommendations for future modifications in the application of SD methods in order to enhance their performance.