Integrated Scheduled Waste Management System in Kuala Lumpur Using Expert System

Over the past decade, Malaysia has enjoyed tremendous growth in its economy. This has brought about a population growth along with a great influx of foreign workforce to cities. This resulted in an increase in the amount of waste scheduled generated. Furthermore, scheduled waste management has long been a problem area for local authorities in Kuala Lumpur. Continued illegal dumping by waste generators is being practiced at large scale due to lack of proper guidance and awareness. This report reviewed and discussed about service provided for scheduled waste management by an authority and international scenario of scheduled waste management. An expert system was developed to integrate scheduled waste management in Kuala Lumpur. The knowledge base was acquired through journals, books, magazines, annual report, and web sites. An object oriented expert system shell, Microsoft Visual Basic 2005 Express Edition was used as the building tools for the prototype development. The overall development of this project has been carried out in several phases which are problem identification, problem statement and literature review, identification of domain experts, prototype development, knowledge acquisition, knowledge representation and prototype development. Scheduled waste expert system is developed based on five types of scheduled waste management which are label requirements, packaging requirements, impact of scheduled wastes, recycling of scheduled wastes, and recommendations. Besides, it contains several sub modules by which the user can obtain a comprehensive background of the domain. The output is to support effective integrated scheduled waste management

Performance Measures in Acousto-optic Chaotic Signal Encryption System Subject to Parametric Variations and Additive Noise

Signal encryption and recovery using chaotic optical waves has been a subject of active research in the past 10 years. Since an acousto-optic Bragg cell with zeroth- and first-order feedback exhibits chaotic behavior past the threshold for bistability, such a system was recently examined for possible chaotic encryption using a low-amplitude sinusoidal signal applied via the bias input of the sound cell driver. Subsequent recovery of the message signal was carried out via a heterodyne strategy employing a locally generated chaotic carrier, with threshold parameters matched to the transmitting Bragg cell. The simulation results, though encouraging, were limited to relatively low chaos frequencies and sinusoidal message signals only. In this paper, we extend the previous work by (i) increasing the chaos frequency using appropriate parameter control; (ii) carefully examining the system sensitivity to three system parameters, viz., feedback delay, feedback gain, and dc bias level; (iii) examine signal recoverability relative to shifts in the three parameters mentioned above relative to the transmitter; and (iv) determining the robustness of such a system relative to the primary transmitter parameters. Additionally, we consider the effect of the additive bandpass noise (obtained from white Gaussian noise in the simulator) on signal recovery in such a system from a performance standpoint. It is also conjectured that signal recovery can be effected by passing the modulated light through a second sound cell in a matched chaotic regime. This aspect is also under investigation

Design of Acousto-optic Chaos Based Secure Free-space Optical Communication Links

We discuss the design of an acousto-optic cell based free space optical communication link where the data beam is made secure through chaos encryption. Using external signal modulation of the diffracted light from a hybrid acousto-optic cell chaos (or directly via incorporation in the sound-cell driver\u27s bias voltage) encryption of data is possible. We have shown numerically that decryption of the encoded data is possible by using an identical acousto-optic system in the receiver

Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq

Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods

Optimal sizing design and operation of electrical and thermal energy storage systems in smart buildings

Photovoltaic (PV) systems in residential buildings require energy storage to enhance their productivity; however, in present technology, battery storage systems (BSSs) are not the most cost-effective solutions. Comparatively, thermal storage systems (TSSs) can provide opportunities to enhance PV self-consumption while reducing life cycle costs. This paper proposes a new framework for optimal sizing design and real-time operation of energy storage systems in a residential building equipped with a PV system, heat pump (HP), thermal and electrical energy storage systems. For simultaneous optimal sizing of BSS and TSS, a particle swarm optimization (PSO) algorithm is applied to minimize daily electricity and life cycle costs of the smart building. A model predictive controller is then developed to manage energy flow of storage systems to minimize electricity costs for end-users. The main objective of the controller is to optimally control HP operation and battery charge/discharge actions based on a demand response program. The controller regulates the flow of water in the storage tank to meet designated thermal energy requirements by controlling HP operation. Furthermore, the power flow of battery is controlled to supply all loads during peak-load hours to minimize electricity costs. The results of this paper demonstrate to rooftop PV system owners that investment in combined TSS and BSS can be more profitable as this system can minimize life cycle costs. The proposed methods for optimal sizing and operation of electrical and thermal storage system can reduce the annual electricity cost by more than 80% with over 42% reduction in the life cycle cost. Simulation and experimental results are presented to validate the effectiveness of the proposed framework and controller

Preparation and in vitro evaluation of synthetic high-density lipoproteins as parenteral drug delivery system for tamoxifen citrate

The aim of this study was to develop a bioinspired drug delivery system for tamoxifen citrate (TC) based on synthetic high density lipoproteins (sHDL). For this purpose, sHDL nanoparticles were prepared from a mimetic peptide (5A peptide) and different lipids using thin film hydration method followed by sonication and thermal cycling. Various formulation parameters including lipid composition, lipid to peptide ratio, and drug to carrier ratio had a remarkable impact on the properties and the release pattern of the nanoparticles. The optimized formula (F14) displayed a spherical morphology, average diameter of (35.7±12.4) nm, and a zeta potential (ζ) equals to (-48.4± 0.5) mV. The encapsulation efficiency and drug loading of F14 were (96.5±0.7%) and (9.65±0.1%), respectively. Besides, F14 showed a good stability in human plasma for 24 hours. The encapsulation of the lipophilic drug within the hydrophobic core of the nanocarrier enabled a slow drug release from nanoparticles which follows a near zero order controlled mechanism. The promising results of this study opens an avenue for using sHDL as a delivery system for administration of TC intravenously. Therefore, the optimized formula is suggested to be subject for future analyses in terms of in vitro cytotoxicity against breast cancer cells and in vivo evaluation in tumor bearing animals

Modelling and simulation of energy-saving potential of Sequential Batch Reactor (SBR) in the abatement of ammoniacal-nitrogen and organics

The ammonical nitrogen removal in discharged effluents from a typical sewerage treatment plant has not been consistent with the stringent discharge standards. There is the need to optimize the energy consumption as well as improve the ammonical nitrogen removal in the treatment plant. This study reports the investigation of the behaviour of process dynamicity of ammoniacalnitrogen (NH3-N) removal in a Sequencing Batch Reactor (SBR) through Activated Sludge Model No.1 (ASM1) and standard SBR design computation for optimal aeration time, while meeting the treatment requirements. Thestudy further evaluates the performance of NH3-N removal based on the data obtained from an existing SBR system. The time profile of process dynamics and the minimum required aeration time with maximum nitrogen removal was studied while taking into account the system’s energy consumption. Moreover, the simulation results by MATLAB Software suggested that the process dynamicity of the carbon and NH3-N concentration is 7 hour batch time with one fill and 1.5 hours aeration time. For computation of SBR standard design, the reduction from current 1.5 hours to 1.35 hours of aeration for 80% to 93% of NH3-N removal brought about the total energy saving of up to 10 percent

Cake compressibility analysis of BPOME from a hybrid adsorption microfiltration process

This study investigates the utility of a hybrid adsorption-membrane process for cake compressibility evaluation of biotreated palm oil mill effluent. A low-cost empty fruit bunch (EFB) based powdered activated carbon (PAC) was employed for the upstream adsorption process with operation conditions of 60 g/L PAC dose, 68 min mixing time, and 200 rpm mixing speed to reduce the feed-water strength and alleviate probable fouling of the membranes. Two polyethersulfone microfiltration (MF) membranes of 0.1 and 0.2 lm pore sizes were investigated under constant transmembrane pressures (TMP) of 40, 80, and 120 kPa. The compressibility factors (z), which was obtained from the slopes of power plots (function of specific cake resistance (a) and pressure gradient) were evaluated. The z values of 0.32 and 0.52, respectively obtained, for the 0.1 and 0.2 lm MF membranes provided compressible and stable z values as observed from their power plots. Besides, these membranes were found suitable for the measurement of z since the results are in consonance with the established principle of cake compressibility. Moreover, the upstream adsorption mitigated the clogging of the membranes which ultimately led to moderate resistances and cake compressibility. These are indications that with the secondary cake filtration, a sustainable flux can be achieved during BPOME filtration. The membranes exhibited close to 100% restoration after cleaning

Bioimaging of C2C12 Muscle Myoblasts Using Fluorescent Carbon Quantum Dots Synthesized From Bread

Biocompatible carbon quantum dots (CQDs) have recently attracted increased interest in biomedical imaging owing to their advantageous photoluminescence properties. Numerous precursors of fluorescent CQDs and various fabrication procedures are also reported in the literature. However; the use of concentrated mineral acids and other corrosive chemicals during the fabrication process curtails their biocompatibility and severely limits the utilization of the products in cell bio-imaging. In this study; a facile; fast; and cost-effective synthetic route is employed to fabricate CQDs from a natural organic resource; namely bread; where the use of any toxic chemicals is eliminated. Thus; the novel chemical-free technique facilitated the production of luminescent CQDs that were endowed with low cytotoxicity and; therefore; suitable candidates for bioimaging sensors. The above mentioned amorphous CQDs also exhibited fluorescence over 360-420 nm excitation wavelengths; and with a broad emission range of 360-600 nm. We have also shown that the CQDs were well internalized by muscle myoblasts (C2C12) and differentiated myotubes; the cell lines which have not been reported before

Real-time multiscale monitoring and tailoring of graphene growth on liquid copper

The synthesis of large, defect-free two-dimensional materials (2DMs) such as graphene is a major challenge toward industrial applications. Chemical vapor deposition (CVD) on liquid metal catalysts (LMCats) is a recently developed process for the fast synthesis of high-quality single crystals of 2DMs. However, up to now, the lack of in situ techniques enabling direct feedback on the growth has limited our understanding of the process dynamics and primarily led to empirical growth recipes. Thus, an in situ multiscale monitoring of the 2DMs structure, coupled with a real-time control of the growth parameters, is necessary for efficient synthesis. Here we report real-time monitoring of graphene growth on liquid copper (at 1370 K under atmospheric pressure CVD conditions) via four complementary in situ methods: synchrotron X-ray diffraction and reflectivity, Raman spectroscopy, and radiation-mode optical microscopy. This has allowed us to control graphene growth parameters such as shape, dispersion, and the hexagonal supra-organization with very high accuracy. Furthermore, the switch from continuous polycrystalline film to the growth of millimeter-sized defect-free single crystals could also be accomplished. The presented results have far-reaching consequences for studying and tailoring 2D material formation processes on LMCats under CVD growth conditions. Finally, the experimental observations are supported by multiscale modeling that has thrown light into the underlying mechanisms of graphene growth.Catalysis and Surface Chemistr