The overarching goal of the Medical Waste Management Plan is to avoid and/or reduce the detrimental impacts of medical waste on human health and the environment. Design is the method through which humans achieve their...
The overarching goal of the Medical Waste Management Plan is to avoid and/or reduce the detrimental impacts of medical waste on human health and the environment. Design is the method through which humans achieve their intended results. The design of the Smart waste container is quite sophisticated since the Smart waste container operates in a very complicated environment. To acquire the best and most suitable design of Smart waste container to withstand various stresses and pressures, finite element analysis with Solidworks suggests the minimal design standard. Pro/Engineer Solidworks 2018 for finite element analysis is used for this purpose. Optimization is a procedure that delivers the optimum choice and outcome. Following observation and modelling, an optimization study was carried out. It is concerned with weight comparison and minimizing the size of the Smart waste container by analyzing numerous elements that impact performance, such as stress. The purpose of doing this research was to demonstrate the Container behaviour impacted by fatigue phenomena owing to fatigue tests and to examine the results for additional time and cost, as two extremely important metrics related to production. The analysis shows that with totally continual loading, it is possible to predict the lifespan of a Smart Waste container but also identify the crucial spots when crack formation is most likely to occur. The fatigue results we obtained for the improved design were Alternating Stress (1.72E+05 N/m^2) Cycles 1.00E+06 Which shows that the design is good and has a long service life.
Several statistical distributions are used in generating synthetic temperature data such as normal, gamma and others distribution. However, selecting a best marginal distribution from a variety of distribution is time...
Several statistical distributions are used in generating synthetic temperature data such as normal, gamma and others distribution. However, selecting a best marginal distribution from a variety of distribution is time consuming. If the marginal distribution is misidentified, it led to the underestimation of multivariate model. Hence, this synthetic temperature data failed to represent the actual temperature data. Therefore, a more accurate and general application of probability distribution and a study on how to reduce the discrepancy between variance of observed and synthetic data are needed. In this study, the values of the synthetic variances are observed by incorporating the appropriate level of dependence among the individual monthly amount. Three selected meteorological stations which are Alor Setar, Bayan Lepas and Chuping are used from January 1994 to December 2017. The gamma distribution is proposed to fit the marginal distribution which will be used later to transform the data to uniform unit formed. The correlation coefficient is calculated. Next, the synthetic data will be generated using skew-t copula and the correlation coefficient will be noted. The correlation coefficient of the observed data will be matched to the correlation coefficient of the synthetic data. The result shows gamma distribution is suitable for modelling the marginal distribution of the temperature data. It is found that the skew-t copula can be used to generate synthetic temperature data that is close to the observed data with strong correlation values between the temperature stations. It also shown that the skew-t copula is ideal for modelling synthetic temperature data for strong correlated stations. The synthetic generation of temperature data is important in cases where data is limited or unavailable. The copula model is also expected to reflect approximately real situation of the temperature data in Malaysia that can support flood risk management.
This paper provides preliminary discourse on buzz words about Industry 4.0 and Society 5.0. This discourse focuses on the lens of Condition Based Maintenance (CBM) and Machine Learning of Artificial Intelligence (MLAI...
This paper provides preliminary discourse on buzz words about Industry 4.0 and Society 5.0. This discourse focuses on the lens of Condition Based Maintenance (CBM) and Machine Learning of Artificial Intelligence (MLAI). To some extent several companies have embarked Industry 4.0 and Society 5.0 within Internet of Things (IoT) technology. Through the wave of IoT Technology, Industries are adopting automated machinery. Predictive maintenance (PM) is indispensable not only toward the machines' vitality and longevity purpose, but also toward the human error reduction. This paper elaborates its discourse of Industry 4.0 and Society through the lens of CBM and MLAI. The mentioned Machine Learning, in this paper, refers to research methodology, as methodological frameworks. Those frameworks comprise several phases, which are: 1. Equipment Analysis; 2. Data Evaluation; 3. Data Selection and Process; 4. Modeling; 5. Decision Support Model Evaluation. The MLAI techniques are based upon the identification of behaviour patterns. This identification comprises datasets that exclude mathematical models or prior historical knowledge. The discourse in this paper intertwines CBM process and MLAI through data cleaning and processing, features stratification and extraction, model stratification and validation. This paper elaborates two renowned maintenance approaches which are preventive and corrective maintenance. Discourse in this paper focuses on corrective action, known as predictive maintenance (PM), or condition based maintenance (CBM) within Reliability Centered Maintenance (RCM). CBM is chosen as the most desirable strategy, as it involves the intervention as the consequence of the machine breakdown. It also provides cost savings toward spare parts consumption, and optimizes production.
Ground-level ozone (O3) is a major air pollutant that can have significant impacts on human health, ecosystems well-being, and agricultural productivity. This study aims to map the spatial distribution of extreme O3 i...
Ground-level ozone (O3) is a major air pollutant that can have significant impacts on human health, ecosystems well-being, and agricultural productivity. This study aims to map the spatial distribution of extreme O3 in Peninsular Malaysia using stationary and nonstationary Generalized Extreme Value (GEV) models. The models are applied to air quality data collected from 24 air monitoring stations across the region between 2000 and 2016. The stationary GEV model assumes that the distribution of extreme O3 values is constant for all parameters while the nonstationary GEV model allows for cyclic effect on location parameters to capture trends or changes in the underlying distribution while other parameters remain constant. The results show that both stationary and nonstationary GEV models perform well in terms of goodness-of-fit statistics using probability plotting method. Maps generated by the stationary and nonstationary GEV model reveal significant spatial variation in extreme O3 concentrations across the region, with hotspots in urban areas and near major industrial facilities. The findings provide important information for policymakers and other stakeholders working to mitigate the impacts of air pollution in Peninsular Malaysia and demonstrate the use of extreme value theory techniques in modelling spatial distribution of extreme environmental events.
Asteroids have been observed both from the ground and through space missions for decades, which accumulated large amount of their observational data. These data are used to estimate the sizes, orbits, and even possibl...
Asteroids have been observed both from the ground and through space missions for decades, which accumulated large amount of their observational data. These data are used to estimate the sizes, orbits, and even possible chemical compositions of asteroids. Even though the chemical composition is generally difficult to be accurately determined without a sample return or in-situ observation by a spacecraft, asteroids are classified based on their reflectance spectra, which are compared with those of meteorites, which are known to be mostly originated from asteroids. This scheme works reasonably well for some asteroid types, but others, mostly featureless ones in reflectance spectra, remained controversial due to the fact that the observational data of asteroids and measured data of meteorites are different in terms of the data coverage, precision and resolution. Our aim is to connect asteroids with meteorites based on sparse modelling in order to search for the optimal integration scheme for two different databases without relying on preliminary knowledge. For the above purpose, we develop large databases of asteroids and meteorites for easy application of sparse modelling. Through our analyses including principal component analysis, Bayesian spectral deconvolution and dimensionality reduction, we found that our data-driven approach can extract potential information without using empirical knowledge. Our methods show a new type of data handling scheme for asteroid and meteorite data, potentially having a significant contribution for future missions.
Reactive Green (RG19) is one of azo dye that potentially hazardous towards human due to highly recalcitrant to degrade and still lack of effective treatments. This study introduced a significant study since numerous c...
Reactive Green (RG19) is one of azo dye that potentially hazardous towards human due to highly recalcitrant to degrade and still lack of effective treatments. This study introduced a significant study since numerous conventional treatment processes were not capable of removing that azo dye in fast and efficient process. Therefore, increase strong potential of sulfate and hydroxyl radical resulting in an improvement towards Advanced Oxidation Processes (AOPs) which is ozonation process has been proposed to degrade RG19 dye efficiently. Ozonation (O3) and Ozone/Persulfate (O3/S2O82-) processes were tested either can be a standalone process or need a better combination of a catalyst which is sodium persulfate (Na2S2O8). The efficiency of dye degradation as follows: colour removal, chemical oxygen demand (COD) and the presence of organic molecules. The efficiency colour removal with O3 reached 75% while (O3/S2O82-) reached 85% at similar reaction time. Also, the average rate of efficiency COD removal (O3/S2O82-) yielded the highest 27.82% whereas O3 reached only 10%. After that, the effects of operational conditions had been investigated in (O3/S2O82-) process including the fixed initial concentration of the dyes, initial pH of the RG19 (2-6), Na2S2O8 concentration (25-65 mM) and contact time (3-25 min) on the colour and COD removal efficiency. Central composite design (CCD) has been applied to achieve the optimization of (O3/S2O82-) was resulting (Colour removal; R2 = 0.900, COD removal; R2 = 0.508). Hence, the optimum conditions of the process at (pH 8, 40 mM, 14 min) and can be shown specifically by mathematicalmodelling equation also based on interactive effect by 3D contour plot. This overall result indicates that (O3/S2O82-) process enhances a synergistic effect that could be observed in structural changes of dye molecule along RG19 degradation.
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