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The ability to forecast the quantity of municipal solid waste (MSW) is critical for long-term coordination of MSW. Forecasting the amount of MSW is often difficult due to the lack of data, and even when data is available, it is frequently inaccurate. Therefore, planning and implementing sustainable solid waste management strategies is important to determine the accuracy of solid waste generation’s prediction. With regards to the situation, waste prediction models have been conducted to verify the effectiveness of the models towards the prediction of solid waste generation. As one of the most effective non-linear models, the Artificial neural network (ANN) model has been effectively utilized in the prediction of municipal solid waste at the Jeram Sanitary Landfill in Selangor’s state. Datasets of solid waste generation, population, number of trash truck trips, and oil price index were used as input to the model for 114 weeks between 2018 and 2020. The generated models' efficiency was measured using the mean square error (MSE) and coefficient of regression value (R-square). Both measurements showed a good accuracy with the lowest value of MSE at 6379.6, and high value of R-square at 0.91585. Based on the data from 2018 to 2020, The Jeram Sanitary Landfill is expected to last 9.6 years, according to the ANN model. The current study contributes in forecasting and allocating crucial resources that will be necessary in the future for effective solid waste management, as well as exploring alternate approaches to achieving longterm objectives. [ABSTRACT FROM AUTHOR]

Port State Control (PSC) inspection data is used for determining the inspection pattern of PSC in Malaysia and identifying the relationship between the inspection place, flag state, number of deficiency, detention result, and ship risk profile. Based on 8,089 inspection reports from 2015 to 2019, the mining association rule is proposed as a learning approach due to its determination pattern in the information bank. The learning of association rules of PSC inspections is performed primarily on the Apriori Algorithm, in order to produce alluring rules. Inspection patterns of Malaysian ports revealed that flag state, ship risk profile, and inspection place generally lead to no detention result, as well as zero deficiency recorded on-board. The reported quantity of detention was significantly related to the high number of deficiencies raised for ships registered under blacklisted countries. Furthermore, the analysis of deficiency discovered the pattern of inspection at Malaysian ports is frequently related to zero and a low number of deficiencies raised by inspectors. Lastly, five major ports were selected for providing a useful rule to help PSC officers in organising an effective inspection plan. A similar approach can also be used for other ports beyond Malaysia for comparative analysis. [ABSTRACT FROM AUTHOR]

Shell structures are usually used in roof applications and aerospace structures. The advantage of these structures is not fully utilized in transmitting applied force. A thin-shell structure can be used for building construction and aerospace structures due to its lightweight nature. The township high load in this study refers to the high-speed airflow in the case of aerospace. This study was conducted to justify the feasibility of applying a thin-shell structure as an elevated shell platform for township foundations, where several different geometries were analyzed using finite element analysis (FEA) and artificial neural network (ANN). In the findings, all maximum stresses from different geometries are lower than the design value, thus verifying the feasibility of constructing a shell platform for heavy loading applications. The regression plots from the ANN output show that all geometries approached the value of 1, which means that the predicted ANN output and actual datasets of FEA are almost similar. From the ANN predicted output, the mean square error (MSE) was calculated. All the MSE values obtained from each geometry approached zero, indicating the analysis is precise and has a minimal error. Thus, this study has justified the feasibility of proposing elevated shells for township applications, which can be used as a reference in the design phase for future implementation. [ABSTRACT FROM AUTHOR]

The ripening stage is a stage where the fruit is ready to be harvested. During ripening, pectin activity is observed to trigger parenchyma cell wall middle lamella dissolution of a fruit. Additionally, the ripening stage also affects the changing appearance of the fruit. Thus, this research aims to develop a classification model based on ANN that can predict or classify the ripening stage based on either pectin activity or fruit appearance. The study will focus specifically on Ficus carica (fig). To achieve the objective, the researchers developed two Multilayer Perceptron (MLP) models: figNN and pectinNN. We trained figNN using features extracted from images of figs with different ripening stages, and pectinNN with a set of the statistical value of pectin activity such as weight (W), brix of sugar (BS), extraction yield (EY), and degree of esterification (ED) from 30 figs with varying degree of ripening. From the result of this research, figNN and pectinNN can distinguish the ripening stage based on either the chemical properties or the images. Furthermore, we can also show that the image-based classification is more accurate than the pectin-based classification. For future work, the study of the correlation between pectin and image features is highly encouraged. [ABSTRACT FROM AUTHOR]

Optimization means the mathematical determination of the optimal decisions out of diverse alternatives. Based on the preceding Finite Element Analysis (FEA), a proposed shell produced a maximum stress that exceeded the design value. To make the design feasible, an optimization was done to minimize the maximum stress by using the gradient method. The performance of the structure can be optimized to fulfil the design requirements with the optimum value of displacement to achieve the objective function. The results show that the optimum displacement is 8.8mm, which reduced the maximum stress by 99.94% from the initial level. Thus, optimization methods are important for new applications of shell structures to find their best parameters in the design stage. [ABSTRACT FROM AUTHOR]