In this paper a fully reused VLSI architecture of FM0/Manchester encoding technique for memory application has been proposed.In this paper we are encoding the 1 bit data into 16 bit data and storing it into a memory of certain address location given by the linear feedback shift register (LFSR),whose input is taken from the pseudo random sequence generator (PRSG).The encoded 16 bit data is stored into memory controller; the encoded data is decoded back into 1 bit data under the condition: when MSB bit is at logic state 1.By using FM0/Manchester encoding and decoding technique, the data will be secure , this process is easy and faster to carry out.This paper develops a fully reused VLSI architecture, and also exhibits an efficient performance.

Streamflow prediction is crucial for effective water resource management and flood prediction. Therefore, this study aims to predict streamflow within the Klang river catchment. Two machine learning approaches, namely artificial neural network (ANN) and support vector machine (SVM), were employed to forecast streamflow within the Klang river catchment. The performance of each model was evaluated using mean absolute error (MAE), root mean square error (RMSE), and percentage error. SVM outperformed ANN in streamflow prediction, achieving the lowest values of MAE, RMSE and percentage error, recorded as 7.23, 9.03 and 19.24, respectively. The model was then used to run the future scenarios, under two shared socioeconomic pathways (SSPs), which are SSP2-4.5 and SSP5-8.5, from coupled model intercomparison project phase 6 (CMIP6). SSP5-8.5 displays greater fluctuations than SSP2-4.5. This heightened variability evident in SSP5-8.5 can be attributed to its premise of rapid population expansion, significant technological advancements, and inadequate measures to address environmental issues. Consequently, these factors contribute to more frequent occurrences of extreme climate events.

Response Surface Methods (RSM) and desirability functions are good examples of data-based multivariate methods that can be used to study coagulation treatment optimization. This study examined and optimized the efficacy of turbidity reduction, under the operating conditions of initial turbidity Tin (50-200 NTU), pH (5-9), and alum dose (50-250 mg/L) for two types of fan (two paddles and four paddles) using Face-Centered Central Composite Design F-CCCD of RSM. The experimental results of F-CCCD were fitted to the second-order quadratic model for the two types of fans in order to formulate the impacts of each element and their interactions on the response of interest in a mathematical connection. The results showed that at optimal operating conditions for a fan with two paddles of Tin, pH and alum dose of 87.76 NTU, 8.86 and 170.43 mg/L, respectively, the predicted values of turbidity removal efficiency (E%) was 85.92% and for four paddles fan type when Tin, pH and alum dose of 156.58 NTU, 8.93, and 138.7 mg/L, respectively, the predicted values of turbidity removal efficiency (E%) was 93.71, with the desirability of 1.000. This study showed how well F-CCCD works with a desirability function to find the best conditions for the process (Tin, pH, and alum dosage) of coagulation for the turbidity removal efficiency E%. A four-paddle fan type provided the best removal efficiency. Instead of jar testing, drinking water treatment companies might utilize the findings of this study as a starting point for their work.

This research proposes to enhance the non-destructive method for classifying the juiciness of pineapples using tapping sound sensing. Ten statistical features were extracted from the waveform signals by waveform analysis. These features were then separated into a spectral feature set (3 features) and a temporal feature set (7 features). Each feature set was calculated with the weight of important features and selected features for 15 training datasets using 10 machine learning classifiers. Ten machine learning classifiers were Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Ensemble Voting, Adaboost, Ensemble Bagging, and Ensemble Stacking. The classifiers were evaluated with accuracy and the kappa coefficient. Grid search was used to determine various important hyperparameters for Machine Learning classifiers. The experiment results showed that the Ensemble Voting (soft), Ensemble Stacking, and MLP outperformed other classifiers. They can obtain an accuracy of 92.08%, and kappa coefficients are 0.8811, 0.8808 and 0.8808, respectively.

This paper aims to determine the required lap splice length of tensile reinforcement embedded in high volume fly ash-self compacting concrete (HVFA-SCC) beams. The lap splice length is governed by the bond strength of the concrete to the reinforcement. Several studies have shown that the bond strength of HVFA-SCC is greater than that of normal concrete (NC). The difference in the bond strength value will affect the lap splice length requirement. In this research, an experimental investigation was carried out on eight beams with dimensions of 150 × 250 mm and a length of 2 m under four points of loading. The beams were designed with various lap splices, i.e., 25%, 50% and 75% of the expected lap splice length. Other beams without lap splice were prepared as a control. A numerical study using ATENA Engineering software was also conducted to extend the investigation covering a longer lap splice length. The analysis results show that a lap splice length of 28.8 dB produces a flexural capacity equivalent to the control beam, and the tensile reinforcement has reached its yield state. This lap splice length is lower than expected in NC, suggesting that an efficient tensile reinforcement lap splice can be projected in HVFA-SCC beams.

Cities are complex and dynamic environments that are constantly facing a range of issues with both historical and spatial dimensions. While there are several international design criteria for regenerative design and promoting community well-being, such as the Living Community Challenge (LCC), Living Building Challenge (LBC), and Placemaking (Power of 10+), there are no localized referable design criteria in Taiwan. Therefore, this research aims to explore and propose a new design-factor framework for sustainable community-based public building design through various reviews and suggestions from design experts based on national and international design criteria. It has three levels of factors based on the needs of the community, which are "Surviving and Living", "Happiness and Well-being", and "Mission and Pride", corresponding to 22 design elements and their measure indicators, which help develop a sustainable community in each of the developing stages. By adopting appropriate factors within the developed framework through public investigation, inspiration, and imagination, either government policies or community proposals could improve designs. This will enable private groups or governments to adopt environmental, social, and governance (ESG) practices easily in open spaces, public buildings, and surrounding communities.

The aim of this research is to incorporate the nanographite particle (NGP) modified microencapsulated phase change material (GPCM) and carbon nanotube (CNT) into shape memory materials (SMMs). The GPCM with paraffin in the core and NGP in the shell possesses characteristics such as high thermal capacity, rapid heat absorption, and reduced electrical resistance. Moreover, the thermal storage effect of this GPCM may enhance the shape fixity and shape recovery of the SMMs. Additionally, CNTs with high aspect ratios and exceptional electrical and thermal conductivities can improve the mechanical strength and facilitate electrical or thermal activation of SMMs. After adding these GPCM and CNT to the polyurethane (PU), the thermal conductivity increased to 0.60 W/mK, and the surface resistance decreased to 2.5 × 102 Ω/sq. Upon thermal activation, the shape fixity and shape recovery of these SMMs can reach 98% and 99%, respectively. In addition, it was observed that the material could form a complete circuit under an AC voltage of 100V and successfully achieve the shape recovery effect. This indicates that the addition of GPCM and CNT can enable the SMMs to achieve shape recovery effects through heat, light, and electrical activation.

Productivity improvement is a more complicated and challenging issue to resolve. There is a solution to the multi-response optimization problem. This study proposes a novel approach to optimizing parameters in the cotton swab process using a hybrid Multiple criteria decision-making (MCDM) method based on Response surface methodology (RSM). Simultaneously enhance decision-making efficiency by integrating Technique for order preference by similarity to ideal solution (TOPSIS) and Weighted aggregated sum product assessment (WASPAS) methodology. The optimal conditions were a speed rate of 1300 rpm, a thickness of 1.5 g/m, and a slidver gap of 20 cm, while the defect and downtime were 2.54 kg and 360.67 mins, respectively. The confirmation demonstrates that the actual practical and predicted results were similar. The proposed method's total cost improves from condition A to condition B by 33.86% and 2.45%, respectively. Furthermore, the energy consumption of cotton was found to be 6,208.08 MJ. The total energy consumption may be divided into three main categories: electric energy, thermal energy, and manual energy, which account for 43.12%, 55.73%, and 1.15%, respectively. The entropy reaches its maximum value in the drying and packaging units, which have inefficiencies of 91.24% and 4.35%, respectively, while the combined inefficiencies in the other five units are only 4.41%. This study contributes to advancing decision-making processes and offers insights for enhancing operational efficiency in the pharmaceutical or other manufacturer sector.

Foot weight bearing (FWB) is important in foot-measurement studies. Methods used to estimate the subject’s FWB in standing posture were usually not practicable in that sitting postures, and FWB was then usually assumed in previous studies. This study investigated the FWB variations in sitting postures with respect to trunk angle and thigh length on the chair. Thirty-six subjects (18 males and 18 females) were recruited in this study. A four-factor factorial design was conducted, including gender (male, female), trunk angle (0°, 45°, Max), thigh length on the chair (1/3-, 1/2-, 2/3-thigh length), and foot side (left, right). FWB was calculated as the proportion of the subject’s own body weight for further analyses. The results showed that both trunk angle and thigh length on the chair had significant effects on FWB. As the trunk angle increased, the mean FWB increased, ranging from 6.57% to 21.91%; as the thigh length on chair decreased, the mean FWB increased, ranging from 12.17% to 17.19%. Overall, the mean FWB ranged from 5.53% and 24.89%. The results provided values of FWB variations in nine sitting postures with respect to three trunk angles and three thigh lengths on chair in both feet of the male and the female subjects (also total population). As a general referential protocol in foot measurement studies, these values can be used by researchers as the predetermined FWB of their studies and then to determine the subject’s sitting postures in terms of trunk angle and thigh length accordingly, or vice versa.