The first step in the road handling effort is to survey to get an accurate road condition value to take correct action in implementing maintenance. As pavement performance is known to be probabilistic, various levels of uncertainty must be assumed. Modern pavement management methods are ineffective without an effective model to predict pavement performance. Discrete-time Markov chains are the most widely used probabilistic models, and examples from different countries worldwide can be found in pavement management systems. This research aims to predict the value of road conditions during maintenance and compare road assessments with real conditions during road maintenance using the IRI, SDI, and PCI methods using the Markov process. The analysis method used is to collect secondary data from related departments and carry out direct data collection in the field to obtain condition values based on IRI, SDI, and PCI to forecast by making a pavement condition prediction model based on the Markov process and then assessing road conditions by comparing the three index values with the slightest deviation value. The analysis showed that the average value of road conditions with the IRI indicator is 4.45, which is moderate, and the most negligible difference between the probability distribution of pavement condition prediction modeling and the actual survey results was the IRI (International Roughness Index) method. This model is closest to the actual conditions during implementation, with a difference value of 5.7%.

Conventional methods of quantifying the chemical content of potatoes at different storage temperatures are time-consuming and expensive. This research studied the Visible and Near Infrared (Vis-NIR) spectroscopy for possible rapid and nondestructive methods. In this study, healthy and Fusarium spp. Potato seeds of Granola L varieties were infected artificially through the process of inoculation of fungi, and healthy potatoes were stored in various post-harvest storage conditions, namely temperatures 12°C, 25°C and a combination of temperatures 12°C and 25°C. VIS-NIR spectral data from seeds are observed periodically during the storage period. The study results showed that Vis-NIR predicted sucrose content in potatoes. The best-developed PLSR calibration model for potatoes stored at 25°C and a combination of 12°C and 25°C show R2c of 0.87 and 0.83 and RMSEC of 0.26 and 0.28. The models also successfully predicted the sugar content of potato stored at 25°C and a combination of temperatures 12°C and 25°C with R2p 0.75 and 0.78, RMSEP of 0.36 and 0.32, and RPD of 1.99 and 2.81 for sucrose. The developed model of sucrose content or potato storage temperatures of 12°C is not recommended for monitoring and detection due to the low RPD < 1.9 even though the R2c values are 0.65 – 0.9. the results of this investigation indicate that VIS-NIR spectroscopy could potentially serve as a tool for quantifying the chemical composition of potatoes during post-harvest storage.

The popularity of mobile gaming is increasing globally, fueled by technological advancements, high-quality smartphones with gaming features, and widespread internet access. A large number of young people and also children are mainly involved in different types of mobile gaming, primarily online mobile gaming, which is a matter of concern for society. This addiction leads to various psychological issues, including mental health problems, loneliness, introversion, insomnia, and a lack of self-control. Bangladesh’s lack of public mental health facilities exacerbates this challenge, particularly in rural areas. To address this issue, we have developed a machine learning model to predict the level of mental health issues faced by Bangladeshi urban children due to gaming addiction. A total of 1996 data were collected from urban parents about their children's gaming activities and categorized them as 'Serious,' 'Partial,' and 'Normal.' The dataset was then split into a 70:30 ratio for training and testing purposes. K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes, & Random Forest were applied as the machine learning approaches for prediction. Here, the Support Vector Machine gives good accuracy (92.75%), and other classifiers have different accuracy. The research highlights the significance of addressing gaming addiction in children and the potential of machine learning technology in predicting mental health issues, particularly in the absence of public mental health services. The study concludes by proposing the expansion of the research to include smartphone and mobile gaming addiction among Bangladeshi rural children and ensure a holistic approach to tackling the broader issue of technology-related addiction.

In the future, 3D food printing may be a fruitful area for the food industry. The presence of this technology opens the public's eyes to a revolution in the food sector, where food processing is limited to the conventional. The existence of this technology will also make food production much more effective and efficient. However, there is a problem when the 3D Food Printing process is carried out; when object defects occur, there will be material waste. This research proposes deep learning-based defect detection for defect and non-defect objects. This model mainly uses CNN as a deep learning method. The dataset is taken from the image of the print process performed at the time of object creation to be trained and validated to check the effectiveness of the proposed model. The architecture used uses pre-trained CNN models namely Inception-V3 and ResNet50 with the hope of classifying images that have a higher viscosity of the material. Where the model has been tested with previous datasets and applied with 3D Food Printing datasets with a dataset division ratio of 85% which is training data and 15% is validation data. After testing the two proposed scenarios, the accuracy result obtained in the test model scenario 1, Inception-V3, is 84.62% and for the test model scenario 2, ResNet50, the accuracy is 93.83%. The outcomes also demonstrate that improved accuracy, loss, and classification time may be obtained by applying CNN in conjunction with data augmentation.

The purpose of this study is to design a tool that is able to monitor moisture and light automatically and knowing the performance of lettuce seeding tools that are able to monitor moisture and light automatically, this research is descriptive research using the type of Research and Development (R &; D) research with the Waterfall method. The variables controlled are light and moisture using soil moisture sensors and grow light. The automatic seedling tool modification process consists of 3 stages including making mechanical components, electronic comments, and functional checking and product testing. The data obtained from the sensor readings will be displayed through the LCD screen on the control box. The results showed that the seedling tool can monitor moisture when the moisture is below 60%, the pump will be on and when the moisture is above 60%, the pump will be off, and the light produced with grow light will turn on at 06.00 to 18.00 pm every day. Researchers recommend adding moisture to every shelf, growing lights or LED sensor lights, and fans for further research based on their findings. As a consideration and reference resource addressing the current science on the manufacture and performance test of seedling tools that are able to monitor moisture and light automatically. incorporating the most recent ideas and information on creating and evaluating seedling equipment with automatic light and moisture monitoring. It is anticipated that this researcher will serve as a seeding industry reference.

FFA content is one of the essential qualities of oil palm fruit. FFA content exceeding 5% is considered unsuitable for human consumption. Commonly, FFA content is determined by chemical methods in the laboratory, but it is destructive, time-consuming, and costly. Several non-destructive methods have been investigated, but a maturity prediction approach has been primarily used with no direct relation to FFA content. Thus, there is a pressing need for a non-destructive framework to assess the FFA levels in the oil palm fruit directly. An attempt has been explored using a non-destructive palm fruit quality assessment that relied on electrical properties (impedance, admittance, resistance, and capacitance) in the frequency range from 50 Hz to 5 MHz was investigated to predict FFA and moisture content directly. Two statistical analyses were employed: stepwise multiple linear regressions (MLR) and artificial neural networks (ANN) to calibrate and validate electrical properties with FFA and moisture content. The best-performing models ANN showcased significant results: r= 0.96, R2=0.92, SEC at 0.86%, SEP at 0.97%, CV at 19.45%, consistency at 88.54, and RPD at 3.43 for FFA prediction, and r= 0.99, R2=0.98, SEC at 3.09 %, SEP at 3.46 %, CV at 5.44 %, consistency at 89.08 and RPD at 7.02 for predicting moisture content. In modeling oil palm quality determination, applying the ANN method significantly improved model performances, demonstrating its efficacy in predicting non-destructively both FFA levels based on admittance and moisture content based on impedance.

Electric bicycles, also known as e-bikes, are future bicycles with an electric motor as a locomotive. Electric bicycles using rechargeable batteries can go from 25 to 32 km/h, while more powerful varieties can go more than 45 km/h (28 mph). The research method begins with making an electric bicycle with a 350-watt BLDC motor powered by a 36 V 21 Ah battery, which theoretically will get up to 2 hours of battery use at maximum speed. The test was carried out in terms of travel speed fuel consumption on flat or uphill road conditions. After all the tests have been completed, data on the results of the performance of the electric bicycle can be obtained, and then a graph of the performance characteristics of the electric bicycle can be made. After conducting data analysis based on the graph of the performance characteristics of this electric bicycle, it is concluded that the use of the smallest gear is effective when the electric bicycle is starting, but the low speed and the largest gear are used when the motorbike is running to get a high speed of 32 km. The success of this research will impact Reducing dependency on fossil fuel vehicles.

Roselle plant (Hibiscus sabdariffa L.) has many benefits. Vitamin C is an antioxidant in which the human body required. It contains a high amount of vitamin C. However, vitamin C is susceptible to high temperatures. Proper extraction treatment conditions produce optimum vitamin C. This study aims to determine the optimization of vitamin C in rosella flowers. This research uses UAE with Qsonica type – Q500 (500 W, 20 kHz). Extraction treatment conditions control amplitude, time and amount of solvent. The research method used is a laboratory experiment with the RSM (Response Surface Methodology) type CCD (Central Composite Design) approach. The research method was an experimental laboratory using a CCD (Central Composite Design) via RSM (Response Surface Methodology) with an amplitude level from 20% to 50%, a time level from 10 minutes to 20 minutes, and a ratio of the amount of solvent from 140 mL to 260 mL by weight. The results showed that the length of time and the amount of solvent had a significant effect, while the amplitude had no significant effect. The significant quadratic model at p value 0.05 and R2 0.9221 with optimum UAE conditions in the treatment combination of 50 percent amplitude, 20 minutes of time, and 140 ml of solvent resulted in an optimum vitamin C content of 38.76 mg/100g. This research can be used as a reference to determine the value of vitamin C with different amplitude, time, and amount of solvent from the research data.

The rapid advancement of Artificial Intelligence (AI) technology has brought about significant positive changes across society. However, it has also introduced challenges like privacy breaches, data bias, and spreading fake news. In response, several countries, including South Korea, have provided ethical guidelines and policies for AI. Yet, these measures often fall short of keeping pace with the speed and diversity of AI development. To address these issues, this study developed a comprehensive approach by integrating ethical considerations into AI education, covering the entire AI technology development and application process. It involved analyzing previous research on AI and AI ethics education and creating a draft of an integrated AI education program focused on problem-solving based on computational thinking and ethical practicality. The draft was refined and supplemented through two rounds of expert Delphi surveys, ultimately leading to an “Integrated AI Education Program.” This proposed program emphasizes that the knowledge content of AI technology and ethical considerations should not be treated separately but addressed together. It aims to enhance moral and social capacities alongside the experience of thinking like a computer scientist when solving real-life problems. Through this, the program supports the ultimate goal of AI education: to foster computational thinking while providing an educational experience that considers both technology and ethics. This study is expected to advance the discussion on integrating AI ethics into AI education curricula and contribute to developing socially responsible AI developers and users.

Sanitation is crucial in the food industry, involving cleaning and disinfection processes to ensure facility cleanliness. This study examined sanitation knowledge and practices among small and medium enterprises (SMEs) in Malaysia's frozen meat sector. It also evaluated the acceptance of eco-friendly sanitation technology, specifically an electrolysis unit producing electrolyzed water. Data was collected through an open-ended online questionnaire using Google Forms. The questionnaire comprises demographic profiles, sanitation knowledge, sanitation program design knowledge, sanitation challenges, and the acceptance of integrating a green cleaner with the current sanitation program. Results show SME manufacturers possess sanitation knowledge but face implementation challenges due to absent sanitation programs, skilled labor shortage, hot water supply issues, difficulty cleaning narrow spaces in equipment, chemical storage, and budget constraints. Due to low-temperature operations, only 73 % of frozen meat industries use hot water for sanitation. Additionally, 45 % lack wastewater treatment, leading to chemical residue discharge into municipal drainage. SMEs show interest in electrolyzed water for its dual role as a cleaning and disinfecting agent. The technology's room-temperature efficacy and environmentally friendly degradation into salt and water appeal to SMEs. Adopting electrolysis sanitation technology is expected to significantly reduce costs by utilizing only salt, water, and electricity for electrolyzed water generation.