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Ultrafine bubbles and low doses of gamma irradiation had a positive effect on seed germination. This research tested the effectiveness of ultrafine bubbles (UFB) water and gamma irradiation for improving the viability and vigor of Albizia chinensis (Osbeck) Merr. seeds that had been stored for four years. A completely randomized two-factor experiment, i.e., UFB water and gamma irradiation treatments, were used to determine the best seed treatment. The UFB water treatment had a significant effect on all seed germination parameters; the gamma irradiation treatment did not have a significant effect on improving the viability and vigor of the seeds, with a very low initial germination (11.7%). Soaking the seeds in UFB water for 30 min gave the best germination (51.3%). These improvements also occurred in other germination parameters such as germination rate, germination value, radicle length and vigor index. The UFB water treatment has the potential to improve the germination of seeds with low physiological qualities.

Palm kernel cake (PKC), a by-product of palm oil production, has the potential to serve as a cost-effective alternative to traditional feed ingredients such as soybeans, owing to its nutritional value and affordability. However, its quality can vary significantly, impacting its suitability for different livestock. This study aims to develop a machine learning model to identify the dominant features influencing PKC quality using data from proximate analysis. Our model utilizes feature selection techniques and machine learning algorithms with a decision tree to determine the critical factors affecting key quality parameters, such as fat, dry matter, crude protein, crude fiber, and ash. We explore the relationship between these features and PKC suitability for different animal diets. Our novel approach sheds light on the intricate factors governing PKC quality, enabling improved processing and utilization of this valuable resource.

The waste generated by detergents is very damaging to the environment. The detergent consists of three main components: surfactants, building materials (phosphate compounds), and additives (bleach and fragrance). Surfactants widely used as detergents are generally anionic, and toxic, and can cause destabilization of living things. Therefore, finding a good solution to replace using detergents as an ingredient in the cleaning process is necessary. One of the best and environmentally friendly alternatives is using ultrafine bubbles, in the cleaning process. Ultrafine bubbles that are negatively charged and tend to collect positively charged matter. The cleaning power is enhanced by several effects of ultrafine bubbles such as; the degreasing effect, the effect of sloughing off deposits by getting between them and the cleaning agent, and the forces and shock waves when the gas bubbles burst. One of the big effects of ozonized ultrafine bubbles is very high detergency. Ozonized ultrafine bubbles have a degreasing effect of separating and removing oil as they penetrate the oil and between the boundaries with the adhering surface. Additionally, ozonized ultrafine bubbles can penetrate tight spaces between objects that touch each other, and when the ozonized nanobubbles coalesce into ozonized microbubbles, they can act as wedges to separate them.

The purpose of this study is to identify sustainability variables in domestic wastewater management in informal self-help housing in Bogor City. The analysis tool used is MICMAC analysis, which can identify variables of influences and dependences, mapped in a graph to explain the relationships between variables, and know the causal chain of a system. The identified variables are derived from books, journals, regulations issued by the government, field observations, in-depth interviews with stakeholders, focuss group discussion. The result of the MICMAC analysis is 1) very influential variables as key variables include: variables of the role of government and social capital; 2) relay variables or variables that are influential but highly dependent, including variables of community participation and empowerment, knowledge and education, sharing community resources, financial assistance, non-financial assistance, provision/ construction of facilities and infrastructure, operation and maintenance of facilities and infrastructure, community institutions, management collaboration, policy reform, budget support, regulations for providing incentives/disincentives and rewards/sanctions that are implemented; 3) depending variables or result variables are variable of spatial and building planning; 4) excluded variables or variables whose influence is relatively small on the system are variables of culture and customs, alternative financing, land provision and legal aspects of land, partnerships between government, private, community and volunteer organizations and asset security.

Ethnic culture influences the plant diversity in homegardens, automatically impacting socio-economic and ecological services. The Malay ethnic in the Belitung district uses many species of plants from their homegardens. However, ethnobotanical studies of the homegarden must be carried out for more efficient and sustainable use. This study aimed to describe the plant diversity and structure of the Malay homegarden in the Belitung district through an ethnobotanical perspective. Ethnobotanical data were collected using the participatory observation method in three villages from different sub-districts, Kembiri, Sijuk, and Suak Gual. The key and general respondents were selected using the purposive and the snowball sampling methods, and the plant diversity was identified using the inventory method. The structure of the Malay homegarden in Belitung consists of side, front, and back yards. The homegarden plants have four habitus types: trees, shrubs, herbs, and lianas. The homegarden area in Sijuk villages was the most spacious (599.55 m 2 ). Myrtaceae dominated the homegarden in Sijuk, while the Zingiberaceae dominated the Kembiri and Suak Gual villages. The highest Index of Cultural Significance value is Cocos nucifera in Sijuk and Kembiri villages and Citrus macrocarpa in Suak Gual Village. Malay ethnic uses most homegarden plants for food and medicine ingredients.

Fruit oil content (FOC) is one of the most significant commercial characteristics in oil palm output both in upstream and downstream phases. The NIR Spectroscopy approach was used as a method to determine the oil content of fresh oil palm fruits. Several tests on the NIR spectroscopy approach for estimating the oil content of oil palm fruits revealed that the accuracy was still ideal when utilizing earlier spectra processing. Still, some crucial information was lost when using this spectra processing. Five hundred samples, categorized into ten groups according to their maturity levels, were readied for reflectance measurements and chemical assessments of oil content. The NIRFlex N-500 FT-NIR Spectrometer was employed to measure the reflectance of the samples within the 1000-1500 nm wavelength range. The acquired spectrum of fresh oil palm fruits was then converted to absorbance (Log 1/R). The method applied in this research is to select NIR absorption wavelengths that correlate with palm fruit oil content using PCA analysis and then develop a model using MLR. Five influential wavelengths were identified for predicting oil content of oil palm fruit based on the highest PC values in the loading plot of PCA those are 1166.59 nm, 1188.2 nm, 1212.4 nm, 1387 nm, and 1486 nm. The wavelength of 1212 nm (Ar) was selected as the reference of absorbance for establishment of MLR model, as A1, A2 and A3 were absorbances at the wavelengths of 1251.88, 1252.51 and 1468.86 nm, respectively. The established MLR model of Y= -14088.57 (A1/Ar) + 14017.9 (A2/Ar) – 12.24 (A3/Ar) + 120.67 can predict oil content of oil palm fruit (Y) accurately (R 2 of 0.8 and SEP of 3.28 %). These findings show that the regression model has a strong capacity for prediction of oil content of palm fruits easily and nondestructively without the need for spectral preprocessing, solvents, or reagents, making it environmentally friendly.

Red chili ( Capsicum annuum L.) is one of the basic consumption commodities in Indonesia for household and industrial needs. Chili is a horticultural product that was easily damaged (both physically and mechanically) and was always needed so that proper packaging and storaging can be a critical post-harvest point to maintain horticulture product freshness. The purpose of this study was to determine the effect plastic crate and cardboard box packaging on the physical quality of fresh curly chili. The curly chilies that have been harvested are then sorted based on uniformity of quality. Sorting is carried out to ensure that the quality of fresh curly chilies is uniform. The chilies are put into cardboard packaging and plastic baskets neatly and fully filled, then covered with adhesive so that during transportation the chilies do not spill out of the package. The weight of each package is measured to determine the initial weight of the packed chilies and then transported using a pick-up car. After that, the chilies are stored at 15 0 C for 16 days until curly chilies are not suitable for consumption. Chili quality measurement included weight loss, color change, hardness and water content changes. The cardboard packaging is better than using plastic crate when viewed in terms of increased weight loss (crate packaging weight losses 27,5%, cardboard packaging weight loss 24,8% after storage). Storage using plastic crate and cardboard is proven to be good at maintaining the hardness of chili (around 2,15-2,20 N after storage). Color changes was seen in basket packaging and cardboard packaging (the degree of L, a, b). In terms of changes in moisture content, plastic crate packaging is better than cardboard packaging to maintain the moisture content (The moisture content in plastic crate packaging was 82% to 74%, moisture content in cardboard packaging was 81% to 75%).

The agarwood grading system in Indonesia comprises physical properties, such as colour, weight and aroma. Agarwood resin content can be a simple guide for grading agarwood quality. Thus, this research aimed to evaluate the quality of agarwood (Aquilaria malaccensis) from Bintan Island based on Indonesian standard, and to determine its resin content, using near-infrared (NIR) spectroscopy (NIRS), as a predicting model for agarwood quality. The samples were obtained from community plantation forest in Bintan island which were inoculated by Fusarium solani. Agarwood chips were graded by its colour, weight and aroma, and were converted into powder samples for colour testing and NIRS acquisition, as well as laboratory testing for resin content determination. Results, according to SNI 7631-2011, showed the agarwood quality of gubal gaharu (agar) and kemendangan (dhum) where the colour of gubal gaharu was darker than kemendangan, showing that gubal gaharu had higher resin content (24.74%) than kemendangan (12.22%). The NIR spectroscopy analysis showed that the partial least square regression (PLSR) model could be used to predict the resin content with passable accuracy.

Water management plays a crucial role in paddy rice cultivation. Inappropriate use of irrigation water can hinder optimal plant growth and lead to wastage. Therefore, it is essential to optimize the irrigation water delivery system. This optimization can be achieved after developing a model that identifies the relationship between the irrigation system and plant growth. This research aims to develop a plant growth model influenced by the irrigation system. An artificial neural network model with a backpropagation algorithm is employed to predict plant growth under different irrigation treatments. The model development is based on a lab-scale rice cultivation experiment conducted over two growing seasons in 2021 and 2022, comparing a flooded system (CFI) and a more water-efficient intermittent irrigation system (IIS). The first growing season was used for model training, while the second season was for model validation. The developed model incorporates three inputs: soil moisture, plant height, and the number of tillers from the previous week. The outputs are the plant height and the number of tillers for the following week. The results of the model training indicate that the neural network model accurately predicts plant height and the number of tillers, with R 2 values of 0.99 and 0.93, respectively. For validation, the R 2 values are slightly lower, at 0.97 and 0.65. These results suggest that the model can effectively predict plant height and the number of tillers.

As nitrogen (N) is needed at the greatest amount for the plant growth, the addition of adequate amount of this nutrient in rice paddy field is one of the key factors for maximizing the rice production. Knowing the spatial soil N status in the rice field measured using conventional analysis takes time and expensive. Many previous researchers reported that near infrared spectroscopy (NIRS) was able to successfully predict soil N due to its high correlation to the soil carbon (C). The aim of this research is to test whether NIRS able to predict soil N content, independently predicted from soil C. Soil samples in 147 locations, including the coordinates, were collected in rice paddy field of Lombok Island, Indonesia. Parts of the samples were analysed in a laboratory using conventional analysis for total N and total organic C, and the other parts were scanned using near infrared spectroscopy (NIRS) for spectral data collection. A Partial Least Square Regression (PLSR) calibration model was developed using laboratory-analysed soil N (and C) data and soil spectral data. Although soil N and soil C have a poor correlation, but both can be predicted well using NIR technology, indicating the soil N was independently predicted from soil C. This finding shows that soil N content in rice paddy field of Lombok Island can be predicted and monitored by NIRS without depending on its high autocorrelation with soil C.