Indonesian Wooden Building Code (SNI 7973-2013) has adopted the National Design Specification (NDS) for Wood Construction since 2013. A periodic harmonization of the building-code-designated values (i.e., reference design values and adjustment factors) with the experimental data of commercial wood species is necessary. This study aimed to compare the building code’s wet service factors (C_M) with the laboratory test of some commercial wood species. Since wood is weaker when its moisture content is high, the wet service factor (C_M) must adjust the sawn lumber reference design values if the building serves in wet or aquatic environments. Four commercial wood species, namely pine (Pinus merkusii), agathis (Agathis dammara), red meranti (Shorea leprosula), and mahogany (Swietenia mahagoni), were subjected to mechanical property tests. To calculate the empirical C_M values, the mechanical properties tests were conducted on air-dry and wet wood. Instead of testing the full-sized timber, which contains the growth characteristics and defects, this study chose clear-wood specimens to resemble the boundary condition of the ceteris paribus (other things being equal). The wet (water-saturated) specimens were immersed in water for 65 days, and the test was carried out when the specimen was still immersed. The test arrangement imitated the submerged wood as the worst-case scenario of the wet environment where the construction serves, rather than green or partially immersed timber. As many as 40 specimens were tested to compare each mechanical property’s wet service factor; thus, this study reported 200 specimens’ laboratory test results. The empirical C_M values to adjust the modulus of elasticity, modulus of rupture, shear strength parallel-to-grain, tensile strength parallel-to-grain, and maximum crushing strength (C_M = 0.59, 0.76, 0.65, 0.73, and 0.67, respectively) were significantly lower than SNI 7973-2013 designated values (C_M = 0.9, 0.85, 0.97, 1, and 0.8, respectively). The empirical C_M for the compression stress perpendicular-to-grain at the proportional limit and that at the 0.04″ deformation (C_M = 0.66) were slightly lower than the designated values (C_M = 0.67), although they were not significantly different. This study resulted in lower empirical C_M values than the designated ones, which found that the building code lacked conservativeness. The lacked conservativeness is mainly attributed to the building code’s recent choices, e.g., (1) the wet service environment basis is the green timber rather than the fully water-saturated one, and (2) the ratio of near minimum (5% lower) distribution value is chosen as the C_M value rather than the average of wet timber’s mechanical property divided by the air-dry one. This study proposes changing both recent choices to alternative ones to develop more safe and reliable designated C_M values.

A function from the domain (x-set) to the codomain (y-set) connects each x element to precisely one y element. Since each x-point originating from the domain corresponds to two y-points on the graph of a closed curve (i.e., circle, ellipse, superellipse, or ovoid) in a rectangular (Cartesian) diagram, it does not fulfil the function’s requirements. This non-function phenomenon obstructs the nonlinear regression application for fitting observed data resembling a closed curve; thus, it requires transforming the rectangular coordinate system into a polar coordinate system. This study discusses nonlinear regression to fit the circumference of a tree stem’s cross-section and its sapwood–heartwood transition by transforming rectangular coordinates (x, y) of the observed data points’ positions into polar coordinates (r, θ). Following a polar coordinate model, circular curve fitting fits a log’s cross-sectional shape and sapwood–heartwood transition. Ellipse models result in better goodness of fit than circular ones, while the rotated ellipse is the best-fit one. Deviation from the circular shape indicates environmental effects on vascular cambium differentiation. Foresters have good choices: (1) continuing using the circular model as the simplest one or (2) changing to the rotated ellipse model because it gives the best fit to estimate a tree stem’s cross-sectional shape; therefore, it is more reliable to determine basal area, tree volume, and tree trunk biomass. Computer modelling transforms the best-fit model’s formulas of the rotated ellipse using Python scripts provided by Wolfram engine libraries.

The freshness of beef becomes the most important factor affecting the quality of the meat. The need for beef continues to increase, indicating that awareness of the importance of eating meat is getting higher and higher. With the high price of beef in the market, it makes some people who cheat mix the quality of beef with cut meat that has been stored for a long time. The purchase of beef is usually carried out to identify the freshness of the meat with the naked eye and sense of smell as well as direct contact by suppressing the texture of the meat. However, this method has the disadvantage that consumers are not observant and have difficulty distinguishing the quality of beef freshness. Therefore, in this study, we tried to examine the level of freshness of beef by using the TGS 2602, MQ-135 gas sensors as detectors of voc and ammonia compounds produced by meat and the TCS3200 color sensor by using RGB values as identification of the beef. This study also analyzed the level of beef freshness with indicators obtained from the three sensors using the K-Nearest Neighbor method. In this study, there were 3 conditions of the level of freshness of the meat tested, namely fresh meat, slightly fresh meat, and non-fresh meat with an accuracy rate of 93%.

Forest monitoring is essential for environmental conservation and resource management, addressing deforestation, climate change, illegal activities, and natural disasters. This book chapter explores the application of remote sensing technology and the Internet of Things (IoT) for accurate, efficient, and sustainable forest condition monitoring. Remote sensing technologies, such as Landsat, MODIS, and Sentinel, track land cover changes, vegetation health, and illegal activities, while vegetation indices like NDVI and EVI assess ecosystem conditions. IoT technology, with real-time sensors measuring temperature, humidity, soil moisture, and air quality, provides continuous data for early risk detection. This integrated approach combines large-scale data collection with real-time analysis, empowering stakeholders to address forest challenges effectively. The integration of IoT with Geographic Information Systems (GIS) enhances spatial analysis, supporting informed decision-making for forest conservation. By adopting these innovations, forest ecosystems can be better preserved, ensuring sustainability and resilience against environmental threats.