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.

Fall armyworm (FAW) is a polyphagous and voracious pest, destroying maize plants in farms in Cameroon. An annual yield loss is estimated to range from15 to 78%, valued at US$ 2,481 to US$ 6,187 million. With most damage experienced in the mono-cropping system. Maize is the most widely grown cereal crop globally due to its several uses, namely human consumption, animal feed and biofuel. In Cameroon, maize is a staple food grown by small- scale producers in all ten regions. The control of FAW is unsuccessful with only the use of pesticide method, the application is knowledge-intensive, and misuse often leads to pesticide resistance, resurgence and increased production cost. The purpose of this review was to explore the different controlling methods adopted to suppress FAW from causing economic damage in maize farms of small-scale producers in Cameroon. Integrated pest management (IPM) approach was used to control FAW, including cultural control, chemical control, botanicals, push-pull farming system, biological control and indigenous knowledge. Results showed that push-pull farming system provides protection and improves maize nutrition, botanicals have similar efficacy like synthetic insecticide, and wood ash is a bio-pesticide. The combined application of pesticides and handpicking FAW was effective though feasible in small surface areas. Based on the general assessment, the push-pull farming system deserves to be promoted due to its numerous benefits: eco-friendly, enhancement of natural enemies, increased soil fertility and economic returns. Natural enemies and bio-pesticides application are essential to control FAW since farmers are resource-poor, causes no health problem and are environmentally friendly.

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 (CM) with the laboratory test of some commercial wood species. Since wood is weaker when its moisture content is high, the wet service factor (CM) 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 CM 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 CM values to adjust the modulus of elasticity, modulus of rupture, shear strength parallel-to-grain, tensile strength parallel-to-grain, and maximum crushing strength (CM = 0.59, 0.76, 0.65, 0.73, and 0.67, respectively) were significantly lower than SNI 7973-2013 designated values (CM = 0.9, 0.85, 0.97, 1, and 0.8, respectively). The empirical CM for the compression stress perpendicular-to-grain at the proportional limit and that at the 0.04″ deformation (CM = 0.66) were slightly lower than the designated values (CM = 0.67), although they were not significantly different. This study resulted in lower empirical CM 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 CM 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 CM values.

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This paper presents a performance analysis and comparison of several pre-processing methods used in automatic patent classification with graph kernels for Support Vector Machine (SVM). The pre-processing methods are based on the data transform techniques, namely data scaling, data centering, data standardization, data normalization, the Box-Cox transform and the Yeo-Johnson transform. The automatic patent classification is designed to classify an input of patent citation graphs into one of 10 possible classes of the International Patent Classification (IPC). The input is taken with various background conditions. The experiments showed that the best result is achieved when the pre-processing method is data normalization, achieving a classification accuracy of up to 85.33.15% for the KEHL and 93.80% for the KVHL. In contrast, for the KEHG, the preprocessing method application decreased the accuracy.