In this paper, we demonstrate the optimization of reactive-ion etching (RIE) parameters for the fabrication of tantalum pentoxide (Ta2O5) waveguide with chromium (Cr) hard mask in a commercial OIPT Plasmalab 80 RIE etcher. A design of experiment (DOE) using Taguchi method was implemented to find optimum RF power, mixture of CHF3 and Ar gas ratio, and chamber pressure for a high etch rate, good selectivity, and smooth waveguide sidewall. It was found that the optimized etch condition obtained in this work were RF power = 200 W, gas ratio = 80 %, and chamber pressure = 30 mTorr with an etch rate of 21.6 nm/min, Ta2O5/Cr selectivity ratio of 28, and smooth waveguide sidewall.

Pertanika Journal information and guidelines

The study sought to precisely determine the dimensions of Harumanis mango fruit while they are still attached to the tree, using digital image processing methods. Harumanis mangoes images were photographed at varying distances (1, 2, and 3 meters) and heights (0.5, 1, and 1.5 meters) using a camera and then analysed with MATLAB software. The study showed that the lowest percentage errors for fruit length and width were 1.16% and 0.22%, respectively, when the camera was placed 2 meters away and 1 meter above the fruits. The average growth rate of fruit width and length was found to be around 1 cm/week and 0.44 cm/week, respectively, using digital image analysis. The coefficient of determination (R2) values for fruit length and width was 0.79 and 0.88, respectively, indicating the dependability of the estimated results. This method can be utilized to monitor the fruit's sizes, effectively replacing the tedious manual measuring procedure.

Early and reliable detection of plant wilting is crucial for maintaining crop productivity and ensuring sustainable agricultural practices. This study aimed to evaluate the effectiveness of machine learning models in detecting wilting in Vinca rosea plants using simple geometric features extracted from digital images. The extracted features included leaf width, length, area, aspect ratio, and area coverage. Four machine learning classifiers, Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbours (kNN), and Naive Bayes (NB) were trained and validated to classify plants as wilted or healthy. Results showed that SVM achieved the highest performance, with 0.98 accuracy, 0.99 precision, 0.95 recall, 0.97 F1 Score, and 0.99 area under the curve (AUC), outperforming the other models. DT and kNN achieved competitive accuracy (0.97 and 0.95, respectively) but exhibited weaker recall, while NB showed high recall (0.92) but reduced precision (0.87). The findings highlight the robustness of SVM and the potential of simple geometric features combined with machine learning for rapid and reliable wilting detection. Moreover, integrating such image-based wilting detection with irrigation control can optimise water use while sustaining plant health and yield.

HYDRUS is one of the popular software packages used for water, heat and solute transport simulation. Although numerous studies on bibliometric analysis were conducted, the bibliometric research and systematic searching strategy on the HYDRUS are rarely explored by scholars. Therefore, the main focus of this study is to explore existing literature on the global research trends of HYDRUS according to bibliometric methods. The data was filtered and extracted from the Scopus database. Bibliometric analysis was performed in different aspects such as yearly publications trends, countries productivity, leading journals and subject areas. Visualisation analysis was deployed using VOSviewer software for collaboration patterns and content analysis to explore networking and emerging topics in HYDRUS. In total, 1903 literature related to HYDRUS publications from the year 1993 until 2020 were retrieved. The quantity of HYDRUS publications has substantially increased for almost three decades. HYDRUS mainly used in the field of Environmental Science (n=1120, 34.1%), Agricultural and Biological Science (n=871, 26.5%) and Earth and Planetary Sciences (n=424, 12.9%). In addition, Agricultural Water Management Journal (Elsevier, Netherlands) is the leading journal in HYDRUS publications. The top three prolific scholars on subject matters are Šimůnek Jirka (H-Index 71, University of California, United States), Martinus Th. van Genuchten (H-Index 82, Utrecht University, Netherlands) and Harry Vereecken (H-Index 65, Institute of Bio- and Geosciences, Germany). The United States, China and Germany, among the most productive country published papers related to HYDRUS. The HYDRUS study is applied in three clusters; media properties cluster, irrigation cluster and groundwater study cluster, with a primary focus on water flow and solute transport modelling. The keywords analysis revealed that the most active research topics were "model", "water", and "parameter" throughout the study period. This bibliometric technique helps researchers identify and understand the specific knowledge domain on the past, current, and emerging research topics.