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Indonesia’s rice imports throughout 2022 will increase by 5%. Indonesia plans to import again to anticipate production disruptions due to a potential El Nino. Indirectly, Indonesia depends on other countries for its rice needs, so this study’s main objective is to analyse rice import performance and estimate the competitiveness and elasticity of demand for Indonesian rice imports. This research used annual import data of Indonesia from Thailand, Vietnam, and India, which came from the UN-Comtrade and will be analysed using the Almost Ideal Demand System (AIDS). The results showed that Thailand has the largest share and an inelastic own-price elasticity, so the consumption of imported rice from Thailand is unaffected by price changes. Based on income elasticity, it shows that imported rice is a normal good. Rice imports from Thailand and India are complementary, while others are substitutes for each other. Thailand is still the largest supplier of imported rice for Indonesia, and it will be a challenge for Indonesia if Thailand limits the amount of rice it exports to domestic needs. Imports are a short-term solution, so a long-term policy must deal with limited rice stocks caused by climate and weather uncertainties.

Hadoop and Apache Spark have become popular frameworks for distributed big data processing. This research aims to configure Hadoop and Spark for conducting training and testing on big data using distributed machine learning methods with MLlib, including linear regression and multi-linear regression. Additionally, an external library, LSTM, is used for experimentation. The experiments utilize three desktop devices to represent a series of tests on single and multi-node networks. Three datasets, namely bitcoin (3,613,767 rows), gold-price (5,585 rows), and housing-price (23,613 rows), are employed as case studies. The distributed computation tests are conducted by allocating uniform core processors on all three devices and measuring execution times, as well as RMSE and MAPE values. The results of the single-node tests using MLlib (both linear and multi-linear regression) with variations of core utilization ranging from 2 to 16 cores, show that the overall dataset performs optimally using 12 cores, with an execution time of 532.328 seconds. However, in the LSTM method, core allocation variations do not yield significant results and require longer program execution times. On the other hand, in the multinode (2) tests, optimal performance is achieved using 8 cores, with an execution time of 924.711 seconds, while in the multi-node (3) tests, the ideal configuration is 6 cores with an execution time of 881.495 seconds. In conclusion, without the involvement of HDFS, distributed MLlib programs cannot be processed, and core allocation depends on the number of nodes used and the size of the dataset.

Stabilization of clayey soil is commonly used to enhance unfavorable engineering properties. The effects of silica additives on soil improvement have been considered recently, and documented research studies on the characteristics of problematic clays stabilized by silica additives are interesting for many researchers. Alternative waste disposal strategies that would be both economically beneficial and environmentally friendly have been identified by this study. In the present study, waste silica-based addition is used to stabilize clayey soil, and the time-dependent changes in strength properties are investigated. Unconfined compressive strength (UCS) tests were conducted on stabilized samples at various curing durations to investigate macro-level properties. The UCS test results revealed that the 7.5% of GP+ 5% L content was optimal for the clayey soil as the strength was increased by 166.06%. The addition of the glass powder with hydrated lime components to the clayey soil resulted in stabilized samples, indicating a dense and compact matrix and reduced soil porosity, which increased the mechanical strength, according to the SEM analysis. This curing reaction technique is very beneficial and economical for geotechnical engineering applications.

Porous metals are very attractive materials for biomedical application as the physical and mechanical properties of these materials can be tailored similarly to the natural bone. In this work, porous stainless steel 316L has been fabricated by foam replication method. This method offers a lot of advantages including of easy processing technique, very economic, does not involve the use of toxic chemical and capable of producing porous structure that almost similar to natural bone. The porous stainless steel 316L (SS316L) samples were prepared by varying the SS316L composition from 40 wt% to 60 wt%. Sintering process was carried out at 1250°C in a vacuum furnace. The microstructure and pore size were observed and determined through Scanning Electron Microscope (SEM). Archimedes method was used to measure the samples density, while compression test was carried out to determine the compressive strength and elastic modulus. The average pore size for samples with 50 wt% and 60 wt% SS316L are 268μm and 299μm respectively. Samples with 40 wt% SS316L experienced the largest shrinkage which is 33% while the sample with 60 wt% SS316L experienced the smallest shrinkage which is 21%. The density and porosity of the porous SS316L with 50 wt% SS316L are 0.43g/cm3 and 93.6% respectively, and for porous SS316L with 60 wt% SS316L are 0.69 g/cm3 and 89.2% respectively. The modulus of elasticity and compressive strength for porous SS316L with 60 wt% SS316L are 0.46 GPa and 56 MPa respectively. All these properties are in the range of the natural bone properties. Besides, the cytotoxicity test showed that this porous SS316L does not have a cytotoxic potential for biomedical implant.

Introduction: Changes to federal legislation allowed nicotine-based e-cigarettes legal entry into the Canadian market in 2018. This included pod-type e-cigarettes (pods), such as JUUL, that were later found to be associated with steeply increasing prevalence and greater frequency of e-cigarette use among US and Canadian youth. Multiple studies of risk factors of JUUL use and use initiation have been conducted among various population groups in the US, but little evidence exists pointing to similar risk factors of pod use among Canadian youth and young adults. Understanding these risk factors can inform use prevention and intervention strategies in Canadian and other jurisdictions., Methods: A total of 668 Canadian youth and young adults recruited by the 2018-19 Youth and Young Adult Panel Study were provided a baseline survey 3 months before and a follow-up survey 9 months after the relaxation of federal nicotine e-cigarette regulations. We used multivariable logistic regression to understand and rank importance of baseline predictors of future pod use among respondents., Results: Past-month cannabis use (OR [odds ratio] = 2.66, 95% CI: 1.66-4.21, p < 0.001), established cigarette use (OR = 3.42, 1.53-7.65, p < 0.01), past cigarette experimentation (OR = 2.40, 1.34-4.31, p < 0.01), having many friends who vaped (OR = 2.15, 1.37-3.34, p < 0.001), age below 18 compared to age over 22 (OR = 5.26, 2.63-10.00, p < 0.001) and male sex (OR = 1.69, 1.16-2.50, p < 0.01) were significant and the most influential predictors of future pod use., Conclusion: Similar factors drove pod use among Canadian and US youth and young adults. Appropriate preventive strategies can benefit from considering polysubstance use among high school-aged youth., Competing Interests: None to be declared.