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A thermal protection system (TPS) is crucial in protecting spacecraft from aerothermal heating during atmospheric entry. Aerospace commonly uses carbon/phenolic composites for their high-temperature resilience Graphi-bond ARMC-551-RN is a commonly used composite system in high-temperature applications. The aim of this work is to observe the failure phenomenon in this system. I have gathered information about the thermomechanical properties of the C/ARMC composite by conducting thermal analysis, tensile testing, and temperature adjustments. One of the methods I used for thermal analysis is thermogravimetric analysis (TGA), which can detect changes in sample weight (mass) with respect to temperature or time and determine the temperature at which material degradation begins. The next step is to conduct a tensile test with temperature variations ranging from room temperature (RT) to 800 °C in increments of 100 °C. Observing failure modes such as matrix failure, fiber fracture, fiber debonding, and delamination is necessary to understand composite behavior. Through our research, we discovered that the composite material had the highest tensile strength at a temperature of 400 °C, measuring around 240 MPa. We confirmed this through the matrix's degradation at approximately 473.49 °C. Additionally, we observed various failure modes during temperature testing up to 800 °C. The thermomechanical data we obtained can be useful in designing a TPS for aerospace applications.

ABSTRACTThe need for more resilient infrastructures entails an accurate and efficient structural reliability analysis. In recent years, Kriging-based meta-modeling has constantly been adopted to reduce the computational cost in estimating the probability of failure. Active learning models focus on most informative samples to increase computational efficiency. Nevertheless, the efficiency gained from the models may diminish when dealing with a rare event with a relatively small probability of failure. To resolve the difficulty, this study proposes a new method, Adaptive Kriging with Multi-layered Hyperball-based Importance Sampling (AK-MHIS), to estimate the structural probability of failure. AK-MHIS uses an adaptive multi-layered hyperball as its candidate sample pool with a new sample filtering mechanism and a more robust stopping condition for the active-learning phase. The performance of AK-MHIS method is validated in benchmark cases. The verification results confirm the superior performance of AK-MHIS to previous methods.

Stable isotopes, δ2H, δ18O, and d-excess, are valuable tools as natural tracers of diffusion processes and phase changes in the global hydroclimatological cycle. The Indonesian Maritime Continent (IMC) is an archipelago area surrounded by very warm waters which induce convective activities as the primary heat source driving global atmospheric circulation. Given the central role of IMC in this hydroclimatological cycle, comprehensive study, and data collection on the stable isotopes of precipitation in this region is crucial. In this study, we collected monthly stable isotope data from 62 stations spread throughout the Indonesian archipelago from September 2010 to September 2017. We cleaned the data and conducted quality control activities by comparing the Local Meteoric Water Line (LMWL) to previous studies in a similar climatic region. We shared these data openly on our GitHub repository, making them easier to update and interact with users in the future.

Old leachate from the waste management process can negatively impact the environment if it is not appropriately treated. Commonly, old leachate processing is complicated in view of its low biodegradability, making it impossible to be treated by biological processes. One technology that can increase biodegradability is ozonation. This study aims to determine the effects of dosed ozone on organic removal and the improvement of biodegradability in aged high salinity leachate. This study used leachate samples from the Jungut Batu Landfill in Nusa Lembongan, Klungkung Regency, Indonesia. Something that needs to be considered from the leachate characteristics in the study site included the high salinity value that might lead to low biodegradability. Variations in this study were detention time and ozone dose administration (1, 10, 30, and 100 mg·O3/min). The initial characteristics of leachate in the study site showed very low biodegradability, reaching 0.084. During the 60-min detention, the maximum chemical oxygen demand (COD) removal could only reach a value of 21.1% (100 mg·O3/min). At the same time, the ozonation process could achieve an increase in biochemical oxygen demand (BOD)/COD biodegradability of 0.156. Multivariant test results on ozone dosing variations significantly affected organic degradation (BOD and COD) and increased biodegradability.

Introduction Condylomata acuminata (CA) is a sexually transmitted infection with a high prevalence associated with psychosexual morbidity in both men and women of various age. Up to now, treatment modalities yield low clearance and recurrence rate (RR) and are also deemed low quality evidence-wise. Photodynamic therapy (PDT) is a novel and promising therapy to effectively cure and prevent CA recurrence.Method This systematic review was reported according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) and registered to the International Prospective Register of Systematic Review (PROSPERO) (CRD42022332760).Results Ten studies were included in this systematic review. A significant value of complete response (CR) ranging from 63–100% in patients with genital warts after receiving several sessions of PDT. A relatively low recurrence rate was seen in all 10 studies, with an RR of less than 17%. Quality assessment of included studies reported mostly high-quality research.Conclusions PDT therapy resulted in a higher CR with significantly lower RR compared to other therapies. Thus, PDT can be an alternative treatment of CA with low RR and minimal side effects. Additional research, especially randomized clinical trials in various countries, is needed to further substantiate this treatment and formulate definitive protocols.

Absorbance differential spectra could be utilized to identify dissolved organic matter (DOM) characteristics such as the disinfection by products (DBPs) formation. This research purposed to establish the relationship between absorbance differential spectra and trihalomethanes-4 (THM4) formation potential, as well as the estimated cytotoxicity of tropical peat water DOM percentage. The ion exchange resin was used to separate the DOM components from peat water. In addition, the UV–Vis spectrum was examined between 200 and 700 nm. The hydrophobic-acid (HPOA) fraction contains the highest concentration of dissolved organic carbon (DOC), THM4 production potential, and calculated cytotoxicity. On the other hand, the hydrophilic-neutral (HPIN) fraction has the lowest potential THM4 production. The UV–Vis absorbance spectra of all DOM fractions showed a comparable peak at 277 nm. The differential spectra of 277 nm (ΔA277) indicated a significant association with DOC concentration (99.6 %), trichloromethane (TCM) creation (86.6 %), total-THM4 (TTHM4) (81.3 %) formation, THM4 estimated cytotoxicity (89.7 %), and a moderate correlation with bromodichloromethane (BDCM) formation (55.6 %). Meanwhile, ΔA277had a poor correlation with brominated THM4 formation potential (chlorodibromomethane (CDBM): 2.59, tribromomethane (TBM): 2.78 %). The absorbance differential spectra might be employed as a surrogate measure for the peat water DOM fraction and its precursor properties to form THM4 during the chlorination process, as well as its estimated cytotoxicity.

Watersheds are increasingly experiencing environmental damage in various parts of the world recently, including in Indonesia. Indonesia, which has approximately 17 thousand watersheds, faces major challenges in watershed management, especially with the increasing pressure on watershed resources. The accuracy of watershed management is very necessary for the era of rapid development and growth. The abilities of Soil and Water Assessment Tool (SWAT) will assist in making the right decisions in watershed management. Although the model has several advantages such as free access and user-friendly, the model still has limitations when applied to watersheds that do not have sufficient observation data. The purpose of this study is to review the uses of this model in Indonesia. The SWAT has been applied in various watersheds reported in 178 publications that have passed peer-review. Most of the applications were found in Java Island, with 112 publications. The availability of climatological and hydrological data was main obstacle for model application. However, remotely sensed data this limitation and challenge can be handled with remote sensing techniques which will make this model very compatible to be applied in Indonesia. The SWAT will expectantly direct its application to regions still less or never been established, such as the Kalimantan and Papua regions.

Sensitivity analysis is an important aspect of model development as it can be used to assess the level of confidence that is associated with the outcomes of a study. In many practical problems, sensitivity analysis involves evaluating a large number of parameter combinations which may require an extensive amount of time and resources. However, such a computational burden can be avoided by identifying smaller subsets of parameter combinations that can be later used to generate the desired outcomes for other parameter combinations. In this study, we investigate machine learning-based approaches for speeding up the sensitivity analysis. Furthermore, we apply feature selection methods to identify the relative importance of quantitative model parameters in terms of their predictive ability on the outcomes. Finally, we highlight the effectiveness of active learning strategies in improving the sensitivity analysis processes by reducing the total number of quantitative model runs required to construct a high-performance prediction model. Our experiments on two datasets obtained from the sensitivity analysis performed for two disease screening modeling studies indicate that ensemble methods such as Random Forests and XGBoost consistently outperform other machine learning algorithms in the prediction task of the associated sensitivity analysis. In addition, we note that active learning can lead to significant speed-ups in sensitivity analysis by enabling the selection of more useful parameter combinations (i.e., instances) to be used for prediction models.