This study examines the mechanical properties, characterization studies, wear, and absorption tests of hybrid polymer composites reinforced with Aloe vera and ramie fibres. The hybrid composites were manufactured using a hot press compression moulding machine at 110 °C and 1500 PSI. During composite production, Aloe vera and ramie fibres of varying lengths (10 mm, 15 mm, 20 mm, and 25 mm) and weight percentages (10
The bio-based natural fibres are increasingly being used for lightweight structural applications in the automotive and construction industries due to their significant environmental benefits. Water hyacinth fibre composites have been tested previously for mechanical and thermal properties. The vibration, absorption and hardness properties of water hyacinth-reinforced composite samples are tested for the first time in this study. By using the compression moulding technique, a high-performance epoxy polymer matrix is mixed with water hyacinth fibres to produce composite plates with different weight percentages. The 20-mm length of the hyacinth fibre with 20%, 25%, 30% and 35% weight percentage samples is tested in this research. The 20-mm fibre length was calculated by using the critical fibre length formula. The 30% of the hyacinth composite sample hardness property was increased by 14% compared to the other samples. Composites made from water hyacinth plant fibres were capable of dampening vibrations and emitting acoustic disturbances. Based on coincidence frequency analysis and wave number amplitude analysis, 30% water hyacinth fibre-reinforced composites performed the best acoustic performance. The interlinking properties of the composite sample were tested by using the SEM studies.
Despite the increased international attention to whale shark conservation, their populations remain predominantly depleted due to anthropogenic activities such as fishing, ship collisions, and marine pollution. Reports of whale shark strandings in Indonesia have been increasing in recent years, elevating concerns regarding their well-being and the potential disturbance to their population recovery. However, limited understanding of stranding patterns, trends, and the oceanographic factors potentially driving these events has resulted in efforts focusing primarily on responding to strandings rather than implementing effective mitigation strategies. Using a 13-year stranding dataset (n = 115) obtained from open-access databases, reports, news, and publications, we examined the characteristics of stranding cases in Indonesia, including population demographics, where hotspots occur, and whether their occurrence is related to oceanographic dynamics in the region. Our study highlights significant population-level disturbances, with 70% of stranded individuals being large juveniles (4–7 m). It also documented a positive interannual trend in stranding cases (R² = 0.67, p < 0.01). The southern coast of Java has emerged as a stranding hotspot, with events seasonally associated with strong upwelling, likely related to the seasonal foraging activities of whale sharks in the region. Although natural events were identified as the main factors contributing to whale shark strandings, anthropogenic activities may also play an important role and require further investigation.
This research has proven that Radar Absorbing Material (RAM) can be synthesized from natural materials by utilizing iron sand (Fe3O4) from volcanoes and coconut shell charcoal as sources of reduced graphene oxide (rGO). The PANI/rGO/Fe3O4 with Fe3O4 variation 0.5 g (PrGF1); 0.6 g (PrGF2); 0.7 g (PrGF3) were synthesized through a combination of coprecipitation, hummer modification, and in-situ polymerization methods. Characterization was carried out using X-ray diffraction (XRD), scanning electron microscope with energy dispersive spectroscopy (SEM–EDS), electro impedance spectroscopy (EIS), vibrating sample magnetometer (VSM), and vector network analyzer (VNA) to determine the crystal structure, morphology, electrical properties, magnetic properties, and microwave absorption capabilities in the X-band frequency range (8–12 GHz) respectively. The test results showed that increasing the Fe3O4 content strengthened the magnetic properties and supported impedance matching through a balance between dielectric and magnetic losses. The best sample, PrGF3, showed a maximum reflection loss value of − 42.41 dB at a frequency of 10.1 GHz, with a reflection coefficient of 0.0075, meaning 99.25
Accurate bias correction of climate model rainfall projections is essential for hydrological and climate-impact assessments in regions with complex precipitation regimes, such as the Lake Toba basin in Indonesia. This study evaluates four quantile-mapping (QM) approaches—non-parametric, semi-parametric, parametric, and a Sliding 3-Month Window technique—applied to monthly rainfall from four CMIP6 models across 13 observation stations. Ten probability distributions, including the Alpha-Power Transformed X-Lindley (APTXL), Weibull, GEV, and Gamma, were examined as candidate parametric forms. Although Weibull and GEV provided the best statistical goodness-of-fit to historical rainfall, their performance did not translate into effective bias correction. In contrast, APTXL, while not always the best-fitting distribution, delivered the most consistent improvement across correlation, variability skill, and magnitude-based error metrics within the QM framework. The Sliding 3-Month Window parametric quantile-mapping (PQM)–APTXL approach achieved the highest Comprehensive Rating Index (CRI) among all methods and distributions, reflecting its superior ability to address strong seasonal dynamics and preserve interannual variability. Post-correction assessment showed substantial reductions in MAE and centered RMSE for all CMIP6 models, with EC-Earth3-Veg-LR and the ensemble mean emerging as the most reliable sources of corrected rainfall. These findings highlight the importance of evaluating distributions not only by statistical fit but by functional correction performance, and they demonstrate the effectiveness of APTXL-based quantile mapping for bias correction of tropical rainfall. The study provides a robust basis for improving climate model applicability in hydrological modeling and future climate-impact studies for the Lake Toba region. APTXL shows the most consistent performance in bias correction, even though it is not always the best-fitting distribution in statistical evaluation. Sliding 3-Month PQM–APTXL achieves the highest overall correction skill, effectively reducing magnitude errors and preserving interannual variability across CMIP6 models. Post-correction results indicate that EC-Earth3-Veg-LR and the ensemble mean provide the most reliable rainfall estimates, supporting improved climate-impact assessments for the Lake Toba region.
