Fragmentation and fission of giant molecular clouds occasionally results in a pair of gravitationally bound star clusters that orbit their mutual centre of mass for some time, under the influence of internal and external perturbations. We investigate the evolution of binary star clusters with different orbital configurations, with a particular focus on the Galactic tidal field. We carry out N-body simulations of evolving binary star clusters and compare our results with estimates from our semi-analytic model. The latter accounts for mass-loss due to stellar evolution and two-body relaxation, and for evolution due to external tides. Using the semi-analytic model, we predict the long-term evolution for a wide range of initial conditions. It accurately describes the global evolution of such systems, until the moment when a cluster merger is imminent. N-body simulations are used to test our semi-analytic model and also to study additional features of evolving binary clusters, such as the kinematics of stars, global cluster rotation, evaporation rates, and the cluster merger process. We find that the initial orientation of a binary star cluster with respect to the Galactic field, and also the initial orbital phase, is crucial for its fate. Depending on these properties, the binaries may experience orbital reversal, spiral-in, or vertical oscillation about the Galactic plane before they actually merge at t ≈ 100 Myr, and produce rotating star clusters with slightly higher evaporation rates. The merger process of a binary cluster induces an outburst that ejects ∼10 per cent of the stellar members into the Galactic field.

A new astronomical observatory in south-eastern Indonesia is currently under construction. This Timau National Observatory will host a 3.8-m telescope for optical and near-infrared observations. To support the operation and planning, the characterization of the site needs to be appropriately performed. However, limited resources and access to the site hindered the deployment of instruments for comprehensive site testing. Fortunately, in situ sky brightness data from the Sky Quality Meter (SQM) have been available for almost 2 yr. Based on the data acquired in 470 nights, we obtain a background sky brightness of μ₀ = 21.86 ± 0.38 mag arcsec⁻². Additionally, we evaluate the moonlit sky brightness to estimate the atmospheric extinction coefficient (k) and level of scattering on site. We find an elevated value of k = 0.48 ± 0.04, associated with a high atmospheric aerosol content. It is considered regular for an equatorial area situated at a low altitude (∼1300 masl). By analysing the fluctuation of the sky brightness and infrared images from Himawari-8 satellite, we estimate the available observing time (AOT) of at least 5.3 h/night and the yearly average percentage of usable nights of |$66{{\ \rm per\ cent}}$|⁠. The monthly average AOT from SQM and satellite data analysis correlate with R = 0.82. In terms of the monthly percentage of usable nights, the correlation coefficient is R = 0.78. During the wet season (November–April), the results from SQM and satellite data analysis deviate more significantly, mainly due to the limited capability of Himawari-8 in detecting fragmented low-altitude clouds. According to these results, we expect Timau to complement other observatories greatly.

Global change impacts including climate change, increased CO₂ and nitrogen deposition can be determined through a more precise characterisation of Land Surface Phenology (LSP) parameters. In addition, accurate estimation of LSP dates is being increasingly used in applications such as mapping vegetation types, yield forecasting, and irrigation management. However, there has not been any attempt to characterise Middle East vegetation phenology at the fine spatial resolution appropriate for such applications. Remote-sensing based approaches have proved to be a useful tool in such regions since access is restricted in some areas due to security issues and their inter-annual vegetation phenology parameters vary considerably because of high uncertainty in rainfall. This study aims to establish for the first time a comprehensive characterisation of the vegetation phenological characteristics of the major vegetation types in the Middle East at a fine spatial resolution of 30 m using Landsat Normalized Difference Vegetation Index (NDVI) time series data over a temporal range of 20 years (2000–2020). Overall, a progressive pattern in phenophases was observed from low to high latitude. The earliest start of the season was concentrated in the central and east of the region associated mainly with grassland and cultivated land, while the significantly delayed end of the season was mainly distributed in northern Turkey and Iran corresponding to the forest, resulting in the prolonged length of the season in the study area. There was a significant positive correlation between LSP parameters and latitude, which indicates a delay in the start of the season of 4.83 days (R² = 0.86, p < 0.001) and a delay in the end of the season of 6.54 days (R² = 0.83, p < 0.001) per degree of latitude increase. In addition, we have discussed the advantages of fine resolution LSP parameters over the available coarse datasets and showed how such outputs can improve many applications in the region. This study shows the potential of Landsat data to quantify the LSP of major land cover types in heterogeneous landscapes of the Middle East which enhances our understanding of the spatial-temporal dynamics of vegetation dynamics in arid and semi-arid settings in the world.

Sky brightness measuring and monitoring are required to mitigate the negative effect of light pollution as a byproduct of modern civilization. Good handling of a pile of sky brightness data includes evaluation and classification of the data according to its quality and characteristics such that further analysis and inference can be conducted properly. This study aims to develop a classification model based on Random Forest algorithm and to evaluate its performance. Using sky brightness data from 1250 nights with minute temporal resolution acquired at eight different stations in Indonesia, datasets consisting of 15 features were created to train and test the model. Those features were extracted from the observation time, the global statistics of nightly sky brightness, or the light curve characteristics. Among those features, 10 are considered to be the most important for the classification task. The model was trained to classify the data into six classes (1: peculiar data, 2: overcast, 3: cloudy, 4: clear, 5: moonlit-cloudy, and 6: moonlit-clear) and then tested to achieve high accuracy (92) and scores (F-score=84 and G-mean=84). Some misclassifications exist, but the classification results are considerably good as indicated by posterior distributions of the sky brightness as a function of classes. Data classified as class-4 have sharp distribution with typical full width at half maximum of 1.5mag/arcsec², while distributions of class-2 and -3 are left skewed with the latter having lighter tail. Due to the moonlight, distributions of class-5 and -6 data are more smeared or have larger spread. These results demonstrate that the established classification model is reasonably good and consistent.

Ludendorff coronal flattening index of the Total Solar Eclipse (TSE) on March 9, 2016, was calculated at various distances in solar radius. As a result, we obtained the coronal flattening index ( = a + b) at a distance of 2 solar radii is 0.16. The 24^th solar cycle phase based on the 2016 TSE event obtained -0.64 which showed the corona is pre-minimum type. Resulted coronal flattening index value gives a predicted maximum amplitude of the monthly sunspot number (W_max) for the 25^th solar cycle to be 70 ± 65. Therefore, the solar activity for 25^th solar cycle predicted to be lower than the current solar cycle, which has a maximum sunspot number value of 146 in February 2014