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Research on flood disaster generate ideas and provoke the best solution for disaster management. This work primarily focuses research on monsoon flood due to its frequency and severity in the southern flood plain of Nepal. Here we review the previous studies on flood disaster at the regional and national level and compare with the global context. This facilitates exploring the data and methods that are mostly unexplored, and areas that have not lightened in the field of flood studies in Nepal. Our scope of literature review limited the literature that are accessed through internet. The findings are revised and compared with different contexts. Multi-criteria weighted arithmetic mean have been used to find the spatial severity of flood disaster in 2017. We found several studies carried out on flood in Nepal. They are mostly based on field-based data, except few that have used current state-of-art, remote sensing method, using satellite images. Since the multi-spectral optical satellite imageries have a high cloud effect, it is not very useful in real time flood mapping; and very limited Synthetic-Aperture Radar (SAR) image, has been used in Nepal. In Global context, Support Vector Machine and Random Forest method are used in flood risk assessment; VNG flood V1.0 software has been used in flood forecasting, and Probabilistic Change Detection and Thresholding have widely been used in flood research, which can also be adopted in Nepalese context.

Understanding the development mechanism of drought events, characterization of future drought metrics, and its impact on crop yield is crucial to ensure food security globally, and more importantly, in South Asia. Therefore, the present study assessed the changes in future projected drought metrics and evaluated the future risk of yield reduction under drought intensity. We characterized the magnitude, intensity, and duration of future drought by means of the SPEI drought index using CMIP6 (Coupled Model Inter-comparison Phase-6) climate models. The impact of future drought on crop yield was quantified from the ISI-MP (Inter-Sectoral Impact Model Inter-comparison Project) crop model by a proposed non-linear ensemble of Random Forest (RF) and Gradient Boosting Machine (GBM). Results suggested that high drought magnitude with a longer drought duration is projected in some regions of South Asia while high drought intensity comes with a shorter duration. It was also found that Afghanistan, Pakistan, and India will experience a longer drought duration in the future. Our proposed ensemble machine learning (EML) approach had high predictive skill with a minimum value of RMSE (0.358-0.390), MAE (0.222-0.299), and a maximum value of R2 (0.705-0.918) compared to the stand-alone methods of RF and GBM for yield loss risk projection. The drought-driven impact on crop yield demonstrates a high risk of yield loss under extreme drought events, which will encounter 54.15%, 29.30%, and 50.66% loss in the future for rice, wheat, and maize crops, respectively. Furthermore, drought and yield loss risk dynamics suggested a one unit decrease in SPEI value would lead to a 14.2%, 7.5%, and 10.9% decrease in yield for rice, wheat, and maize crops, respectively. This study will provide a notable direction for policy agencies to build resistance to crop production against the drought impact in the regions that are critical to climate change.

Landslide susceptibility assessment along the Araniko highway was done using the relationship between the landslide causative factor and presence/absence of landslide using linear discriminant analysis, and divided into Low, Medium, High, and Very High susceptibility zone. The spatial analysis of landslide distribution with its conditioning factors depicts 40°-60° of the slope, South-east and South direction of aspect, 0–5 km North from MCT, 10–20 km of distance from the epicentre, where barren land and forest area are found most susceptible to a landslide. This research can help undertake the proper mitigation and adaptation measures for the landslide risk along the Araniko highway. Keywords: Landslides, Linear discriminant analysis, Susceptibility, Araniko Highway, Factors

Two recent indigenous cases of tinea capitis in children due to pale isolates of Trichophyton violaceum are reported herein for the first time from South-East Europe (Greece). Pale isolates of Trichophyton violaceum, reported in the past as Trichophyton glabrum, are thus far sporadically reported only from African or Asian countries. The cases reported herein raise the awareness of its existence in the community, assigning special importance to its accurate identification in the clinical laboratory.

The usual Metropolis Monte Carlo algorithm, when applied to highly associated liquids like ST-2 water is shown to be very slow in establishing equilibrium, and often leads to bottlenecks in configuration space. A new algorithm is presented in which the Monte Carlo moves are biased in the direction of the forces and torques acting on the individual molecule. Comparison with the Metropolis shows that this new method is much more rapidly convergent.

Using various software tools, the Monte Carlo SU(3) lattice gauge theory algorithm is optimized on the Fujitsu VP200 Vector Processor (to be available outside Japan as the Amdahl 1200). The link update time, working in 64-bit arithmetic, is 25.85 μs on a 12 4 lattice with 9 hits-per-link.