Once widespread throughout the tropical forests of the Indian Subcontinent, the sloth bears have suffered a rapid range collapse and local extirpations in the recent decades. A significant portion of their current distribution range is situated outside of the protected areas (PAs). These unprotected sloth bear populations are under tremendous human pressures, but little is known about the patterns and determinants of their occurrence in most of these regions. The situation is more prevalent in Nepal where virtually no systematic information is available for sloth bears living outside of the PAs. We undertook a spatially replicated sign survey-based single-season occupancy study intending to overcome this information gap for the sloth bear populations residing in the Trijuga forest of southeast Nepal. Sloth bear sign detection histories and field-based covariates data were collected between 2 October and 3 December 2020 at the 74 randomly chosen 4-km 2 grid cells. From our results, the model-averaged site use probability (ψ ± SE) was estimated to be 0.432 ± 0.039, which is a 13% increase from the naïve estimate (0.297) not accounting for imperfect detections of sloth bear signs. The presence of termite mound and the distance to the nearest water source were the most important variables affecting the habitat use probability of sloth bears.The average site-level detectability (p ± SE) of sloth bear signs was estimated to be 0.195 ± 0.003 and was significantly determined by the index of human disturbances.We recommend considering the importance of fine-scale ecological and anthropogenic factors in predicting the sloth bear-habitat relationships across their range in the Churia habitat of Nepal, and more specifically in the unprotected areas.

Local people are the major stakeholders of biodiversity conservation. Human-wildlife conflict (HWC) could result in a negative attitude of the general public towards wildlife adding challenges for conservation. This is more applicable in the landscapes which are outside the protected area (PA) coverage. But, the majority of HWC related studies in Nepal have centered on PAs and their peripheries. This study documents the prevailing situation of HWC in Sundarpur of Udayapur district that shelters some HWC prone wildlife species, while situating outside PA. Data about conflict and people's perception of wildlife conservation was collected using household surveys supplemented by key informant interviews and direct observation. Monkeys (93%, n=93) and elephants (86%, n=86) were found to be the major animals involved in the conflict, mostly resulting in crop raiding, the major form of conflict as reported by (95%, n=95) of respondents. Livestock depredation cases were mostly by common leopard (84%, n=21) and sloth bear was involved in the majority of human attack cases (90%, n=9). The results showed increasing trend of conflicts for elephants (63%, n=63) and monkeys (73%, n=73), while declining trend for sloth bear (64%, n=64), wild boar (85%, n=85), and leopard (46%, n=46). People believed the natural attraction of wildlife towards crops and livestock to be the major driving factor of conflict. Majority of respondents had a positive attitude towards wildlife conservation. However, implementation of community based conflict management strategies, robust compensation schemes along with conservation education programs are highly essential to achieve desired conservation success.

Red ant (Dorylus orientalis Westwood) has long been known as an important pest of potato in middle hills of Nepal. Several management approaches against this pest were studied in Fungling VDC-4 of Taplejung district in 2012. Field efficacy of botanicals (Artemisa, Eupatorium, Agave, Justicia, and Azadirachta) @ 1.44 kg/plot and chemical chlorpyrifos (pyrifos 20 EC @ 2 ml/litre of water) as treatments with four replications were tested using the susceptible variety, Kufri Jyoti. The application of agave was effective to reduce the damage by 42.93 ± 1.20% as compared to control i.e. 68.39±0.62%. Among all the treatments, the application of chemical insecticide, chlorpyrifos gave lower mean yield damage than the control plot. These findings can be verified and applied for the management of red ant of potato under farmers' field condition.

This study was carried out to document the prevailing situation of human-wildlife conflict in Sundarpur of Udayapur district, Nepal where significant numbers of sloth bear along with other troublesome wildlife species occur. Data about conflict and people's perception towards wildlife conservation was collected using household surveys supplemented by key informant interviews and direct observation method. Monkeys (93%) and elephants (86%) were found to be major animals involved in conflict mostly resulting into crop raiding, which was the major form of conflict as reported by (95%) of respondents. Livestock depredation cases were mostly by common leopard (84%) and sloth bear was involved in majority of human attack cases (90%). According to respondents, the trend of conflict was found to be increasing for elephants (63%) and monkeys (73%) while it was found to be decreasing for sloth bear (64%), wild boar (85%), and leopard (46%), where people believed natural attraction of wildlife towards crops/livestock to be the major driving factor of conflict. Despite the prevalence of conflict most of the respondents showed positive attitude towards wildlife conservation in Sundarpur. This implies a better future for wildlife conservation in this area if the issues associated with human-wildlife conflict are addressed effectively.

Clustering in data mining is a way of organizing a set of objects in such a way that the objects in same bunch are more comparable and relevant to each other than to those objects in other bunches. In the modern information retrieval system, clustering algorithms are better if they result high quality clusters in efficient time. This study includes analysis of clustering algorithms k-means and enhanced k-means algorithm over the wholesale customers and wine data sets respectively. In this research, the enhanced k-means algorithm is found to be 5% faster for wholesale customers dataset for 4 clusters and 49%, 38% faster when the clusters size is increased to 8 and 13 respectively. The wholesale customers dataset when classified with 18 clusters the speedup was seen to be 29%. Similarly, in the case of wine dataset, the speed up is seen to be 10%, 30%, 49%, and 41% for 3, 8, 13 and 18 clusters respectively. Both of the algorithms are found very similar in terms of the clustering accuracy.