License GPL-2 NeedsCompilation No Repository CRAN

Description Various R programming tools for plotting dat

Suitability for salt-lick tourism: A preliminary assessment on the natural saltlicks at Segaliud-Lokan Forest Reserve, Sandakan, Sabah

The natural salt-licks are visited by various species of terrestrial mammals, hence ideal for mammal watching, although the suitability for salt-lick tourism is influenced by other factors. Preliminary research was conducted on the suitability of four natural salt-licks for mammal watching in Segaliud-Lokan Forest Reserve (SLFR), Sabah. The camera trapping survey and field assessment were conducted for eight months, and then the assessment criteria applied in this study included the lick accessibility, detection frequency, species richness, viewable activity sighting probability, reliability and visibility on the terrestrial mammals, at a given lick. A total of 12 different mammal species were recorded, where Sambar Deer, Bearded Pig, Banteng, and Bornean Orang-utan were determined as the main visitor species of this study. Among the four selected salt-licks, the rating score of SL50A (Score=1.71) was significantly lower than those of SL50B, SL56 and SL59 (Score=2.57 respectively, χ2 2=6.794, p=0.042), hence highlighting that SL50A was not suitable for conducting mammal watching activity, unlike the other three natural licks at SLFR. The assessment on the compatibility between the supply (mammalian physical availability) and demand (highly anticipated species) was excluded from this research, therefore emphasizing the need to fill up this particular research gap in the future

Feasibility of Natural Salt-licks for Wildlife-Watching in Segaliud-Lokan Forest Reserve, Sandakan, Sabah

Natural salt-licks are well-recognized as wildlife-watching hotspots that can provide visitors with high opportunities for sighting many different outstanding mammals at close-range. Various natural salt-licks were discovered throughout Segaliud-Lokan Forest Reserve (SLFR), but then the physical availability of local mammals at given licks were yet to be examined scientifically by past researchers. Henceforth, this study intended to investigate mammal species that were available for wildlife-viewing at the licks in SLFR. Four natural wet licks that were accessible from the main road and situated close to Sungai Rawog were selected as sampling areas to identify mammal species that visited given licks across different times through camera trapping survey. A total of 676 independent sightings of 12 different mammal species were recorded in 197 trap nights, especially at SL59 and during night-time. Sighted mammal individuals were mainly comprised of large-sized, threatened and non-carnivorous species, where Sambar Deer, Bearded Pig, Orang-utan, and Banteng were identified as the top 4 mammal species that were detected frequently at the licks in SLFR. In sum, it is feasible to conduct wildlife-viewing activity at the licks in SLFR, although further research is required to investigate the actual sighting probability and viewing duration of different mammal species by visitors at given licks and across different times or seasons

Professor Anthony P. F. Turner: An innovative educator and pioneer of biosensors in the 21st century (On his 60th birth anniversary)

In the pharmaceutical industry it is common to generate many QSAR models from training sets containing a large number of molecules and a large number of descriptors. The best QSAR methods are those that can generate the most accurate predictions but that are not overly expensive computationally. In this paper we compare eXtreme Gradient Boosting (XGBoost) to random forest and single-task deep neural nets on 30 in-house data sets. While XGBoost has many adjustable parameters, we can define a set of standard parameters at which XGBoost makes predictions, on the average, better than those of random forest and almost as good as those of deep neural nets. The biggest strength of XGBoost is its speed. Whereas efficient use of random forest requires generating each tree in parallel on a cluster, and deep neural nets are usually run on GPUs, XGBoost can be run on a single CPU in less than a third of the wall-clock time of either of the other methods

Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships

Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision, and natural language processing., In the past four years, DNNs have also generated promising results for quantitative structure–activity relationship (QSAR) tasks., Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR data sets. It was also found that multitask DNN modelsthose trained on and predicting multiple QSAR properties simultaneouslyoutperform DNNs trained separately on the individual data sets in many, but not all, tasks. To date there has been no satisfactory explanation of why the QSAR of one task embedded in a multitask DNN can borrow information from other unrelated QSAR tasks. Thus, using multitask DNNs in a way that consistently provides a predictive advantage becomes a challenge. In this work, we explored why multitask DNNs make a difference in predictive performance. Our results show that during prediction a multitask DNN does borrow “signal” from molecules with similar structures in the training sets of the other tasks. However, whether this borrowing leads to better or worse predictive performance depends on whether the activities are correlated. On the basis of this, we have developed a strategy to use multitask DNNs that incorporate prior domain knowledge to select training sets with correlated activities, and we demonstrate its effectiveness on several examples