Feasibility study of intelligent autonomous determination of the bladder voiding need to treat bedwetting using ultrasound and smartphone ML techniques

Unsatisfactory cure rates for the treatment of nocturnal enuresis (NE), i.e. bed-wetting, have led to the need to explore alternative modalities. New treatment methods that focus on preventing enuretic episodes by means of a pre-void alerting system could improve outcomes for children with NE in many aspects. No such technology exists currently to monitor the bladder to alarm before bed-wetting. The aim of this study is to carry out the feasibility of building, refining and evaluating a new, safe, comfortable and non-invasive wearable autonomous intelligent electronic device to monitor the bladder using a single-element low-powered low-frequency ultrasound with the help of Machine Learning techniques and to treat NE by warning the patient at the pre-void stage, enhancing quality of life for these children starting from the first use. The sensitivity and specificity values are 0.89 and 0.93 respectively for determining imminent voiding need. The results indicate that customised imminent voiding need based on the expansion of the bladder can be determined by applying a single-element transducer on a bladder in intermittent manner. The acquired results can be improved further with a comfortable non-invasive device by adding several more features to the current features employed in this pilot study

Probabilistic fire spread forecast as a management tool in an operational setting

Background: An approach to predict fire growth in an operational setting, with the potential to be used as a decision-support tool for fire management, is described and evaluated. The operational use of fire behaviour models has mostly followed a deterministic approach, however, the uncertainty associated with model predictions needs to be quantified and included in wildfire planning and decision-making process during fire suppression activities. We use FARSITE to simulate the growth of a large wildfire. Probabilistic simulations of fire spread are performed, accounting for the uncertainty of some model inputs and parameters. Deterministic simulations were performed for comparison. We also assess the degree to which fire spread modelling and satellite active fire data can be combined, to forecast fire spread during large wildfires events. Results: Uncertainty was propagated through the FARSITE fire spread modelling system by randomly defining 100 different combinations of the independent input variables and parameters, and running the correspondent fire spread simulations in order to produce fire spread probability maps. Simulations were initialized with the reported ignition location and with satellite active fires. The probabilistic fire spread predictions show great potential to be used as a fire management tool in an operational setting, providing valuable information regarding the spatial–temporal distribution of burn probabilities. The advantage of probabilistic over deterministic simulations is clear when both are compared. Re-initializing simulations with satellite active fires did not improve simulations as expected. Conclusion: This information can be useful to anticipate the growth of wildfires through the landscape with an associated probability of occurrence. The additional information regarding when, where and with what probability the fire might be in the next few hours can ultimately help minimize the negative environmental, social and economic impacts of these firesinfo:eu-repo/semantics/publishedVersio

Future challenges for woody biomass projections

Many drivers affect woody biomass projections including forest available for wood supply, market behavior, forest ownership, distributions by age and yield classes, forest typologies resulting from different edaphic, climatic conditions, and last but not least, how these factors are incorporated into projection systems. Net annual increment has been considered a useful variable for estimating future wood and biomass supply, but it can be misleading. In Europe, two different approaches have been used: a common European-level tool for all countries (“top-down” approach); and national tools (“bottom-up” approach). The trade-offs are that the “top-down” approach produces comparable results among countries, but ignores most of the topographic, climatic, vegetative and socio-economic conditions that are unique to countries and regions. The “bottom-up” approach better accommodates national and regional conditions but at the cost of comparability among country level results. A brief discussion of how these issues are handled in North America provides insights into different approaches and their linkages to national circumstances regarding country sizes, ownerships and general political frameworks. Another challenge lies in accommodating climate change and uncertainty in projections. Finally, working closely with experts from the demand side to minimize possible misunderstandings is also required. The first step towards increasing comparability of results from country-level projection systems is to understand the differences among these tools. Only then, can progress be made in terms of harmonizing the input and output variables or even progressing towards a common methodological approach and software structure