Search

Ghost forests and abandoned farms are stark indicators of ecological change along world coastlines, caused by sea level rise (SLR). These changes adversely affect terrestrial ecosystems and economies, but expanding coastal marshes resulting from SLR also provide crucial ecosystem services such as carbon sequestration and mediate material fluxes to the ocean. We introduce a US-NSF Critical Zone Network project designed to untangle the hydrological, ecological, geomorphological, and biogeochemical processes that are altering the functioning of the marsh-upland transition in the coastal critical zone. We have instrumented six sites in the mid-Atlantic region of the US, along the coastlines of the Atlantic Ocean, Delaware Bay, and Chesapeake Bay where marshes are rapidly encroaching into forests and farmland. We have installed field sensors to observe the effects of slow hydrologic change (i.e. SLR) and fast episodic events such as high tides and storm surges on water levels, land surface elevation, salinity, redox conditions, and sap flow. We are coupling these measurements to laboratory experiments and analyses, as well as modeling to elucidate drivers and feedbacks in these complex and highly transient critical zone systems.

Interpreting time domain reflectometry (TDR) waveforms obtained in soils with non-uniform water content is an open question. We design a new TDR waveform interpretation model based on convolutional neural networks (CNNs) that can reveal the spatial variations of soil relative permittivity and water content along a TDR sensor. The proposed model, namely TDR-CNN, is constructed with three modules. First, the geometrical features of the TDR waveforms are extracted with a simplified version of VGG16 network. Second, the reflection positions in a TDR waveform are traced using a 1D version of the region proposal network. Finally, the soil relative permittivity values are estimated via a CNN regression network. The three modules are developed in Python using Google TensorFlow and Keras API, and then stacked together to formulate the TDR-CNN architecture. Each module is trained separately, and data transfer among the modules can be facilitated automatically. TDR-CNN is evaluated using simulated TDR waveforms with varying relative permittivity but under a relatively stable soil electrical conductivity, and the accuracy and stability of the TDR-CNN are shown. TDR measurements from a water infiltration study provide an application for TDR-CNN and a comparison between TDR-CNN and an inverse model. The proposed TDR-CNN model is simple to implement, and modules in TDR-CNN can be updated or fine-tuned individually with new datasets. In conclusion, TDR-CNN presents a model architecture that can be used to interpret TDR waveforms obtained in soil with a heterogeneous water content distribution., One popular way to measure water content in a whole soil profile is to insert an array of short TDR sensors at multiple, pre-specified depths. Despite the efforts needed to install the sensors and the potential disturbance of nearby soils, it provides discretized measurements in space. Instead, vertically inserting long TDR sensors could provide measurement without the restriction of pre-specific depths, but the spatial variation of soil water content along the TDR sensor rods needs to be reconstructed. That is an open and challenging question in TDR data interpretation studies. We developed a deep machine learning model that can compute the soil water content distributions along the sensor rods, and the model performance is evaluated using simulated TDR data with varying soil water content but under relatively stable electrical conductivity. The artificial neural network architecture in this study presents a model prototype, which is important for scientists and engineers in TDR data analysis, sensor design and related industries.

Background: General practitioners (GPs) and other health professionals have a key role in signposting their patients to appropriate opportunities for engaging in arts and creative activities for the health and wellbeing benefits they may bring. Training is needed to ensure that GPs are aware of the evidence supporting the role of the arts, and the local availability of ‘creative arts for health’ activities for their patients. Aim: This article describes the content and evaluation of three arts and health training events for trainee GPs conducted over the period 2016–2019. They took place in association with Guy’s and St Thomas, Hillingdon and York and Scarborough General Practice Vocational Training Schemes (GPVTS). Methods: Evaluation was undertaken for the London events using a specially constructed questionnaire, with rating scales, completed before and after the training events. For the York event, a simple bespoke evaluation questionnaire was employed at the end of the training day. All participants gave consent for photography and filming during the events. Results: Feedback from GPs on the training events revealed increased awareness of the evidence for arts and health interventions, and more positive attitudes towards the role of creative arts in primary care in promoting the health and wellbeing of patients. An additional finding was a recognition by GP trainees that creative activities can enhance a holistic approach to patient care and play a positive part in supporting their own wellbeing. Conclusion: Training events of the kind described, with opportunities for creative participation for GP trainees, can enhance awareness of the benefits of creative activity for patient health and wellbeing, and may motivate future GPs to signpost patients towards opportunities for engaging in creative activities. This training model is applicable for any health worker who can refer patients to arts for health activities and has potential to be scaled up nationally.

Objectives In this new era of educational milestones and entrustable professional activities, residency programs have recognized the need to directly observe resident performance. In fact, there is little information about how often residents are observed, what procedures they perform early in training, and whether they receive feedback. Previous publications have addressed these issues exclusively through retrospective survey analyses. The purpose of this naturalistic point-of-care study was to answer the following questions about obstetrics/gynecology (OB/GYN) residents in their second month of training: what activities do residents report performing, how often are they observed and who observes them, how often do they receive feedback and what is their perception of its usefulness, and does the time of shift affect the frequency of observation and feedback? Methods Nine of 10 first-year OB/GYN residents at George Washington University Hospital participated in a month-long study during their second month of training. Based on point-of-care experiences, participants prospectively recorded the time of shift, activities performed, the person who observed them, whether they received feedback, and whether it was perceived as helpful. Results First-year OB/GYN residents (postgraduate year 1 [PGY-1]) perform a variety of activities early in training while being observed by senior residents, nurses, and attending physicians 70% of the time. Residents commented that feedback was helpful almost every time they received it, regardless of who provided the feedback. There were no significant differences in the quantity of observations and feedback received between day and night shifts; however, nurses and senior residents were most likely to observe residents during night shifts. Conclusions In this naturalistic pilot study, OB/GYN residents reported performing various procedures in their second month of training, with some observations from faculty, senior residents, and nurses. Feedback, as defined in the study, is an important aspect of their early training, although it is not reported with each patient encounter. This pilot study raises critical issues that need further study, such as the following: What should be the gold standard for observing residents around a particular activity? Where should the bar be set for types and numbers of procedures that residents perform early on in training, either with patients or in simulations? What is an acceptable feedback rate around patient encounters? Should we not consider training nurses and senior residents to deliver effective feedback to residents as part of a 360-degree process, because many trainees were observed most frequently by these members of the medical team?

This paper presents results based on a series of indentation tests (hobbing) performed on supported billets of Coronze, a superplastic alloy of copper, aluminium, silicon and cobalt. The relationship between hobbing force, depth of indentation and strain rate was established. An upper bound technique was used to predict the hobbing pressure ratio and the results are compared with those from the experimental tests. The feasibility of using this material to produce a die for a polymer injection moulding machine was also assessed.