Northern Appalachian, USA relic charcoal hearths and their unique Ecological Fingerprint

<p>Abrupt changes in a forest ecosystem, whether natural or anthropogenic, are changes that occur over short time periods; such disturbance has the potential to drive state changes and alter forest resilience. Understanding how present-day abrupt forest change may alter ecosystem services is becoming more important due to ever-growing anthropogenic stresses. Forest managers trying the adapt to anthropogenic stress can benefit from the study and quantification of past abrupt changes in forests, especially when the legacy of past disturbance is still evident. Across the United Kingdom, Europe, and recently the northeastern United States, the examination of historic forest change due to charcoal manufacturing for the firing of iron or lime furnaces is yielding new insights relative to landscape stability, anthropogenic vs natural soil genesis, and forest evolution. We present results of a study that strives to evaluate how historic land clearing for the charcoal industry (supporting iron furnaces) affected local soils and may drive surrounding present day forest composition. We incorporate field sampling of hearth soils and modeled hydrologic parameters (in hearth and non-hearth areas), to quantify the uniqueness of relict charcoal hearth (RCH) systems. We identified 1,239 hearths using a LiDAR terrain analysis; approximately 10% of these were visited to quantify hearth morphology and soil moisture differences on and off hearth. Nine hearths from this 10% were intensively sampled and were associated with a northern Appalachian, USA furnace that was in operation from 1867 to 1904. Three profiles were excavated across each hearth and compared to an adjacent soil profile on the same contour. Soil descriptions were made of hearths and soil samples analyzed for total, trace and rare earth element content (Aqua Regia digestion). Soil pH (water) and fertility (Mehlich III extraction) were also determined. Results indicate that hearths have a unique geochemistry with higher bases and some concentrated metals and higher organic carbon. Coupled with a higher hearth soil water content, hypothesized to be due to an observed restrictive subsurface morphology and higher organic carbon, hearths are potentially unique locations of refugia for forest flora and fauna. Future research should more closely investigate whether hearths support unique species assemblages and how they may play a role in enhancing today’s forest biodiversity.</p>

Surrogate Model-Based Uncertainty Quantification for a Helical Gear Pair

Competitive industrial transmission systems must perform most efficiently with reference to complex requirements and conflicting key performance indicators. This design challenge translates into a high-dimensional multi-objective optimization problem that requires complex algorithms and evaluation of computationally expensive simulations to predict physical system behavior and design robustness. Crucial for the design decision-making process is the characterization, ranking, and quantification of relevant sources of uncertainties. However, due to the strict time limits of product development loops, the overall computational burden of uncertainty quantification (UQ) may even drive state-of-the-art parallel computing resources to their limits. Efficient machine learning (ML) tools and techniques emphasizing high-fidelity simulation data-driven training will play a fundamental role in enabling UQ in the early-stage development phase.This investigation surveys UQ methods with a focus on noise, vibration, and harshness (NVH) characteristics of transmission systems. Quasi-static 3D contact dynamic simulations are performed to evaluate the static transmission error (TE) of meshing gear pairs under different loading and boundary conditions. TE indicates NVH excitation and is typically used as an objective function in the early-stage design process. The limited system size allows large-scale design of experiments (DoE) and enables numerical studies of various UQ sampling and modeling techniques where the design parameters are treated as random variables associated with tolerances from manufacturing and assembly processes. The model accuracy of generalized polynomial chaos expansion (gPC) and Gaussian process regression (GPR) is evaluated and compared. The results of the methods are discussed to conclude efficient and scalable solution procedures for robust design optimization.

Self-generated whisker movements drive state-dependent sensory input to developing barrel cortex

SummaryCortical development is an activity-dependent process [1–3]. Regarding the role of activity in developing somatosensory cortex, one persistent debate concerns the importance of sensory feedback from self-generated movements. Specifically, recent studies claim that cortical activity is generated intrinsically, independent of movement [3, 4]. However, other studies claim that behavioral state moderates the relationship between movement and cortical activity [5–7]. Thus, perhaps inattention to behavioral state leads to failures to detect movement-driven activity [8]. Here, we resolve this issue by associating local field activity (i.e., spindle bursts) and unit activity in the barrel cortex of 5-day-old rats with whisker movements during wake and myoclonic twitches of the whiskers during active (REM) sleep. Barrel activity increased significantly within 500 ms of whisker movements, especially after twitches. Also, higher-amplitude movements were more likely to trigger barrel activity; when we controlled for movement amplitude, barrel activity was again greater after a twitch than a wake movement. We then inverted the analysis to assess the likelihood that increases in barrel activity were preceded within 500 ms by whisker movements: At least 55% of barrel activity was attributable to sensory feedback from whisker movements. Finally, when periods with and without movement were compared, 70–75% of barrel activity was movement-related. These results confirm the importance of sensory feedback from movements in driving activity in sensorimotor cortex and underscore the necessity of monitoring sleep-wake states to ensure accurate assessments of the contributions of the sensory periphery to activity in developing somatosensory cortex.

Sleep disorders

Dysfunctional sleep is an important cause of morbidity and is associated with numerous long-term health problems. Sleep-related symptoms can loosely be divided into insomnias, disorders causing excessive daytime sleepiness, and parasomnias, with some conditions having elements of all three categories. The need to sleep is imperative, reflecting the fact that sleepiness, similar to hunger and thirst, is a true drive state. Although its function remains largely elusive, disordered sleep can be associated with profound adverse effects on cognition, mental health, and physical well-being. Moreover, sleep-related symptoms are very common, with 25% of people reporting problems that significantly and regularly impact on daily activities.