Changes in weight, volume and oxygen consumption during reorganization and regeneration in Sabella pavonina Sav.

1) During the first period of regeneration and reorganization of fragments from the abdoalnal region. of Sabella, marked by logs of uncini, their weight and volume Increase during the period in which reorganization is started, and respiration remains more or less unchanged.It is suggested that the increase in weight and volume is due to the taking up of water.2) During the succeeding period, marked by loss and degeneration of setae/ and formation of new setae (at room temperature) weight and volume decrease considerably and rapidly while respiration Increases markedly.3) A third period consists of two phases, distinctly separated at lower temparaturesf the first characterized by a decrease of weight without increase of respiration, coinciding with the degenerative processes; the second by loss of weight with a sharp increase in the rato of oxygen consumption » coinciding with the formation of new appendages.4) Anterior fragments from the abdominal region seem to pass more quickly through the degeneration phase. They also show a greater Initial increase in weight than fragments from more posterior regions.5) Fragments amputated after a complete regeneration .and- reorganisation had taken place do not show that rapid degeneration of original appendages, shown by fragments mentioned in (4), out they exhibit a high increase in weight in the early stage, of reorganization.6) Regeneration was found to be markedly delayed by exposure to lortemparaturet and this effect increased with the length of time of exposure. Reorganisation also is delayed but not to the same extent, - and after transference to room temperature the process is rapidly completed.7) These results and their bearing on the interpretation of the phenomena of regeneration and reorganization are discusse

Direct Simulation of Low-Pressure Supersonic Gas Expansions and its Experimental Verification

The use of gas expansions to generate atomic or molecular beams has become a standard technique in nuclear and hadron physics for the production of polarized ion beams and gas targets. A direct simulation Monte Carlo method was used to understand the processes occurring in an expansion of highly dissociated hydrogen or deuterium gas at low densities. The results were verified in several measurements including time-of-flight and beam-profile determinations which showed that the supersonic gas expansions can properly be described by the Monte Carlo calculations. Additionally a new method of beam formation, the hollow carrier jet, was tested under the conditions of the atomic beam source operation

Advancing Use of Key Integrated Pest Management Practices in Schools

Since 2011, Oregon State University has conducted integrated pest management (IPM) training specific to public schools. School personnel receive onsite training on key IPM practices as well as associated materials. To determine which practices and materials school employees are using as a result of the program, we administered a survey to 2016 training attendees. We found that all returning attendees had been implementing practices and using materials as a result of the training. The most common practice was sealing holes to keep pests out. Additionally, the majority of respondents reported a reduction in pesticide use. Our approach may serve as a reference for Extension specialists in developing school IPM programs in other states

A Transformer-Based Classification System for Volcanic Seismic Signals

Volcanic seismic signals are a key element in volcano monitoring to assess the state of unrest and a possible eruption style and timing. Different sources such as brittle fracture (volcano-tectonic - VT) or fluid movement (long period - LP) generate signals with distinct characteristics in frequency content and shape, but site effects such as attenuation or background noise make their determination difficult to the untrained eye. In cases of unrest or an eminent eruption, the amount of data would require a fast and reliable source of pre-classification to classify and catalogue to aid in the job usually done by a human. To model the problem, we will develop a custom-made Transformer model. Transformers are state-of-the-art deep learning methodologies that work with sequence-based data such as audio, text or, in this case, volcanic signals. The power of transformers lies in their ability to identify complex, disconnected patterns and then use them to identify phenomena in a very effective manner. We will be building the model architecture in TensorFlow and will be running them through SHARCNET. Unfiltered continuous data from seismic stations in Villarrica volcano will be used as train dataset and catalogued from at least these two types of events (VT and LP). The model will be then tested with a different set of stations to assess changes in the signal due to attenuation at the site. This will allow to discriminate the same event in different stations

A Transformer-Based Classification System for Volcanic Seismic Signals

Monitoring volcanic events as they occur is a task that, to this day, requires significant human capital. The current process requires geologists to monitor seismographs around the clock, making it extremely labour-intensive and inefficient. The ability to automatically classify volcanic events as they happen in real-time would allow for quicker responses to these events by the surrounding communities. Timely knowledge of the type of event that is occurring can allow these surrounding communities to prepare or evacuate sooner depending on the magnitude of the event. Up until recently, not much research has been conducted regarding the potential for machine learning (ML) models to supplement or substitute human monitoring of volcanoes. Recent initiatives in this field have demonstrated that it is possible to classify volcanic events using ML techniques. Additionally, recent research in general signal processing has shown that the novel technique of multi-head self-attention (MHSA), used in natural language processing (NLP), is also useful in signal analysis. In this report, we seek to apply MHSA to create a deep neural network (DNN) that can automatically classify volcanic events. Our proposed model architecture provides minor improvements over existing approaches on pre-processed data. When considering raw signals coming directly from monitoring stations, our model outperforms existing approaches by a great margin

Relationships among Maximal and Explosive Strength Production of the Leg Extensors and Vertical Jump Peak Power Output in Female Youth Volleyball Athletes

ABSTRACT Architectural and physical performance measurements are commonly implemented to identify various physical capacities in many populations. However, previous research has suggested architectural measures, notably in the leg extensors, are ineffective predictors of vertical jumping (VJ) performance. Given the functional relevance of rapid strength development on explosive dynamic tasks, further research is warranted examining, a) the presence of associations of maximal (e.g., peak torque; PT) and, in particular, explosive (e.g., rate of torque development; RTD) strength-related characteristics with jumping performance in the leg extensors, and b) the extent to which PT and RTD either uniquely, or synergistically contribute to VJ performance. The purpose of this study was to examine the relationships between isometric maximal and explosive strength measures of the leg extensors and VJ peak power (PP) output in female youth volleyball athletes. Thirty (mean ±SD, range: age= 13.73±1.11, 12-17 years, height=162.53±6.39 cm, body mass=57.84±12.05 kg) female youth competitive volleyball players reported to the laboratory on two occasions, with the first visit being a familiarization session. The second visit involved experimental testing, in which participants performed two isometric maximal voluntary contractions of the leg extensors on a dynamometer at a leg angle of 60º, followed by three countermovement VJ trials. Subjects performed countermovement jumps, starting in a standing position and feet firmly on the ground. Following the descent to the midpoint position and without pause, the subjects exploded upward as hard and fast as possible. PT and RTD were calculated as the highest 500ms epoch and the slope of the rise in torque in the first 200ms from onset, respectively. Lower-body PP was assessed using a linear velocity transducer, which was attached to the posterior side of a belt that was securely fastened to the subjects’ waistline. Pearson correlation (r) and stepwise linear regression analyses were performed to examine the relationships. Results indicated that both PT (r=0.7) and late RTD (r=0.62) were significantly correlated to PP (p≤0.01). However, linear regression analysis revealed that PT was the only variable entered into the stepwise regression model (R=0.71; R²=0.50). These findings showed that while both maximal and explosive strength variables correlated with VJ performance, only PT was necessary to effectively predict PP output with no additional explained variance from RTD. Thus, training regimens aimed at development of high force production of the leg extensors may enhance PP production during explosive vertical jump tasks more so than enhancing early rapid force production