Crisis Management: Addressing the Impact of Insufficient Resources and Supports on Middle School Students\u27 Mental Health

This Dissertation-in-Practice (DiP) addresses a problem of practice which is an urgent need for enhanced mental health supports at Alpine Prairie Middle School (APMS) within the Western Canadian School District (WCSD). Amidst resource and budgetary constraints, this study explores holistically informed strategies grounded in the principal\u27s positionality and grounded by critical theory. This DiP focusses on transformative and distributed leadership approaches, considering the school as a complex adaptive system. A leadership framework integrates critical theory, transformative leadership, distributed leadership, and pragmatism to effectively lead the change process. The implementation plan includes a four-step change model: awakening, mobilization, acceleration, and institutionalization, tailored for APMS. This model aligns the organization with changes involving mental health literacy (MHL) and transformative social emotional learning (tSEL), managing the transition smoothly. Comprehensive communication and evaluation plans ensure successful integration of change strategies into the school\u27s operations, supporting and improving students\u27 mental health. Adopting a holistic approach, emphasizing equity, diversity, inclusion, and decolonization (EDID), this DiP describes a supportive and resilient environment where every student can thrive. This DiP also emphasizes the importance of a proactive stance on student mental health, the need for ongoing professional development for educators, and the value of involving the entire school community in mental health initiatives. By addressing these critical areas, the DiP seeks to create a more inclusive and supportive educational environment for all students at APMS. The findings may also inform future initiatives to improve mental health supports in middle schools

Acquisition Challenges of Autonomous Systems

The Department of Defense has stated publicly that future defense capabilities will depend strongly on autonomous systems;systems that make sophisticated judgments about the world and choose appropriate courses of action, and perhaps even adapt and learn over time. Developing and deploying such systems poses more than just a technical challenge in robotics and artificial intelligence;it also poses many challenges to the acquisition process and workforce. From cost estimation to sustainment planning, every aspect of acquisition will be affected. Test and evaluation, in particular, may require not only novel methodologies and resources, but organizational and process changes as well.Naval Postgraduate School Acquisition Research Progra

Highly challenging balance program reduces fall rate in Parkinson disease

Published in final edited form as: J Neurol Phys Ther. 2016 January ; 40(1): 24–30. doi:10.1097/NPT.0000000000000111BACKGROUND AND PURPOSE: There is a paucity of effective treatment options to reduce falls in Parkinson disease (PD). Although a variety of rehabilitative approaches have been shown to improve balance, evidence of a reduction in falls has been mixed. Prior balance trials suggest that programs with highly challenging exercises had superior outcomes. We investigated the effects of a theory-driven, progressive, highly challenging group exercise program on fall rate, balance, and fear of falling. METHODS: Twenty-three subjects with PD participated in this randomized cross-over trial. Subjects were randomly allocated to 3 months of active balance exercises or usual care followed by the reverse. During the active condition, subjects participated in a progressive, highly challenging group exercise program twice weekly for 90 minutes. Outcomes included a change in fall rate over the 3-month active period and differences in balance (Mini-Balance Evaluation Systems Test [Mini-BESTest]), and fear of falling (Falls Efficacy Scale-International [FES-I]) between active and usual care conditions. RESULTS: The effect of time on falls was significant (regression coefficient = -0.015 per day, P < 0.001). The estimated rate ratio comparing incidence rates at time points 1 month apart was 0.632 (95% confidence interval, 0.524-0.763). Thus, there was an estimated 37% decline in fall rate per month (95% confidence interval, 24%-48%). Improvements were also observed on the Mini-BESTest (P = 0.037) and FES-I (P = 0.059). DISCUSSION AND CONCLUSIONS: The results of this study show that a theory-based, highly challenging, and progressive exercise program was effective in reducing falls, improving balance, and reducing fear of falling in PD.Video abstract available for more insights from the authors (see Supplemental Digital Content 1, http://links.lww.com/JNPT/A120). TRIAL REGISTRATION: ClinicalTrials.gov NCT02302144.This study was funded by the Boston Claude D. Pepper Older Americans Independence Center (NIH 5P30AG031679). Additional support was provided by the American Parkinson Disease Association (ADPA); ADPAMA Chapter. (NIH 5P30AG031679 - Boston Claude D. Pepper Older Americans Independence Center; American Parkinson Disease Association (ADPA); ADPAMA Chapter

Assessing long-term future climate change impacts on extreme low wind events for offshore wind turbines in the UK exclusive economic zone

The impacts of climate change must be considered while planning offshore wind turbines (OWT), as it will result in more frequent and severe weather extremes. To ensure the dependability and affordability of wind energy, it is necessary to address extreme low wind speed events (LWE). This study aims to assess the reliability of wind power in the future by analyzing the rise of low wind durations and intensities in two future periods, 2021–2040 and 2061–2080, compared to the historical period of 1981–2000. The research compares the results for four main regions in the UK EEZ: East, South, West, and North. We examine different cut-in thresholds of 3 m/s, 4 m/s, 5 m/s, and 6 m/s in the UK exclusive economic zone (EEZ). The seasonal variations in LWE durations <4 m/s demonstrate that summer and autumn have an increase in most of the LWE durations occurrence in the 2061–2080 period in all regions compared to the historical period. Using five days running mean wind speed, the return time for 6 m/s cut-in wind speed shows that OWT will be vulnerable to frequent extreme LWE in most areas, with most sites experiencing a return period of up to 20 years. According to the return year region median and the Risk Ratio (RR) calculations, it is suggested that the South region exhibits a diminished risk of experiencing more frequent instances of wind speeds surpassing the cut-in threshold, specifically when utilizing cut-in thresholds of 5 m/s and 6 m/s, during the period spanning 2021–2040, as compared to the historical period. Furthermore, when employing 6-, 7-, and 8-day running means, the analysis reveals that the return period for wind speeds of 4 m/s in the Western region remains consistently recommended throughout the 2021–2040 period. In contrast, utilizing a 6-day time window for assessing the return period of 4 m/s wind speeds indicates a notable escalation in risk across all regions during the 2061–2080 period

A comparative climate-resilient energy design: Wildfire Resilient Load Forecasting Model using multi-factor deep learning methods

Power grid damage and blackouts are increasing with climate change. Load forecasting methods that integrate climate resilience are therefore essential to facilitate timely and accurate network reconfiguration during periods of extreme stress. Our paper proposes a generalised Wildfire Resilient Load Forecasting Model (WRLFM) to predict electricity load based on operational data of a Distribution Network (DN) in Australia during wildfire seasons in 2015–2020. We demonstrate that load forecasting during wildfire seasons is more challenging than during non-wildfire seasons, motivating an imperative need to improve forecast performance during wildfire seasons. To develop the robust WRLFM, comprehensive comparative analyses were conducted to determine proper Machine Learning (ML) forecast structures and methods for incorporating multiple factors. Bi-directional Gated Recurrent Unit (Bi-GRU) and Vision Transformer (ViT) were selected as they performed the best among all 13 recently trending ML methods. Multi-factors were incorporated to contribute to forecast performance, including input sequence structures, calendar information, flexible correlation-based temperature conditions, and categorical Fire Weather Index (FWI). High-resolution categorical FWI was used to build a forecasting model with climate resilience for the first time, significantly enhancing the average stability of forecast performances by 42%. A sensitivity analysis compared data set patterns and model performances during wildfire and non-wildfire seasons. The improvement rate of load forecasting performance during wildfire seasons was more than two times greater than in non-wildfire seasons. This indicates the significance and effectiveness of applying the WRLFM to improve forecast accuracy under extreme weather risks. Overall, the WRLFM reduces the Mean Absolute Percentage Error (MAPE) of the forecast by 14.37% and 20.86% for Bi-GRU and ViT-based models, respectively, achieving an average forecast MAPE of around 3%

Generating samples of extreme winters to support climate adaptation

Recent extreme weather across the globe highlights the need to understand the potential for more extreme events in the present-day, and how such events may change with global warming. We present a methodology for more efficiently sampling extremes in future climate projections. As a proof-of-concept, we examine the UK’s most recent set of national Climate Projections (UKCP18). UKCP18 includes a 15-member perturbed parameter ensemble (PPE) of coupled global simulations, providing a range of climate projections incorporating uncertainty in both internal variability and forced response. However, this ensemble is too small to adequately sample extremes with very high return periods, which are of interest to policy-makers and adaptation planners. To better understand the statistics of these events, we use distributed computing to run three 1000-member initial-condition ensembles with the atmosphere-only HadAM4 model at 60km resolution on volunteers’ computers, taking boundary conditions from three distinct future extreme winters within the UKCP18 ensemble. We find that the magnitude of each winter extreme is captured within our ensembles, and that two of the three ensembles are conditioned towards producing extremes by the boundary conditions. Our ensembles contain several extremes that would only be expected to be sampled by a UKCP18 PPE of over 500 members, which would be prohibitively expensive with current supercomputing resource. The most extreme winters we simulate exceed those within UKCP18 by 0.85 K and 37% of the present-day average for UK winter means of daily maximum temperature and precipitation respectively. As such, our ensembles contain a rich set of multivariate, spatio-temporally and physically coherent samples of extreme winters with wide-ranging potential applications

A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas

This paper proposes a generalised and robust multi-factor Gated Recurrent Unit (GRU) based Deep Learning (DL) model to forecast electricity load in distribution networks during wildfire seasons. The flexible modelling methods consider data input structure, calendar effects and correlation-based leading temperature conditions. Compared to the regular use of instantaneous temperature, the Mean Absolute Percentage Error (MAPE) is decreased by 30.73% by using the proposed input feature selection and leading temperature relationships. Our model is generalised and applied to eight real distribution networks in Victoria, Australia, during the wildfire seasons of 2015-2020. We demonstrate that the GRU-based model consistently outperforms another DL model, Long Short-Term Memory (LSTM), at every step, giving average improvements in Mean Squared Error (MSE) and MAPE of 10.06% and 12.86%, respectively. The sensitivity to large-scale climate variability in training data sets, e.g. El Ni\~no or La Ni\~na years, is considered to understand the possible consequences for load forecasting performance stability, showing minimal impact. Other factors such as regional poverty rate and large-scale off-peak electricity use are potential factors to further improve forecast performance. The proposed method achieves an average forecast MAPE of around 3%, giving a potential annual energy saving of AU\$80.46 million for the state of Victoria

Frontal Structure of the Antarctic Circumpolar Current in the South Indian Ocean

Using recently published atlas data [Olbers et al., 1992] and the Fine Resolution Antarctic Model (FRAM) [Webb et al., 1991], an investigation has been conducted into the structure of the frontal jets centered around the region of the islands of Crozet (46°27'S, 52°0'E) and Kerguelen (48°15'S, 69°10'E) in the south Indian Ocean. Geostrophic current velocities and transports were calculated from the temperature and salinity fields available from the atlas and compared with results from FRAM and previous studies. We have identified the Agulhas Return Front (ARF) and the Subtropical Front (STF), as well as the following fronts of the Antarctic Circumpolar Current (ACC): the Subantarctic Front (SAP), the Polar Front (PF), and the Southern ACC Front (SACCF), from temperature and salinity characteristics and from geostrophic currents. This analysis of model and atlas data indicates that the jets associated with the ARF, STF, and SAF are topograpliically steered into a unique frontal system north of the islands, having some of the largest temperature and salinity gradients anywhere in the world ocean. The frontal jet associated with the ARF is detectable up to 75°E and has associated with it several northward branching jets. The PF bifurcates in the region of the Ob'Lena (Conrad) seamount; subsurface and surface expressions are identified, separated by as much as 8° of latitude immediately west of the Kerguelen Plateau. The surface expression, carrying the bulk of the transport (~65 Sv), is steered through the col in the Kerguelen Plateau at 56°S, 6° south of the latitude normally associated with the PF at this meridian. On crossing the plateau it rejoins the subsurface expression. In the south, passing eastward along the margin of the Antarctic continent and through the Princess Elizabeth Trough, a frontal jet is identified transporting up to 35 Sv, believed to be the SACCF [Orsi et al., 1995], placing the southern extent of the ACC in the region at 67°S. Copyright 1996 by the American Geophysical Union

A delta-aminolevulinic acid dehydratase (ALAD) polymorphism may modify the relationship of low-level lead exposure to uricemia and renal function: the normative aging study.

In this study we investigated whether a known delta-aminolevulinic acid dehydratase (ALAD) exon 4 polymorphism has a modifying effect on the association of blood or bone lead level with uricemia and indices of renal function among middle-aged and elderly men. We performed a cross-sectional study of subjects who participated between 1991 and 1995 in the Department of Veterans Affairs Normative Aging Study. Information on blood lead levels, bone lead levels (measured by K-shell X-ray fluorescence), serum uric acid, serum creatinine, estimated creatinine clearance, and ALAD polymorphism status was available in 709 subjects. Regression models were constructed to examine the relationships of serum uric acid, serum creatinine, and estimated creatinine clearance to blood or bone lead level, stratified by genotype. We also adjusted for age, body mass index, blood pressure, smoking, alcohol consumption, and ingestion of analgesic medications (n = 638). Of the 709 subjects, 7 (1%) and 107 (15%) were homozygous and heterozygous for the variant (ALAD-2) allele, respectively. The mean (range) serum uric acid and creatinine levels were 6.5 (2.9-10.6) and 1.2 (0.6-2.5) mg/dL. No significant differences were found in serum uric acid, serum creatinine, or estimated creatinine clearance by ALAD genotype. However, after adjusting for other potential confounders, we found a significant linear relationship between serum uric acid and patella bone lead (p = 0.040) among the ALAD 1-2/2-2 genotype individuals above a threshold patellar lead level of 15 micro g/g. In contrast, among the wild-type (ALAD 1-1) individuals, there was a suggestion of a significant linear relationship of serum uric acid with patella bone lead (p = 0.141), but only after a threshold of 101 micro g/g. There was evidence of a significant (p = 0.025) interaction of tibia lead with genotype (ALAD 1-1 vs. ALAD 1-2/2-2) regarding serum creatinine as an outcome, but in the same linear regression model tibia lead alone was not a significant predictor of serum creatinine. Conversely, for estimated creatinine clearance, patella lead, but not the interaction of patella lead with genotype, was a significantly independent predictor (p = 0.026). Our findings suggest that ALAD genotype may modify the effect of lead on the renal excretion of uric acid as well as overall renal function among middle-aged and elderly men who had community (nonoccupational) exposures to lead. Additional research is needed to ascertain whether this constitutes a true gene-environment interaction and, if so, its clinical impact