Stored Grain Volume MeasurementUsing a Low Density Point Cloud

Citation: Turner, A. P., Jackson, J. J., Koeninger, N. K., McNeill, S. G., Montross, M. D., Casada, M. E., . . . Thompson, S. A. (2017). STORED GRAIN VOLUME MEASUREMENT USING A LOW DENSITY POINT CLOUD. Applied Engineering in Agriculture, 33(1), 105-112. doi:10.13031/aea.11870This technical note presents the development of a new apparatus and data processing method to accurately estimate the volume of stored grain in a bin. Specifically, it was developed to account for the variability in surface topography that can occur in large diameter bins when partially unloaded. This was accomplished using a laser distance meter to create a low density point cloud, from which a surface was interpolated using ArcMap geoprocessing tools. The manually controlled and portable system was designed to hold the laser distance meter and provided a common reference point. The data from the laser distance meter was transmitted to a tablet PC via Bluetooth. Measurement of an empty hopper bottom bin (4.6 m in diameter and 6.5 m tall) demonstrated that the system was able to measure a known volume within 0.02%, and repeated measures of an empty flat bottom bin (1.8 m in diameter, and 5.7 m tall) were within 0.29% of the known volume. Two applications are presented which highlight the system's ability to capture complex surfaces, as well as limitations that result from fill scenarios where the field of view was limited

Stored Grain Volume Measurement Using a Low Density Point Cloud

This technical note presents the development of a new apparatus and data processing method to accurately estimate the volume of stored grain in a bin. Specifically, it was developed to account for the variability in surface topography that can occur in large diameter bins when partially unloaded. This was accomplished using a laser distance meter to create a low density point cloud, from which a surface was interpolated using ArcMap geoprocessing tools. The manually controlled and portable system was designed to hold the laser distance meter and provided a common reference point. The data from the laser distance meter was transmitted to a tablet PC via Bluetooth. Measurement of an empty hopper bottom bin (4.6 m in diameter and 6.5 m tall) demonstrated that the system was able to measure a known volume within 0.02%, and repeated measures of an empty flat bottom bin (1.8 m in diameter, and 5.7 m tall) were within 0.29% of the known volume. Two applications are presented which highlight the system‘s ability to capture complex surfaces, as well as limitations that result from fill scenarios where the field of view was limited

Enhancing legacy in palliative care: study protocol for a randomized controlled trial of Dignity Therapy focused on positive outcomes

BACKGROUND: Dignity Therapy is a brief psychotherapy that can enhance a sense of legacy while addressing the emotional and existential needs of patients receiving hospice or palliative care. In Dignity Therapy, patients create a formalized “legacy” document that records their most cherished memories, their lessons learned in life, as well as their hopes and dreams for loved ones in the future. To date, this treatment has been studied for its impact on mitigating distress within hospice and palliative care populations and has provided mixed results. This study will instead focus on whether Dignity Therapy enhances positive outcomes in this population. METHODS/DESIGN: In this study, 90 patients with cancer receiving hospice or palliative care will complete a mixed-methods randomized controlled trial of Dignity Therapy (n = 45) versus Supportive Attention (n = 45). The patients will be enrolled in the study for 3 weeks, receiving a total of six study visits. The primary outcomes examine whether the treatment will quantitatively increase levels of positive affect and a sense of life closure. Secondary outcomes focus on gratitude, hope, life satisfaction, meaning in life, resilience, and self-efficacy. Using a fixed, embedded dataset design, this study will additionally use qualitative interviews to explore patients’ perceptions regarding the use of positive outcome measures and whether these outcomes are appropriately matched to their experiences in therapy. DISCUSSION: Dignity Therapy has shown mixed results when evaluating its impact on distress, although no other study to date has solely focused on the potential positive aspects of this treatment. This study is novel in its use of mixed methods assessments to focus on positive outcomes, and will provide valuable information about patients’ direct experiences in this area. TRIAL REGISTRATION: ISRCTN9138919

High strain-rate material model validation for laser peening simulation

Finite element modeling can be a powerful tool for predicting residual stresses induced by laser peening; however the sign and magnitude of the stress predictions depend strongly on how the material model captures the high strain rate response. Although a Johnson-Cook formulation is often employed, its suitability for modeling phenomena at very high strain rates has not been rigorously evaluated. In this paper, we address the effectiveness of the Johnson-Cook model, with parameters developed from lower strain rate material data (∼10^3 s^–1), to capture the higher strain rate response (∼10^5–10^6 s^–1) encountered during the laser peening process. Published Johnson-Cook parameters extracted from split Hopkinson bar testing were used to predict the shock response of aluminum samples during high-impact flyer plate tests. Additional quasi-static and split Hopkinson bar tests were also conducted to study the model response in the lower strain rate regime. The overall objective of the research was to ascertain whether a material model based on conventional test data (quasi-static compression testing and split Hopkinson bar measurements) can credibly be used in FE simulations to predict laser peen-induced stresses

Establishing an implementation network: lessons learned from community-based participatory research

<p>Abstract</p> <p>Background</p> <p>Implementation of evidence-based mental health assessment and intervention in community public health practice is a high priority for multiple stakeholders. Academic-community partnerships can assist in the implementation of efficacious treatments in community settings; yet, little is known about the processes by which these collaborations are developed. In this paper, we discuss our application of community-based participatory research (CBPR) approach to implementation, and we present six lessons we have learned from the establishment of an academic-community partnership.</p> <p>Methods</p> <p>With older adults with psychosis as a focus, we have developed a partnership between a university research center and a public mental health service system based on CBPR. The long-term goal of the partnership is to collaboratively establish an evidence-based implementation network that is sustainable within the public mental healthcare system.</p> <p>Results</p> <p>In building a sustainable partnership, we found that the following lessons were instrumental: changing attitudes; sharing staff; expecting obstacles and formalizing solutions; monitoring and evaluating; adapting and adjusting; and taking advantage of emerging opportunities. Some of these lessons were previously known principles that were modified as the result of the CBPR process, while some lessons derived directly from the interactive process of forming the partnership.</p> <p>Conclusion</p> <p>The process of forming of academic-public partnerships is challenging and time consuming, yet crucial for the development and implementation of state-of-the-art approaches to assessment and interventions to improve the functioning and quality of life for persons with serious mental illnesses. These partnerships provide necessary organizational support to facilitate the implementation of clinical research findings in community practice benefiting consumers, researchers, and providers.</p

Glucose Recovery from Different Corn Stover Fractions Using Dilute Acid and Alkaline Pretreatment Techniques

Background: Limited availability of corn stover due to the competing uses (organic manure, animal feed, bio-materials, and bioenergy) presents a major concern for its future in the bio-economy. Furthermore, biomass research has exhibited different results due to the differences in the supply of enzymes and dissimilar analytical methods. The effect of the two leading pretreatment techniques (dilute acid and alkaline) on glucose yield from three corn stover fractions (cob, stalk, and leaf) sourced from a single harvest in Uganda were studied at temperatures 100, 120, 140, and 160 °C over reaction times of 5, 10, 30, and 60 min. Results: From this study, the highest glucose concentrations obtained from the dilute acid (DA) pretreated cobs, stalks, and leaves were 18.4 g/L (66.8% glucose yield), 16.2 g/L (64.1% glucose yield), and 11.0 g/L (49.5% glucose yield), respectively. The optimal pretreatment settings needed to obtain these yields from the DA pretreated samples were at a temperature of 160 °C over an incubation time of 30 min. The highest glucose concentrations obtained from the alkaline (AL) pretreated cobs, stalks, and leaves were 24.7 g/L (81.73% glucose yield), 21.3 g/L (81.23% glucose yield), and 15.0 g/L (51.92% glucose yield), respectively. To be able to achieve these yields, the optimal pretreatment settings for the cobs and stalks were 140 °C and for a retention time of 30 min, while the leaves require optimal conditions of 140 °C and for a retention time of 60 min. Conclusions: The study recommends that the leaves could be left on the field during harvesting since the recovery of glucose from the pretreated cobs and stalks is higher

High and low levels of an NTRK2-driven genetic profile affect motor- and cognition-associated frontal gray matter in prodromal Huntington’s disease

This study assessed how BDNF (brain-derived neurotrophic factor) and other genes involved in its signaling influence brain structure and clinical functioning in pre-diagnosis Huntington’s disease (HD). Parallel independent component analysis (pICA), a multivariate method for identifying correlated patterns in multimodal datasets, was applied to gray matter concentration (GMC) and genomic data from a sizeable PREDICT-HD prodromal cohort (N = 715). pICA identified a genetic component highlighting NTRK2, which encodes BDNF’s TrkB receptor, that correlated with a GMC component including supplementary motor, precentral/premotor cortex, and other frontal areas (p < 0.001); this association appeared to be driven by participants with high or low levels of the genetic profile. The frontal GMC profile correlated with cognitive and motor variables (Trail Making Test A (p = 0.03); Stroop Color (p = 0.017); Stroop Interference (p = 0.04); Symbol Digit Modalities Test (p = 0.031); Total Motor Score (p = 0.01)). A top-weighted NTRK2 variant (rs2277193) was protectively associated with Trail Making Test B (p = 0.007); greater minor allele numbers were linked to a better performance. These results support the idea of a protective role of NTRK2 in prodromal HD, particularly in individuals with certain genotypes, and suggest that this gene may influence the preservation of frontal gray matter that is important for clinical functioning.This project was supported by 1U01NS082074 (V.C. and J.T., co-principal investigators) from the National Institutes of Health, National Institute of Neurological Disorders and Stroke. The PREDICT-HD study was supported by NIH/NINDS grant 5R01NS040068 awarded to J.P.; CHDI Foundation, Inc., A3917 and 6266 awarded to J.P.; Cognitive and Functional Brain Changes in Preclinical Huntington’s Disease (HD) 5R01NS054893 awarded to J.P.; 4D Shape Analysis for Modeling Spatiotemporal Change Trajectories in Huntington’s 1U01NS082086; Functional Connectivity in Premanifest Huntington’s Disease 1U01NS082083; and Basal Ganglia Shape Analysis and Circuitry in Huntington’s Disease 1U01NS082085 awarded to Christopher A. Ross

Patterns and associates of cognitive function, psychosocial wellbeing and health in the Lothian Birth Cohort 1936

BACKGROUND: Cognitive function, psychosocial wellbeing and health are important domains of function. Consistencies and inconsistencies in patterns of wellbeing across these domains may be informative about wellbeing in old age and the ways it is manifested amongst individuals. In this study we investigated whether there were groups of individuals with different profiles of scores across these domains. We also aimed to identify characteristics of any evident groups by comparing them on variables that were not used in identifying the groups. METHODS: The sample was the Lothian Birth Cohort 1936, which included 1091 participants born in 1936. They are a community-dwelling, narrow-age-range sample of 70-year-olds. Most had taken part in the Scottish Mental Survey 1947 at an average age of 11, making available a measure of childhood intelligence. We used latent class analysis (LCA) to explore possible profiles using 9 variables indicating cognitive functioning, psychosocial wellbeing and health status. Demographic, personality, and lifestyle variables – none of which were used in the LCA – were used to characterize the resulting profile groups. RESULTS: We accepted a 3-group solution, which we labeled High Wellbeing (65.3%), Low Cognition (20.3%), and Low Bio-Psychosocial (14.5%). Notably, the High Wellbeing group had significantly higher childhood IQ, lower Neuroticism scores, and a lower percentage of current smokers than the other 2 groups. CONCLUSION: The majority of individuals were functioning generally well; however, there was evidence of the presence of groups with different profiles, which may be explained in part in terms of cognitive ability differences. Results suggested that higher life-long intelligence, personality traits associated with less mental distress, and basic health practices such as avoiding smoking are important associates of wellbeing in old age

Suicide risk in schizophrenia: learning from the past to change the future

Suicide is a major cause of death among patients with schizophrenia. Research indicates that at least 5–13% of schizophrenic patients die by suicide, and it is likely that the higher end of range is the most accurate estimate. There is almost total agreement that the schizophrenic patient who is more likely to commit suicide is young, male, white and never married, with good premorbid function, post-psychotic depression and a history of substance abuse and suicide attempts. Hopelessness, social isolation, hospitalization, deteriorating health after a high level of premorbid functioning, recent loss or rejection, limited external support, and family stress or instability are risk factors for suicide in patients with schizophrenia. Suicidal schizophrenics usually fear further mental deterioration, and they experience either excessive treatment dependence or loss of faith in treatment. Awareness of illness has been reported as a major issue among suicidal schizophrenic patients, yet some researchers argue that insight into the illness does not increase suicide risk. Protective factors play also an important role in assessing suicide risk and should also be carefully evaluated. The neurobiological perspective offers a new approach for understanding self-destructive behavior among patients with schizophrenia and may improve the accuracy of screening schizophrenics for suicide. Although, there is general consensus on the risk factors, accurate knowledge as well as early recognition of patients at risk is still lacking in everyday clinical practice. Better knowledge may help clinicians and caretakers to implement preventive measures. This review paper is the results of a joint effort between researchers in the field of suicide in schizophrenia. Each expert provided a brief essay on one specific aspect of the problem. This is the first attempt to present a consensus report as well as the development of a set of guidelines for reducing suicide risk among schizophenia patients