Search results for: Penalized Quasi Likelihood

Authors: Alexandria Carey, Angela Starkweather, Ann Horgas, Hwayoung Cho, Jason Beneciuk

Abstract:

Background/Significance: Over 3.4 million acute axial low back pain (aLBP) cases are treated annually in the United States (US) emergency departments (ED). ED patients with aLBP receive varying verbal and written discharge routine care (RC), leading to ineffective patient self-management. Ineffective self-management increase chronic low back pain (cLPB) transition risks, a chief cause of worldwide disability, with associated costs >$60 million annually. This research addresses this significant problem by evaluating an ED digital pain self-management intervention (EDPSI) focused on improving self-management through improved knowledge retainment, skills, and self-efficacy (confidence) (KSC) thus reducing aLBP to cLBP transition in ED patients discharged with aLBP. The research has significant potential to increase self-efficacy, one of the most potent mechanisms of behavior change and improve health outcomes. Focusing on accessibility and usability, the intervention may reduce discharge disparities in aLBP self-management, especially with low health literacy. Study Questions: This research will answer the following questions: 1) Will an EDPSI focused on improving KSC progress patient self-management behaviors and health status?; 2) Is the EDPSI sustainable to improve pain severity, interference, and pain recurrence?; 3) Will an EDPSI reduce aLBP to cLBP transition in patients discharged with aLBP? Aims: The pilot randomized-controlled trial (RCT) study’s objectives assess the effects of a 12-week digital self-management discharge tool in patients with aLBP. We aim to 1) Primarily assess the feasibility [recruitment, enrollment, and retention], and [intervention] acceptability, and sustainability of EDPSI on participant’s pain self-management; 2) Determine the effectiveness and sustainability of EDPSI on pain severity/interference among participants. 3) Explore patient preferences, health literacy, and changes among participants experiencing the transition to cLBP. We anticipate that EDPSI intervention will increase likelihood of achieving self-management milestones and significantly improve pain-related symptoms in aLBP. Methods: The study uses a two-group pilot RCT to enroll 30 individuals who have been seen in the ED with aLBP. Participants are randomized into RC (n=15) or RC + EDPSI (n=15) and receive follow-up surveys for 12-weeks post-intervention. EDPSI innovative content focuses on 1) highlighting discharge education; 2) provides self-management treatment options; 3) actor demonstration of ergonomics, range of motion movements, safety, and sleep; 4) complementary alternative medicine (CAM) options including acupuncture, yoga, and Pilates; 5) combination therapies including thermal application, spinal manipulation, and PT treatments. The intervention group receives Booster sessions via Zoom to assess and reinforce their knowledge retention of techniques and provide return demonstration reinforcing ergonomics, in weeks two and eight. Outcome Measures: All participants are followed for 12-weeks, assessing pain severity/ interference using the Brief Pain Inventory short-form (BPI-sf) survey, self-management (measuring KSC) using the short 13-item Patient Activation Measure (PAM), and self-efficacy using the Pain Self-Efficacy Questionnaire (PSEQ) weeks 1, 6, and 12. Feasibility is measured by recruitment, enrollment, and retention percentages. Acceptability and education satisfaction are measured using the Education-Preference and Satisfaction Questionnaire (EPSQ) post-intervention. Self-management sustainment is measured including PSEQ, PAM, and patient satisfaction and healthcare utilization (PSHU) requesting patient overall satisfaction, additional healthcare utilization, and pain management related to continued back pain or complications post-injury.

Keywords: digital, pain self-management, education, tool

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Authors: Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung

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Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.

Keywords: digital surface model (DSM), feature reduction, hyperspectral, photogrammetric point cloud, species mapping, unmanned aerial vehicle (UAV)

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Authors: Anjula Negi, Sanjay Gupta

Abstract:

Paradigms studied inform inadequacies of financial resources, be it to finance metro rails for construction or to meet operational revenues or to derive profits in the long term. Funding sustainability is far and wide for much-needed public transport modes, like urban rail or metro rails, to be successfully operated. India embarks upon a sustainable transport journey and has proposed metro rail systems countrywide. As an emerging economic leader, its fiscal constraints are paramount, and the land value capture (LVC) mechanism provides necessary support and innovation toward development. India’s metro rail policy promotes multiple methods of financing, including private-sector investments and public-private-partnership. The critical question that remains to be addressed is what factors can make such mechanisms work. Globally, urban rail is a revolution noted by many researchers as future mobility. Researchers in this study deep dive by way of literature review and empirical assessments into factors that can lead to the adoption of LVC mechanisms. It is understood that the adoption of LVC methods is in the nascent stages in India. Research posits numerous challenges being faced by metro rail agencies in raising funding and for incremental value capture. A few issues pertaining to land-based financing, inter alia: are long-term financing, inter-institutional coordination, economic/ market suitability, dedicated metro funds, land ownership issues, piecemeal approach to real estate development, property development legal frameworks, etc. The question under probe is what are the parameters that can lead to success in the adoption of land value capture (LVC) as a financing mechanism. This research provides insights into key parameters crucial to the adoption of LVC in the context of Indian metro rails. Researchers have studied current forms of LVC mechanisms at various metro rails of the country. This study is significant as little research is available on the adoption of LVC, which is applicable to the Indian context. Transit agencies, State Government, Urban Local Bodies, Policy makers and think tanks, Academia, Developers, Funders, Researchers and Multi-lateral agencies may benefit from this research to take ahead LVC mechanisms in practice. The study deems it imperative to explore and understand key parameters that impact the adoption of LVC. Extensive literature review and ratification by experts working in the metro rails arena were undertaken to arrive at parameters for the study. Stakeholder consultations in the exploratory factor analysis (EFA) process were undertaken for principal component extraction. 43 seasoned and specialized experts participated in a semi-structured questionnaire to scale the maximum likelihood on each parameter, represented by various types of stakeholders. Empirical data was collected on chosen eighteen parameters, and significant correlation was extracted for output descriptives and inferential statistics. Study findings reveal these principal components as institutional governance framework, spatial planning features, legal frameworks, funding sustainability features and fiscal policy measures. In particular, funding sustainability features highlight sub-variables of beneficiaries to pay and use of multiple revenue options towards success in LVC adoption. Researchers recommend incorporation of these variables during early stage in design and project structuring for success in adoption of LVC. In turn leading to improvements in revenue sustainability of a public transport asset and help in undertaking informed transport policy decisions.

Keywords: Exploratory factor analysis, land value capture mechanism, financing metro rails, revenue sustainability, transport policy

Procedia PDF Downloads 66