Improved Robustness In Long-term Pressure Data Analysis Using Wavelets and Deep Learning

2020 ◽  
Author(s):  
Dante Orta Alemán ◽  
Roland Horne
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Pressure Data

Coming Together Through Falling Apart

2019 ◽  
Vol 40 (2) ◽  
pp. 151-168
Author(s):  
Heather Churchill ◽  
Jeremy M. Ridenour
Keyword(s):  
Data Analysis ◽  
Qualitative Data ◽  
Structural Changes ◽  
Systematic Approach ◽  
Qualitative Approach ◽  
Psychological Assessments ◽  
Transference And Countertransference ◽  
Testing Data ◽  
Psychoanalytically Oriented Psychotherapy

Abstract. Assessing change during long-term psychotherapy can be a challenging and uncertain task. Psychological assessments can be a valuable tool and can offer a perspective from outside the therapy dyad, independent of the powerful and distorting influences of transference and countertransference. Subtle structural changes that may not yet have manifested behaviorally can also be assessed. However, it can be difficult to find a balance between a rigorous, systematic approach to data, while also allowing for the richness of the patient’s internal world to emerge. In this article, the authors discuss a primarily qualitative approach to the data and demonstrate the ways in which this kind of approach can deepen the understanding of the more subtle or complex changes a particular patient is undergoing while in treatment, as well as provide more detail about the nature of an individual’s internal world. The authors also outline several developmental frameworks that focus on the ways a patient constructs their reality and can guide the interpretation of qualitative data. The authors then analyze testing data from a patient in long-term psychoanalytically oriented psychotherapy in order to demonstrate an approach to data analysis and to show an example of how change can unfold over long-term treatments.

Becoming Rhizomatic: Researching Flowing in/between Striated and Smooth Space

2019 ◽  
Vol 13 (3) ◽  
pp. 355-376
Author(s):  
Ester A. Betrián Villas ◽  
Gloria Jové Monclus ◽  
Charly Ryan
Keyword(s):  
Data Analysis ◽  
Educational Change ◽  
Research Methodology ◽  
Deleuze And Guattari ◽  
Smooth Space

Exploring long-term educational change, we investigate our re/construction of research methodology as we moved from a positivist framework to working with ideas drawn from Deleuze and Guattari. We reveal our becoming rhizomatic in data analysis in the metamodelling of the richness flowing horizontally through our practices. We tell of our struggles to escape hierarchical thinking and relations researching between the smooth and striated. A space of interactions, conversations and writings created relations between polyphonic voices, leading us to an emergent methodology. Our struggle against hierarchies in data analysis yielded rich educational possibilities for becoming that Deleuzo-Guattarian thinking offers us.

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Evaluation of an Educational Outreach Campaign (IMPACT) on Pain Management Delivered to General Practices in Walsall

2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i48-i49
Author(s):  
S Visram ◽  
J Saini ◽  
R Mandvia
Keyword(s):  
Data Analysis ◽  
Pain Management ◽  
Cancer Pain ◽  
Educational Intervention ◽  
Opioid Analgesics ◽  
Educational Outreach ◽  
Opioid Prescribing ◽  
Prescribing Trends ◽  
The Impact

Abstract Introduction Opioid class drugs are a commonly prescribed form of analgesic widely used in the treatment of acute, cancer and chronic non-cancer pain. Up to 90% of individuals presenting to pain centres receive opioids, with doctors in the UK prescribing more and stronger opioids (1). Concern is increasing that patients with chronic pain are inappropriately being moved up the WHO ‘analgesic ladder’, originally developed for cancer pain, without considering alternatives to medications, (2). UK guidelines on chronic non-cancer pain management recommend weak opioids as a second-line treatment, when the first-line non-steroidal anti-inflammatory drugs / paracetamol) ineffective, and for short-term use only. A UK educational outreach programme by the name IMPACT (Improving Medicines and Polypharmacy Appropriateness Clinical Tool) was conducted on pain management. This research evaluated the IMPACT campaign, analysing the educational impact on the prescribing of morphine, tramadol and other high-cost opioids, in the Walsall CCG. Methods Standardised training material was delivered to 50 practices between December 2018 and June 2019 by IMPACT pharmacists. The training included a presentation on pain control, including dissemination of local and national guidelines, management of neuropathic, low back pain and sciatica as well as advice for prescribers on prescribing opioids in long-term pain, with the evidence-base. Prescribing trends in primary care were also covered in the training, and clinicians were provided with resources to use in their practice. Data analysis included reviewing prescribing data and evaluating the educational intervention using feedback from participants gathered via anonymous questionnaires administered at the end of the training. Prescribing data analysis was conducted by Keele University’s Medicines Management team via the ePACT 2 system covering October 2018 to September 2019 (two months before and three months after the intervention) were presented onto graphs to form comparisons in prescribing trends of the Midland CCG compared to England. Results Questionnaires completed at the end of sessions showed high levels of satisfaction, with feedback indicating that participants found the session well presented, successful at highlighting key messages, and effective in using evidence-based practice. 88% of participants agreed the IMPACT campaign increased their understanding of the management and assessment of pain, and prescribing of opioids and other resources available to prescribers. The majority (85%) wished to see this form of education being repeated regularly in the future for other therapeutic areas. Analysis of the prescribing data demonstrated that the total volume of opioid analgesics decreased by 1.7% post-intervention in the Midlands CCG in response to the pharmacist-led educational intervention. As supported by literature, the use of educational strategies, including material dissemination and reminders as well as group educational outreach was effective in engaging clinicians, as demonstrated by the reduction in opioid prescribing and high GP satisfaction in this campaign. Conclusion The IMPACT campaign was effective at disseminating pain-specific guidelines for opioid prescribing to clinicians, leading to a decrease in overall prescribing of opioid analgesics. Educational outreach as an approach is practical and a valuable means to improve prescribing by continuing medical education. References 1. Els, C., Jackson, T., Kunyk, D., Lappi, V., Sonnenberg, B., Hagtvedt, R., Sharma, S., Kolahdooz, F. and Straube, S. (2017). Adverse events associated with medium- and long-term use of opioids for chronic non-cancer pain: an overview of Cochrane Reviews. Cochrane Database of Systematic Reviews. This provided the statistic of percentage receiving opioids that present to pain centres. 2. Heit, H. (2010). Tackling the Difficult Problem of Prescription Opioid Misuse. Annals of Internal Medicine, 152(11), p.747. Issues with prescriptions and inappropriate moving up the WHO ladder.

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