Prediction models of landscape preferences at the forest stand level

2001 ◽  
Vol 56 (1-2) ◽  
pp. 11-20 ◽  
Author(s):  
Harri Silvennoinen ◽  
Juha Alho ◽  
Osmo Kolehmainen ◽  
Timo Pukkala
Keyword(s):  
Prediction Models ◽  
Forest Stand

scholarly journals INFLUENCE OF FOREST STAND DENSITY ON PINUS SYLVESTRIS ROUNDWOOD QUALITY IN EVEN-AGED MIXED SCOTS PINE STANDS IN MYRTILLOSA MEL. MESOTROPHIC SITE TYPE

2021 ◽  
Vol 3 ◽  
pp. 208-215
Author(s):  
Ziedonis Miklašēvičs
Keyword(s):  
Pinus Sylvestris ◽  
Tree Species ◽  
Prediction Models ◽  
Wood Quality ◽  
Research Work ◽  
Stand Density ◽  
Forest Stand ◽  
Raw Material ◽  
Quality Characteristic ◽  
Round Wood

Among different development directions for better forest utilization, the purposefully detailed assessment of wood quality as raw material, faciliating improved wood utilization in manufacturing of traditional roundwood products as well as brand new products, proves to be very perspective. Roundwood quality features substantially differ depending on forest stand geographic location, growing site conditions, tending of forest stands and other circumstances [2], [3], [11]. Therefore, the economical estimation of more important tree species in Latvia should not be based only on scientific conclusions made in other countries, so particular research is needed for this purpose. Pine (Pinus sylvestris) is the most widespread tree species in Latvia, therefore a research work provided the characteristic of round wood obtained from pine tree stem is an actual point from several aspects, as it is necessary;to create early prediction models of roundwood quality; to plan harvesting purposes;to create the data basis of economically more relevant domestic tree species;to provide the further development of roundwood quality assessment methods;to estimate the competitiveness of Latvia pine timber in the world marketThe main goal of study is to work out the quality characteristic of pine roundwood manufactured in final felling sites depending on timber formation damages- bark abrasion caused side drought.  

scholarly journals Cost evaluation of three ground-logging methods in a mixed broadleaved mountainous forest

2018 ◽  
Vol 27 (2) ◽  
pp. e013
Author(s):  
Aysan Badraghi ◽  
Jörn Erler ◽  
Seyed A. O. Hosseini ◽  
Robert Lang
Keyword(s):  
Prediction Models ◽  
Forest Stand ◽  
Short Length ◽  
Cost Evaluation ◽  
Hyrcanian Forests ◽  
Time Prediction ◽  
Tree Length ◽  
Economical Alternative ◽  
Area Of Study

Aim of study: To compare cost and productivity of three ground-based logging methods by skidder: 1, tree length method (TLM), 2, long length method (LLM) and 3, short length method (SLM).Area of study: A mixed broadleaved mountainous forest stand in the Hyrcanian forests in northern Iran.Material and methods: To develop time prediction models, all measurements of time were replaced by their decadic logarithms, and on the basis of the developed models, we simulated cost of 11 skidding turns depending on the diameter of the log (DL), skidding distance (SD), and the winching distance (WD) for TLM, LLM, and SLM.Main results: Our results demonstrated that on average the net costs per extraction of one cubic meter wood (m3) were 3.06, 5.69, and 6.81 €/m3 in TLM, LLM, and SLM, respectively, and the most economical alternative depending on DL, SD and WD was a TLM. Furthermore, the results of simulated models suggest that as long as the diameter of the felled trees is less than 40 cm, the cut-to-length system is not an economical alternative. The cut-to-length method can be applied for trees with larger diameter (more than 40 cm), and in short skidding distance SLM is preferable to LLM but in cases of long skidding distance, LLM is more economical than SLM.Research highlights: DLand SD were the main causes which influenced the productivity and cost of ground-based logging methods.

4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

2013 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Javaria Manzoor Shaikh ◽  
JaeSeung Park
Keyword(s):  
Pattern Recognition ◽  
Prediction Models ◽  
Uv Light ◽  
Healing Process ◽  
Space Syntax ◽  
Depth Map ◽  
Routine Tasks ◽  
Hyper Plane ◽  
First Time ◽  
Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

The Role of Revascularisation in Patients Undergoing Non-cardiac Surgery

2010 ◽  
Vol 5 (1) ◽  
pp. 104
Author(s):  
Daniel S Menees ◽  
Eric R Bates ◽  
◽  
Keyword(s):  
Cardiac Surgery ◽  
Prediction Models ◽  
Cardiovascular Complications ◽  
Weight Of Evidence ◽  
Beta Blockade ◽  
Cardiac Morbidity ◽  
Increased Risk ◽  
Reduction Strategies ◽  
Artery Disease ◽  
Risk Reduction Strategies

Coronary artery disease (CAD) affects millions of US citizens. As the population ages, an increasing number of people with CAD are undergoing non-cardiac surgery and face significant peri-operative cardiac morbidity and mortality. Risk-prediction models can be used to help identify those patients at increased risk of peri-operative cardiovascular complications. Risk-reduction strategies utilising pharmacotherapy with beta blockade and statins have shown the most promise. Importantly, the benefit of prophylactic coronary revascularisation has not been demonstrated. The weight of evidence suggests reserving either percutaneous or surgical revascularisation in the pre-operative setting for those patients who would otherwise meet independent revascularisation criteria.

Enterprise Performance Analysis of the Selected Service Sector by Applying Modern Methods with an Emphasis on the Creation and Application of the Modified Creditworthy Model (MCWM)

2018 ◽  
Vol 9 (17) ◽  
Author(s):  
Erika Onuferová ◽  
Veronika Čabinová
Keyword(s):  
Service Sector ◽  
Prediction Models ◽  
Economic Value ◽  
Value Added ◽  
Financial Indicators ◽  
Tourism Sector ◽  
Future Success ◽  
Financial Growth ◽  
Net Assets ◽  
Third Dimension

The aim of presented paper was to create and subsequently apply the Modified 3D Creditworthy Model (MCWM) of performance reflecting sectoral characteristics and financial specificities of the selected sample of Slovak tour operators over the years 2013 – 2017. The intention of this research study was to implement the key financial indicators and appropriate prediction models into both dimensions of the traditional 2D Creditworthy Model of performance and to supplement its third dimension applying the selected modern assessment methods – the Economic Value Added and the Return On Net Assets as we consider them to be one of the most important indicators of future success and company's financial growth. This modification will help to better identify the current financial position of tour operators and more accurately identify causes that hinder the development of financial performance of the selected sample of enterprises. However, after adjusting the upper and lower quartile averages of a particular industry, this methodology is applicable in the wider context of enterprises, not only those operating in the tourism sector.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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