scholarly journals Productivity and costs of the mechanised cut-to-length wood harvesting system in clear-felling operations

2008 ◽  
Vol 53 (No. 10) ◽  
pp. 476-482 ◽  
Author(s):  
R. Jiroušek ◽  
R. Klvač ◽  
A. Skoupý
Keyword(s):  
Cost Function ◽  
Labour Productivity ◽  
Regression Function ◽  
Site Conditions ◽  
Regression Equations ◽  
Technical Parameters ◽  
Wood Harvesting ◽  
Two Factors ◽  
The Cost

A study of labour productivity was conducted in fully mechanised harvesting technologies. The study revealed that the productivity of harvesters was particularly affected by the average tree volume of the felled trees, and the productivity of forwarders was affected mainly by two factors – haulage distance and machine payload. Dependences of other factors such as natural and site conditions, technical parameters and skills of operators could not be demonstrated. Regression equations of dependences were created for all these three significant criteria and costs per cubic meter of processed timber were calculated for them according to the cost function. A regression function was then developed for the forwarder that takes into account both significant criteria influencing the forwarder productivity.

The Technology and Knowledge Spıllover Effects of FDI on Labour Productıvıty—Evıdence from Medıum-Hıgh Industry in Malaysıa

2020 ◽  
Vol 11 (1) ◽  
pp. 110-110
Author(s):  
Norhanishah Mohamad Yunus ◽  
Noraida Abdul Wahob
Keyword(s):  
Developing Country ◽  
Multinational Corporations ◽  
Knowledge Spillovers ◽  
Labour Productivity ◽  
Spillover Effects ◽  
International Technology Transfer ◽  
Technology Acquisition ◽  
Productivity Function ◽  
Short Run ◽  
The Cost

A plethora of studies have revealed the importance of new knowledge transfer from foreign multinational corporations (MNCs) in encouraging higher labour productivity and sustainable competitive advantages. However, less attention is given to low labour productivity issue despite the presence of FDI, especially in the developing country context. Most of the studies only heavily emphasised on 'technology' effects rather than 'knowledge' effects on the host country as a result of the presence of foreign technology. As Malaysia is one of the major FDI recipients in Southeast Asia, the specific spillover effects of each FDI investor country in Malaysia, need to be studied. With an abundance of MNCs, international technology transfer is considered as an imported mode for technology acquisition in a developing country like Malaysia. However, the benefits of FDI spillovers on labour productivity function in Malaysia remain ambiguous, even when classified according to specific investor countries. Globalisation and liberalisation have seen trade and investment activities booming, thus increasing multilateral relations between Malaysia and other countries regardless of their level of development. Thus, this study may help the Malaysian government to justify the cost that should be invested to attract more FDI inflows towards the manufacturing industries in the short run. Keywords: spillover effects, Foreign Direct Investment, labour productivity, technology spillovers, knowledge spillovers

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

scholarly journals THU0548 THE ECONOMIC BURDEN OF ANKYLOSING SPONDYLITIS IN SPAIN. RESULTS OF THE SPANISH ATLAS 2017

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 514.2-514
Author(s):  
M. Merino ◽  
O. Braçe ◽  
A. González ◽  
Á. Hidalgo-Vega ◽  
M. Garrido-Cumbrera ◽  
...  
Keyword(s):  
Health Care ◽  
Ankylosing Spondylitis ◽  
Health Care Costs ◽  
Health Care Cost ◽  
Labour Productivity ◽  
General Health Questionnaire ◽  
Indirect Costs ◽  
Productivity Losses ◽  
The Cost ◽  
Care Costs

Background:Ankylosing Spondylitis (AS) is a disease associated with a high number of comorbidities, chronic pain, functional disability, and resource consumption.Objectives:This study aimed to estimate the burden of disease for patients diagnosed with AS in Spain.Methods:Data from 578 unselected patients with AS were collected in 2016 for the Spanish Atlas of Axial Spondyloarthritis via an online survey. The estimated costs were: Direct Health Care Costs (borne by the National Health System, NHS) and Direct Non-Health Care Costs (borne by patients) were estimated with the bottom-up method, multiplying the resource consumption by the unit price of each resource. Indirect Costs (labour productivity losses) were estimated using the human capital method. Costs were compared between levels of disease activity using the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score (<4 or low inflammation versus ≥4 or high inflammation) and risk of mental distress using the 12-item General Health Questionnaire (GHQ-12) score (<3 or low risk versus ≥3 or high risk).Results:The average annual cost per patient with AS in 2015 amounted to €11,462.3 (± 13,745.5) per patient. Direct Health Care Cost meant an annual average of €6,999.8 (± 9,216.8) per patient, to which an annual average of €611.3 (± 1,276.5) per patient associated with Direct Non-Health Care Cost borne by patients must be added. Pharmacological treatment accounted for the largest percentage of the costs borne by the NHS (64.6%), while for patients most of the cost was attributed to rehabilitative therapies and/or physical activity (91%). The average annual Indirect Costs derived from labour productivity losses were €3,851.2 (± 8,484.0) per patient, mainly associated to absenteeism. All categories showed statistically significant differences (p<0.05) between BASDAI groups (<4 vs ≥4) except for the Direct Non-Healthcare Cost, showing a progressive rise in cost from low to high inflammation. Regarding the 12-item General Health Questionnaire (GHQ-12), all categories showed statistically significant differences between GHQ-12 (<3 vs ≥3), with higher costs associated with higher risk of poor mental health (Table 1).Table 1.Average annual costs per patient according to BASDAI and GHQ-12 groups (in Euros, 2015)NDirect Health CostsDirect Non-Health CostsIndirect CostsTotal CostBASDAI<4917,592.0*557.32,426.5*10,575.8*≥43769,706.9*768.05,104.8*15,579.7*Psychological distress (GHQ-12)<31468,146.8*493.6*3,927.2*12,567.6*≥32609,772.9*807.2*4,512.3*15,092.5*Total5786,999.8611.33,851.211,462.3* p <0.05Conclusion:Direct Health Care Costs, and those attributed to pharmacological treatment in particular, accounted for the largest component of the cost associated with AS. However, a significant proportion of the overall costs can be further attributed to labour productivity losses.Acknowledgments:Funded by Novartis Farmacéutica S.A.Disclosure of Interests:María Merino: None declared, Olta Braçe: None declared, Almudena González: None declared, Álvaro Hidalgo-Vega: None declared, Marco Garrido-Cumbrera: None declared, Jordi Gratacos-Masmitja Grant/research support from: a grant from Pfizzer to study implementation of multidisciplinary units to manage PSA in SPAIN, Consultant of: Pfizzer, MSD, ABBVIE, Janssen, Amgen, BMS, Novartis, Lilly, Speakers bureau: Pfizzer, MSD, ABBVIE, Janssen, Amgen, BMS, Novartis, Lilly

Sorting permutations by fragmentation-weighted operations

2020 ◽  
Vol 18 (02) ◽  
pp. 2050006 ◽  
Author(s):  
Alexsandro Oliveira Alexandrino ◽  
Carla Negri Lintzmayer ◽  
Zanoni Dias
Keyword(s):  
Approximation Algorithms ◽  
Computational Biology ◽  
Cost Function ◽  
Traditional Approach ◽  
Upper Bounds ◽  
Evolutionary Distance ◽  
Lower And Upper Bounds ◽  
Approximation Factor ◽  
New Type ◽  
The Cost

One of the main problems in Computational Biology is to find the evolutionary distance among species. In most approaches, such distance only involves rearrangements, which are mutations that alter large pieces of the species’ genome. When we represent genomes as permutations, the problem of transforming one genome into another is equivalent to the problem of Sorting Permutations by Rearrangement Operations. The traditional approach is to consider that any rearrangement has the same probability to happen, and so, the goal is to find a minimum sequence of operations which sorts the permutation. However, studies have shown that some rearrangements are more likely to happen than others, and so a weighted approach is more realistic. In a weighted approach, the goal is to find a sequence which sorts the permutations, such that the cost of that sequence is minimum. This work introduces a new type of cost function, which is related to the amount of fragmentation caused by a rearrangement. We present some results about the lower and upper bounds for the fragmentation-weighted problems and the relation between the unweighted and the fragmentation-weighted approach. Our main results are 2-approximation algorithms for five versions of this problem involving reversals and transpositions. We also give bounds for the diameters concerning these problems and provide an improved approximation factor for simple permutations considering transpositions.

Maximum Likelihood Ensemble Filter: Theoretical Aspects

2005 ◽  
Vol 133 (6) ◽  
pp. 1710-1726 ◽  
Author(s):  
Milija Zupanski
Keyword(s):  
Maximum Likelihood ◽  
Data Assimilation ◽  
Cost Function ◽  
Ensemble Data Assimilation ◽  
Error Covariance ◽  
Ensemble Data ◽  
Analysis Error ◽  
Nonlinear Observation ◽  
The Cost ◽  
Maximum Likelihood Ensemble Filter

Abstract A new ensemble-based data assimilation method, named the maximum likelihood ensemble filter (MLEF), is presented. The analysis solution maximizes the likelihood of the posterior probability distribution, obtained by minimization of a cost function that depends on a general nonlinear observation operator. The MLEF belongs to the class of deterministic ensemble filters, since no perturbed observations are employed. As in variational and ensemble data assimilation methods, the cost function is derived using a Gaussian probability density function framework. Like other ensemble data assimilation algorithms, the MLEF produces an estimate of the analysis uncertainty (e.g., analysis error covariance). In addition to the common use of ensembles in calculation of the forecast error covariance, the ensembles in MLEF are exploited to efficiently calculate the Hessian preconditioning and the gradient of the cost function. A sufficient number of iterative minimization steps is 2–3, because of superior Hessian preconditioning. The MLEF method is well suited for use with highly nonlinear observation operators, for a small additional computational cost of minimization. The consistent treatment of nonlinear observation operators through optimization is an advantage of the MLEF over other ensemble data assimilation algorithms. The cost of MLEF is comparable to the cost of existing ensemble Kalman filter algorithms. The method is directly applicable to most complex forecast models and observation operators. In this paper, the MLEF method is applied to data assimilation with the one-dimensional Korteweg–de Vries–Burgers equation. The tested observation operator is quadratic, in order to make the assimilation problem more challenging. The results illustrate the stability of the MLEF performance, as well as the benefit of the cost function minimization. The improvement is noted in terms of the rms error, as well as the analysis error covariance. The statistics of innovation vectors (observation minus forecast) also indicate a stable performance of the MLEF algorithm. Additional experiments suggest the amplified benefit of targeted observations in ensemble data assimilation.

Using the cost function to generate Marshallian demand systems

2000 ◽  
Vol 25 (2) ◽  
pp. 209-227 ◽  
Author(s):  
Keith R. McLaren ◽  
Peter D. Rossitter ◽  
Alan A. Powell
Keyword(s):  
Cost Function ◽  
Demand Systems ◽  
The Cost

Optimal actuator placement in adaptive optics systems

2021 ◽  
pp. 107754632110324
Author(s):  
Berk Altıner ◽  
Bilal Erol ◽  
Akın Delibaşı
Keyword(s):  
Cost Function ◽  
Adaptive Optics ◽  
Disturbance Attenuation ◽  
Linear Quadratic ◽  
Placement Problem ◽  
Convex Optimization Problem ◽  
Wavefront Aberrations ◽  
The Cost ◽  
Actuator Placement ◽  
Quadratic Cost Function

Adaptive optics systems are powerful tools that are implemented to degrade the effects of wavefront aberrations. In this article, the optimal actuator placement problem is addressed for the improvement of disturbance attenuation capability of adaptive optics systems due to the fact that actuator placement is directly related to the enhancement of system performance. For this purpose, the linear-quadratic cost function is chosen, so that optimized actuator layouts can be specialized according to the type of wavefront aberrations. It is then considered as a convex optimization problem, and the cost function is formulated for the disturbance attenuation case. The success of the presented method is demonstrated by simulation results.

Sparse Incremental Delta-Bar-Delta for System Identification

2014 ◽  
Vol 665 ◽  
pp. 643-646
Author(s):  
Ying Liu ◽  
Yan Ye ◽  
Chun Guang Li
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Learning Algorithm ◽  
Learning System ◽  
The Other ◽  
Sparse System ◽  
Speed Up ◽  
Sparse System Identification ◽  
The Cost ◽  
Zero Attractor

Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

Migration velocity analysis from locally coherent events in 2‐D laterally heterogeneous media, Part I: Theoretical aspects

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1202-1212 ◽  
Author(s):  
Hervé Chauris ◽  
Mark S. Noble ◽  
Gilles Lambaré ◽  
Pascal Podvin
Keyword(s):  
Cost Function ◽  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Estimation ◽  
Velocity Analysis ◽  
Seismic Reflection Data ◽  
Velocity Models ◽  
Migration Velocity Analysis ◽  
Paraxial Ray ◽  
The Cost

We present a new method based on migration velocity analysis (MVA) to estimate 2‐D velocity models from seismic reflection data with no assumption on reflector geometry or the background velocity field. Classical approaches using picking on common image gathers (CIGs) must consider continuous events over the whole panel. This interpretive step may be difficult—particularly for applications on real data sets. We propose to overcome the limiting factor by considering locally coherent events. A locally coherent event can be defined whenever the imaged reflectivity locally shows lateral coherency at some location in the image cube. In the prestack depth‐migrated volume obtained for an a priori velocity model, locally coherent events are picked automatically, without interpretation, and are characterized by their positions and slopes (tangent to the event). Even a single locally coherent event has information on the unknown velocity model, carried by the value of the slope measured in the CIG. The velocity is estimated by minimizing these slopes. We first introduce the cost function and explain its physical meaning. The theoretical developments lead to two equivalent expressions of the cost function: one formulated in the depth‐migrated domain on locally coherent events in CIGs and the other in the time domain. We thus establish direct links between different methods devoted to velocity estimation: migration velocity analysis using locally coherent events and slope tomography. We finally explain how to compute the gradient of the cost function using paraxial ray tracing to update the velocity model. Our method provides smooth, inverted velocity models consistent with Kirchhoff‐type migration schemes and requires neither the introduction of interfaces nor the interpretation of continuous events. As for most automatic velocity analysis methods, careful preprocessing must be applied to remove coherent noise such as multiples.

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