A Multiple Dependent State Repetitive Sampling Plan Based on Performance Index for Lifetime Data with Type II Censoring

In this paper, a multiple dependent state repetitive (MDSR) sampling plan based on the lifetime performance index C-L is proposed for lifetime data with type II censoring when the lifetime of a product follows the exponential distribution or Weibull distribution. The optimal parameters of the proposed plan are determined by minimizing the average sample number while satisfying the producer's risk and consumer's risk at corresponding quality levels. Besides, the performance of the proposed plan is compared with that of the existing lifetime sampling plan in terms of the average sample number and operating characteristic curve. Two illustrative examples are given for the demonstration of the proposed plan.11Ysciescopu

Sampling Plan Using Process Loss Index Using Multiple Dependent State Sampling Under Neutrosophic Statistics

This paper presents the designing of a sampling plan using the process loss consideration for the multiple dependent state sampling under the neutrosophic statistics. The operating characteristics under the neutrosophic statistical interval method (NSIM) are developed to find the neutrosophic plan parameters of the proposed sampling plan. A non-linear optimization under NSIM is used to find the optimal neutrosophic plan parameters under the given conditions. The advantages of the proposed sampling plan are discussed over the existing sampling plans. A real example having some uncertain observations is given for the illustration purpose

EXTREME RANKED REPETITIVE SAMPLING CONTROL CHARTS

ABSTRACT In this paper, we proposed a new ranked data control chart using repetitive sampling criterion to increase the performance of detecting any shift in mean process. For the comparisons target, the average run length (ARL) of the proposed control chart based on repetitive extreme ranked set sampling computed using exact and estimated parameters. The results showed that the ARL affected negatively by the parameter estimation. Moreover, the performances of the proposed control chart is evaluated and compared with similar control chart that obtained by using different sampling schemes such as the simple random sampling, ranked set sampling, extreme ranked set sampling and repetitive ranked set sampling.. The results showed that the ranked data based control chart outperform the classical control chart in terms of the ARL

Report of the 2004 Workshop on In Situ Iron Enrichment Experiments in the Eastern and Western Subarctic Pacific

Foreword 1. BACKGROUND AND OBJECTIVES (pdf, 0.1 Mb) 2. 2004 WORKSHOP SUMMARY (pdf, < 0.1 Mb) 2.1. What have we learned from the enrichment experiments? 2.2 What are the outstanding questions? 2.3 Recommendations for SEEDS-II 3. EXTENDED ABSTRACTS OF THE 2004 WORKSHOP 3.1 Synthesis of the Iron Enrichment Experiments: SEEDS and SERIES (pdf, 0.5 Mb) Iron fertilization experiment in the western subarctic Pacific (SEEDS) by Atsushi Tsuda The response of N and Si to iron enrichment in the Northeast Pacific Ocean: Results from SERIES by David Timothy, C.S. Wong, Yukihiro Nojiri, Frank A. Whitney, W. Keith Johnson and Janet Barwell-Clarke 3.2 Biological and Physiological Responses (pdf, 0.2 Mb) Zooplankton responses during SEEDS by Hiroaki Saito Phytoplankton community response to iron and temperature gradient in the NW and NE subarctic Pacific Ocean by Isao Kudo, Yoshifumi Noiri, Jun Nishioka, Hiroshi Kiyosawa and Atsushi Tsuda SERIES: Copepod grazing on diatoms by Frank A. Whitney, Moira Galbraith, Janet Barwell-Clarke and Akash Sastri The Southern Ocean Iron Enrichment Experiment: The nitrogen uptake response by William P. Cochlan and Raphael M. Kudela 3.3 Biogeochemical Responses (pdf, 0.5 Mb) What have we learned regarding iron biogeochemistry from iron enrichment experiments? by Jun Nishioka, Shigenobu Takeda and W. Keith Johnson Iron dynamics and temporal changes of iron speciation in SERIES by W. Keith Johnson, C.S. Wong, Nes Sutherland and Jun Nishioka Dissolved organic matter dynamics during SEEDS and SERIES experiments by Takeshi Yoshimura and Hiroshi Ogawa Formation of transparent exopolymer particles during the in-situ iron enrichment experiment in the western subarctic Pacific (SEEDS) by Shigenobu Takeda, Neelam Ramaiah, Ken Furuya and Takeshi Yoshimura Atmospheric measurement by Mitsuo Uematsu 3.4 Prediction from Models (pdf, 0.3 Mb) Modelling iron limitation in the North Pacific by Kenneth L. Denman and M. Angelica Peña A proposed model of the SERIES iron fertilization patch by Debby Ianson, Christoph Voelker and Kenneth L. Denman 4. LIST OF PARTICIPANTS FOR THE 2004 WORKSHOP (pdf, < 0.1 Mb) APPENDIX 1 Report of the 2000 Planning Workshop on Designing the Iron Fertilization Experiment in the Subarctic Pacific (pdf, 1 Mb) APPENDIX 2 Terms of Reference for the Advisory Panel on Iron fertilization experiment in the subarctic Pacific Ocean (pdf, < 0.1 Mb) APPENDIX 3 Historical List of Advisory Panel Members on Iron fertilization experiment in the subarctic Pacific Ocean (pdf, < 0.1 Mb) APPENDIX 4 IFEP-AP Annual Reports (pdf, 0.1 Mb) APPENDIX 5 PICES Press Articles (pdf, 0.6 Mb) (194 page document

Identification and separation of DNA mixtures using peak area information (Updated version of Statistical Research Paper No. 25)

We introduce a new methodology, based upon probabilistic expert systems, for analysing forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions. The expert system can be used for ascertaining whether individuals, whose profiles have been measured, have contributed to the mixture, but also to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of our probabilistic methodology is illustrated on case data examples and compared with alternative approaches. The advantages are that identification and separation issues can be handled in a unified way within a single probabilistic model and the uncertainty associated with the analysis is quantified. Further work, required to bring the methodology to a point where it could be applied to the routine analysis of casework, is discussed

Developing a skill profile prediction model for typologies of offsite construction

The aim of the current research was to develop a skill profile prediction model for the typologies of offsite construction (OSC). This research aim was achieved via five research objectives, from which the key findings and research outcomes were generated. OSC is perceived as an effective solution that can be implemented to address the issues evident in traditional construction. Some of the benefits OSC generated include better working conditions, improved productivity, efficiency, reduced wastage, and improved sustainability. Industry 4.0 has promoted OSC as a way to improve the uptake of new technologies in factory-based manufacturing and onsite assembly processes. Such technological advancements can have a significant impact on the skills used in OSC, as some of the existing skills in the construction industry may be eliminated or substituted (e.g., with those in other industries), and new skills may emerge based on industry needs. The magnitude of these possible OSC skill variations has not been a focus in previous studies on OSC. As such, the current research aimed to develop a skill profile prediction model for the typologies of OSC, through the adoption of a case-study based, qualitative research method. The research generated several significant outcomes: the validated OSC typology, the OSC skill classification developed through a logical approach, and a preliminary model for OSC skill prediction. The model can assist in forecasting future OSC skill requirements. Apart from the abovementioned outcomes, deriving a unit of measurement for skill prediction and identifying the complex, non-linear relationships between OSC types and skill variations represent the key outcomes of the research. As such, the research contributes to the current body of knowledge through its development of a unique OSC typology, a master list of onsite and offsite skills, an OSC skill prediction model and a methodology for the prediction of OSC skills. The focus on OSC elements in buildings rather than infrastructure projects, incorporating a limited number of case studies and developing a preliminary model rather than a market-ready product for OSC skill prediction represent the limitations of the research. Future research directions that could be taken to expand on the findings of the current research are as follows: evaluating the skill variations of different building types in the context of varying predominant materials and conducting a fundamentally quantitative study for OSC skills prediction

Multilevel behavioral synchronization in a joint tower-building task

Human to Human sensorimotor interaction can only be fully understood by modeling the patterns of bodily synchronization and reconstructing the underlying mechanisms of optimal cooperation. We designed a tower-building task to address such a goal. We recorded upper body kinematics of dyads and focused on the velocity profiles of the head and wrist. We applied Recurrence Quantification Analysis to examine the dynamics of synchronization within, and across the experimental trials, to compare the roles of leader and follower. Our results show that the leader was more auto-recurrent than the follower to make his/her behavior more predictable. When looking at the cross-recurrence of the dyad, we find different patterns of synchronization for head and wrist motion. On the wrist, dyads synchronized at short lags, and such pattern was weakly modulated within trials, and invariant across them. Head motion instead, synchronized at longer lags and increased both within and between trials: a phenomenon mostly driven by the leader. Our findings point at a multilevel nature of human to human sensorimotor synchronization, and may provide an experimentally solid benchmark to identify the basic primitives of motion, which maximize behavioral coupling between humans and artificial agents

A Model of Movement Coordinates in Motor Cortex: Posture-Dependent Changes in the Gain and Direction of Single Cell Tuning Curves

Central to the problem of elucidating the cortical mechanisms that mediate movement behavior is an investigation of the coordinate systems by which movement variables are encoded in the firing rates of individual motor cortical neurons. In the last decade, neurophysiologists have probed how the preferred direction of an individual motor cortical cell (as determined by a center-out task) will change with posture because such changes are useful for inferring underlying cordinates. However, while the importance of shifts in preferred direction is well-known and widely accepted, posture-dependent changes in the depth of modulation of a cell's tuning curve, i.e. gain changes, have not been similarly identified as a means of coordinate inference. This paper develops a vector field framework which, by viewing the preferred direction and the gain of a cell's tuning curve as dual components of a unitary response vector, can compute how each aspect of cell response covaries with posture as a function of the coordinate system in which a given cell is hypothesized to encode its movement information. This integrated approach leads to a model of motor cortical cell activity that codifies the following four observations: 1) cell activity correlates with hand movement direction, 2) cell activity correlates with hand movement speed, 3) preferred directions vary with posture, and 4) the modulation depth of tuning curves varies with posture. Finally, the model suggests general methods for testing coordinate hypotheses at the single cell level and example protocols arc simulated for three possible coordinate systems: Cartesian spatial, shoulder-centered, and joint angle.Defense Advanced Research Projects Agency (N00014-92-J-4015); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-90-00530, IRI-97-20333); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-94-l-0940, N00014-95-1-0657)

Application of remote sensing to state and regional problems

The author has identified the following significant results. The Lowndes County data base is essentially complete with 18 primary variables and 16 proximity variables encoded into the geo-information system. The single purpose, decision tree classifier is now operational. Signatures for the thematic extraction of strip mines from LANDSAT Digital data were obtained by employing both supervised and nonsupervised procedures. Dry, blowing sand areas of beach were also identified from the LANDSAT data. The primary procedure was the analysis of analog data on the I2S signal slicer

Manufacturing Technology Today

Manufacturing Technology Today, Manufacturing Technology Abstracts, Vol. 14, No. 4, September 2015, Bangalore, India