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A p- chart addressing Braden scale assessments showed that SPC charts complement standard regression analysis. SPC amplifies patient outcomes at the microsystem level and is useful for guiding quality improvement. Macrosystems should monitor effective quality improvement initiatives in microsystems and aid the spread of successful initiatives to other microsystems, followed by system-wide analysis with regression. Further assessment of pressure ulcer incidence could illustrate improvement in the quality of care and prevent HAPUs.
This paper proposes three synthetic-type control charts to monitor the mean time-between-events of a homogenous Poisson process. The first proposed chart combines an Erlang cumulative time between events, Tr chart and a conforming run length CRL chart , denoted as Synth-Tr chart. By using a Markov chain approach, the zero- and steady-state average number of observations to signal ANOS of the proposed charts are obtained, in order to evaluate the performance of the three charts. The optimal design of the proposed charts is shown in this paper.
The proposed charts are superior to the existing T chart , Tr chart , and Synth-T chart. The aim of this study is to identify independent pre-transplant cancer risk factors after kidney transplantation and to assess the utility of G- chart analysis for clinical process control. This may contribute to the improvement of cancer surveillance processes in individual transplant centers. Risk-adjusted multivariable Cox regression was used to identify independent pre-transplant cancer risk factors. G- chart analysis was applied to determine relevant differences in the frequency of cancer occurrences.
Independent pre-transplant risk factors for cancer-free survival were age Background The aim of this study is to identify independent pre-transplant cancer risk factors after kidney transplantation and to assess the utility of G- chart analysis for clinical process control. Patients and Methods patients after kidney transplantation at our institution with a total of 9, person-years of follow-up were compared retrospectively to the general German population using site-specific standardized-incidence-ratios SIRs of observed malignancies. Control chart applications in healthcare: The concept of Statistical process control SPC was given by the physicist Walter Shewhart in order to improve the industrial manufacturing.
The SPC was firstly applied in laboratory and after then shifted to patient level in hospitals. As there is more involvement of human in healthcare, the chances of errors are also more. This paper presents the review of literature on the application of SPC and control chart in healthcare sector. Forty articles are selected out of potentially relevant searched studies. Selected studies are categorised into eight departments. Literature survey shows that most of work on control chart applications in healthcare is carried out in Surgery, Emergency and Epidemiology departments.
US, UK and Australia are the main customers where maximum amount of work was done. The US is the country where control chart in healthcare sector have been used at regular interval. This shows the gap of deploying control chart in different departments and different countries as well.
Control Chart on Semi Analytical Weighting. Semi-analytical balance verification intends to assess the balance performance using graphs that illustrate measurement dispersion, trough time, and to demonstrate measurements were performed in a reliable manner. From to , 2 weight standards were monitored before any balance operation. This work intended to evaluate if any significant difference or bias were presented on weighting procedure over time, to check the generated data reliability. This work also exemplifies how control intervals are established. Creating the Taconite Process ; flow chart of Cumulative sum control charts for assessing performance in arterial surgery.
The Melbourne Vascular Surgical Association Melbourne, Australia undertakes surveillance of mortality following aortic aneurysm surgery, patency at discharge following infrainguinal bypass and stroke and death following carotid endarterectomy. Quality improvement protocol employing the Deming cycle requires that the system for performing surgery first be analysed and optimized.
Then process and outcome data are collected and these data require careful analysis. There must be a mechanism so that the causes of unsatisfactory outcomes can be determined and a good feedback mechanism must exist so that good performance is acknowledged and unsatisfactory performance corrected. A simple method for analysing these data that detects changes in average outcome rates is available using cumulative sum statistical control charts. Data have been analysed both retrospectively from to , and prospectively during using cumulative sum control methods.
A pathway to deal with control chart signals has been developed. The standard of arterial surgery in Victoria, Australia, is high. In one case a safe and satisfactory outcome was achieved by following the pathway developed by the audit committee. Cumulative sum control charts are a simple and effective tool for the identification of variations in performance standards in arterial surgery.
The establishment of a pathway to manage problem performance is a vital part of audit activity. The construction of control chart for PM10 functional data. In this paper, a statistical procedure to construct a control chart for monitoring air quality PM10 using functional data is proposed. By means of an iterative charting procedure, a reference data set that represented a stable PM10 process was obtained.
The data were then used as a reference for monitoring future data. The application of the procedure was conducted using seven-year period of recorded data from the Klang air quality monitoring station located in the Klang Valley region of Peninsular Malaysia. The study showed that the control chart provided a useful visualization tool for monitoring air quality and was capable in detecting abnormality in the process system.
As in the case of Klang station, the results showed that with reference to , the air quality PM10 in was better than that in Competition between the homoneous companies cause the company have to keep production quality. To cover this problem, the company controls the production with statistical quality control using control chart. Shewhart control chart is used to normal distributed data. The production data is often non-normal distribution and occured small process shift.
Grand median control chart is a control chart for non-normal distributed data, while cumulative sum cusum control chart is a sensitive control chart to detect small process shift. The purpose of this research is to compare grand median and cusum control charts on shuttlecock weight variable in CV Marjoko Kompas dan Domas by generating data as the actual distribution.
The generated data is used to simulate multiplier of standard deviation on grand median and cusum control charts. Simulation is done to get average run lenght ARL Grand median control chart detects ten points that out of control , while cusum control chart detects a point out of control. It can be concluded that grand median control chart is better than cusum control chart.
Reduction in hospitalwide incidence of infection or colonization with methicillin-resistant Staphylococcus aureus with use of antimicrobial hand-hygiene gel and statistical process control charts. To evaluate the impact of serial interventions on the incidence of methicillin-resistant Staphylococcus aureus MRSA. Longitudinal observational study before and after interventions. A series of interventions including the introduction of an antimicrobial hand-hygiene gel to the intensive care unit and a hospitalwide MRSA surveillance feedback program that used statistical process control charts but not active surveillance cultures.
Serial interventions were introduced between January and May The incidence and rates of new patients colonized or infected with MRSA and episodes of MRSA bacteremia in the intensive care unit and hospitalwide were compared between the preintervention and intervention periods. Segmented regression analysis was used to calculate the percentage reduction in new patients with MRSA and in episodes of MRSA bacteremia hospitalwide in the intervention period.
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The hospitalwide rate of new patients with MRSA was 1. Shewhart control charts have been established as an expedient method for analyzing dynamic, trending data in order to identify anomalous subsystem performance as soon as such performance would exceed a statistically established baseline. Additionally, this leading indicator tool integrates a selection methodology that reduces false positive indications, optimizes true leading indicator events, minimizes computer processor unit duty cycles, and addresses human factor concerns i.
This innovation leverages statistical process control , and provides a relatively simple way to allow flight controllers to focus their attention on subtle system changes that could lead to dramatic off-nominal system performance. Shewhart control charts require normalized data. A method for normalizing the data was implemented, as was a means of selecting data windows, the number of standard deviations Sigma Level , the number of consecutive points out of limits Sequence , and direction increasing or decreasing trend data. By varying these options, and treating them like dial settings, the number of nuisance alerts and leading indicators were optimized.
The goal was to capture all leading indicators while minimizing the number of nuisances. Lean Six Sigma L6S design of experiment methodologies were employed. To optimize the results, Perl programming language was used to automate the massive amounts of telemetry data, control chart plots, and the data analysis. There are relatively few examples of quantitative approaches to quality control in educational assessment and accountability contexts. Among the several techniques that are used in other fields, Shewart charts have been found in a few instances to be applicable in educational settings.
This paper describes Shewart charts and gives examples of how…. Robust control charts in industrial production of olive oil. Acidity is one of the most important variables in the quality analysis and characterization of olive oil. During the industrial production we use individuals and moving range charts to monitor this variable, which is not always normal distributed.
After a brief exploratory data analysis, where we use the bootstrap method, we construct control charts , before and after a Box-Cox transformation, and compare their robustness and performance. An ex ante control chart for project monitoring using earned duration management observations. In the past few years, there has been an increasing interest in developing project control systems. The primary purpose of such systems is to indicate whether the actual performance is consistent with the baseline and to produce a signal in the case of non-compliance.
Recently, researchers have shown an increased interest in monitoring project's performance indicators, by plotting them on the Shewhart-type control charts over time. However, these control charts are fundamentally designed for processes and ignore project-specific dynamics, which can lead to weak results and misleading interpretations. By paying close attention to the project baseline schedule and using statistical foundations, this paper proposes a new ex ante control chart which discriminates between acceptable as-planned and non-acceptable not-as-planned variations of the project's schedule performance.
For the sake of clarity, an illustrative example has been presented to show how the ex ante control chart is constructed in practice. Furthermore, an experimental investigation has been set up to analyze the performance of the proposed control chart. As expected, the results confirm that, when a project starts to deflect significantly from the project's baseline schedule, the ex ante control chart shows a respectable ability to detect and report right signals while avoiding false alarms.
FCVCs typically display a vocabulary word, an illustration of the word, synonyms associated with the word, a sentence using a given vocabulary word, and a definition of the term in students' words. For a patient who has survived a surgery, there could be several levels of recovery. Thus, it is reasonable to consider more than two outcomes when monitoring surgical outcome quality.
The risk-adjusted cumulative sum CUSUM chart based on multiresponses has been developed for monitoring a surgical process with three or more outcomes. However, there is a significant effect of varying risk distributions on the in- control performance of the chart when constant control limits are applied. To overcome this disadvantage, we apply the dynamic probability control limits to the risk-adjusted CUSUM charts for multiresponses. The simulation results demonstrate that the in- control performance of the charts with dynamic probability control limits can be controlled for different patient populations because these limits are determined for each specific sequence of patients.
Thus, the use of dynamic probability control limits for risk-adjusted CUSUM charts based on multiresponses allows each chart to be designed for the corresponding patient sequence of a surgeon or a hospital and therefore does not require estimating or monitoring the patients' risk distribution. The aim of this paper is to demonstrate the integration of safety methodology, quality tools, leadership, and teamwork at Hanford and their significant positive impact on safe performance of work.
Deming's System of Profound Knowledge have been the principal tools and theory of an integrated management system. Coupled with involved leadership and teamwork, they have led to significant improvements in worker safety and protection, and environmental restoration at one of the nation's largest nuclear cleanup sites.
Despite acknowledgement of the importance of executive control for learning and behavior, there is a dearth of research charting its developmental trajectory as it unfolds against the background of children's sociofamilial milieus. Using a prospective, cohort-sequential design, this study describes growth trajectories for inhibitory control….
The Control Chart Techniques. Control charts are practical tools to monitor various school indicators attendance rates, standardized test scores, grades, and graduation rates by displaying data on the same scale over time. This article shows how principals can calculate the upper natural- process limit, lower natural- process limit, and upper control limit for attendance. In this study a cumulative-sum CUSUM procedure from the theory of Statistical Process Control was modified and applied in the context of person-fit analysis in a computerized adaptive testing CAT environment.
Medical imaging's technological evolution in the next century will continue to include Picture Archive and Communication Systems PACS and teleradiology. It is difficult to predict radiology's future in the new millennium with both computed radiography and direct digital capture competing as the primary image acquisition methods for routine radiography.
No matter how the acquisition, display, and archive functions change, Quality Control QC of the radiographic imaging chain will remain an important step in the imaging process. The Task Allocation Chart TAC is a tool that can be used in a medical facility's QC process to indicate the testing responsibilities of the image stakeholders and the medical informatics department. The TAC shows a grid of equipment to be serviced, tasks to be performed, and the organization assigned to perform each task.
Additionally, skills, tasks, time, and references for each task can be provided. The TAC can be used to clarify responsibilities during warranty and paid maintenance periods. In general, observations of the statistical process control are assumed to be mutually independence. However, this assumption is often violated in practice. Consequently, statistical process controls were developed for interrelated processes , including Shewhart, Cumulative Sum CUSUM , and exponentially weighted moving average EWMA control charts in the data that were autocorrelation.
One researcher stated that this chart is not suitable if the same control limits are used in the case of independent variables.
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For this reason, it is necessary to apply the time series model in building the control chart. A classical control chart for independent variables is usually applied to residual processes. This procedure is permitted provided that residuals are independent. In , Shewhart modification for the autoregressive process was introduced by using the distance between the sample mean and the target value compared to the standard deviation of the autocorrelation process.
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Image registration assessment in radiotherapy image guidance based on control chart monitoring. Image guidance with cone beam computed tomography in radiotherapy can guarantee the precision and accuracy of patient positioning prior to treatment delivery. During the image guidance process , operators need to take great effort to evaluate the image guidance quality before correcting a patient's position. This work proposes an image registration assessment method based on control chart monitoring to reduce the effort taken by the operator.
According to the control chart plotted by daily registration scores of each patient, the proposed method can quickly detect both alignment errors and image quality inconsistency. Therefore, the proposed method can provide a clear guideline for the operators to identify unacceptable image quality and unacceptable image registration with minimal effort. Experimental results demonstrate that by using control charts from a clinical database of 10 patients undergoing prostate radiotherapy, the proposed method can quickly identify out-of- control signals and find special cause of out-of- control registration events.
Application of capability indices and control charts in the analytical method control strategy. In this study, we assessed the usefulness of control charts in combination with the process capability indices, C pm and C pk , in the control strategy of an analytical method. The traditional X- chart and moving range chart were used to monitor the analytical method over a 2-year period.
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The results confirmed that the analytical method is in- control and stable. Different criteria were used to establish the specifications limits i. Similar results were obtained using the C pk index. The method capability was also assessed as a function of method performance for fixed analyst requirements.
The results indicate that the method does not meet the requirements of the analytical target approach. A real-example data of a SEC with light-scattering detection method was used as a model whereas previously published data were used to illustrate the applicability of the proposed approach. This paper describes new results from a base model of brewing from a bed of packed coffee grains. The model solves for the diffusion of soluble species out of a distribution of particles into the flow through the bed pore space.
It requires a limited set of input parameters. It gives a simple picture of the basic physics of coffee brewing and sets out a set of reduced variables for this process. The importance of bed extraction efficiency is elucidated. A coffee brewing control chart has been widely used to describe the region of ideal coffee brewing for some 50 years.
A new chart is needed, however, one that connects actual brewing conditions weight, flow rate, brew time, grind, etc. The paper shows a new approach to brewing control charts , including brew time and bed extraction efficiency as control parameters. Using the base model, an example chart will be given for a particular grind ratio of coarse to fine particles, and an "espresso regime" will be picked out. From such a chart yield, volume and strength of a brew can be read off at will. Computing exact bundle compliance control charts via probability generating functions.
Compliance to evidenced-base practices, individually and in 'bundles', remains an important focus of healthcare quality improvement for many clinical conditions. The exact probability distribution of composite bundle compliance measures used to develop corresponding control charts and other statistical tests is based on a fairly large convolution whose direct calculation can be computationally prohibitive. Various series expansions and other approximation approaches have been proposed, each with computational and accuracy tradeoffs, especially in the tails.
This same probability distribution also arises in other important healthcare applications, such as for risk-adjusted outcomes and bed demand prediction, with the same computational difficulties. As an alternative, we use probability generating functions to rapidly obtain exact results and illustrate the improved accuracy and detection over other methods. Numerical testing across a wide range of applications demonstrates the computational efficiency and accuracy of this approach.
Endpoint in plasma etch process using new modified w-multivariate charts and windowed regression. Endpoint detection is very important undertaking on the side of getting a good understanding and figuring out if a plasma etching process is done in the right way, especially if the etched area is very small 0. It truly is a crucial part of supplying repeatable effects in every single wafer. When the film being etched has been completely cleared, the endpoint is reached. To ensure the desired device performance on the produced integrated circuit, the high optical emission spectroscopy OES sensor is employed.
The huge number of gathered wavelengths profiles is then analyzed and pre- processed using a new proposed simple algorithm named Spectra peak selection SPS to select the important wavelengths, then we employ wavelet analysis WA to enhance the performance of detection by suppressing noise and redundant information. The employ of three aforementioned statistics is motivated by controlling mean shift, variance shift and their ratio CV if both mean and SD are not stable. The control charts show their performance in detecting endpoint especially W-mean Hotelling chart and the worst result is given by CV statistic.
As the best detection of endpoint is given by the W-Hotelling mean statistic, this statistic will be used to construct a windowed wavelet Hotelling polynomial regression. This latter can only identify the window containing endpoint phenomenon. The use of control charts by laypeople and hospital decision-makers for guiding decision making. Graphs presenting healthcare data are increasingly available to support laypeople and hospital staff's decision making.
When making these decisions, hospital staff should consider the role of chance-that is, random variation. Given random variation, decision-makers must distinguish signals sometimes called special-cause data from noise common-cause data. Unfortunately, many graphs do not facilitate the statistical reasoning necessary to make such distinctions.
Control charts are a less commonly used type of graph that support statistical thinking by including reference lines that separate data more likely to be signals from those more likely to be noise. The current work demonstrates for whom laypeople and hospital staff and when treatment and investigative decisions control charts strengthen data-driven decision making. We present two experiments that compare people's use of control and non- control charts to make decisions between hospitals funnel charts vs. As expected, participants more accurately identified the outlying data using a control chart than using a non- control chart , but their ability to then apply that information to more complicated questions e.
The discussion highlights some common concerns about using control charts in hospital settings. Control chart pattern recognition using RBF neural network with new training algorithm and practical features.
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The control chart patterns are the most commonly used statistical process control SPC tools to monitor process changes. When a control chart produces an out-of- control signal, this means that the process has been changed. The proposed method consists of four main modules: In the feature extraction module, shape and statistical features are used. Recently, various shape and statistical features have been presented for the CCPs recognition.
In the feature selection module, the association rules AR method has been employed to select the best set of the shape and statistical features. Therefore, a new learning algorithm based on the bees algorithm has been used in the learning module. Most studies have considered only six patterns: Since three patterns namely Normal, Stratification, and Systematic are very similar to each other and distinguishing them is very difficult, in most studies Stratification and Systematic have not been considered.
Regarding to the continuous monitoring and control over the production process and the exact type detection of the problem encountered during the production process , eight patterns have been investigated in this study. The proposed method is tested on a dataset containing samples samples from each pattern and the results showed that the proposed method has a very good performance.
Published by Elsevier Ltd. Control charts for monitoring accumulating adverse event count frequencies from single and multiple blinded trials. Conventional practice monitors accumulating information about drug safety in terms of the numbers of adverse events reported from trials in a drug development program. Estimates of between-treatment adverse event risk differences can be obtained readily from unblinded trials with adjustment for differences among trials using conventional statistical methods. Recent regulatory guidelines require monitoring the cumulative frequency of adverse event reports to identify possible between-treatment adverse event risk differences without unblinding ongoing trials.
Conventional statistical methods for assessing between-treatment adverse event risks cannot be applied when the trials are blinded. CUSUM charts for monitoring adverse event occurrence in a Bayesian paradigm are based on assumptions about the process generating the adverse event counts in a trial as expressed by informative prior distributions. This article describes the construction of control charts for monitoring adverse event occurrence based on statistical models for the processes , characterizes their statistical properties, and describes how to construct useful prior distributions.
Application of the approach to two adverse events of interest in a real trial gave nearly identical results for binomial and Poisson observed event count likelihoods. A Randomized Controlled Trial. Our objective was to evaluate the effect of medical scribes on physician satisfaction, patient satisfaction, and charting efficiency.
METHODS We conducted a randomized controlled trial in which physicians in an academic family medicine clinic were randomized to 1 week with a scribe then 1 week without a scribe for the course of 1 year. Scribes drafted all relevant documentation, which was reviewed by the physician before attestation and signing.
In encounters without a scribe, the physician performed all charting duties.
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We found that scribes produced significant improvements in overall physician satisfaction, satisfaction with chart quality and accuracy, and charting efficiency without detracting from patient satisfaction. Scribes appear to be a promising strategy to improve health care efficiency and reduce physician burnout. The application of Preventive Maintenance PM and Statistical Process Control SPC are important practices to achieve high product quality, small frequency of failures, and cost reduction in a production process.
However there are some points that have not been explored in depth about its joint application. First, most SPC is performed with the X-bar control chart which does not fully consider the variability of the production process. Second, many studies of design of control charts consider just the economic aspect while statistical restrictions must be considered to achieve charts with low probabilities of false detection of failures. Third, the effect of PM on processes with different failure probability distributions has not been studied. Hence, this paper covers these points, presenting the Economic Statistical Design ESD of joint X-bar-S control charts with a cost model that integrates PM with general failure distribution.
Experiments showed statistically significant reductions in costs when PM is performed on processes with high failure rates and reductions in the sampling frequency of units for testing under SPC. A run chart is a line graph of a measure plotted over time with the median as a horizontal line. The main purpose of the run chart is to identify process improvement or degradation, which may be detected by statistical tests for non-random patterns in the data sequence. We studied the sensitivity to shifts and linear drifts in simulated processes using the shift, crossings and trend rules for detecting non-random variation in run charts.
The trend rule is virtually useless for detection of linear drift over time, the purpose it was intended for. Principles of statistical process control are applied to a clinical setting through the use of control charts to detect changes, as part of treatment planning and clinical decision-making processes. The logic of control chart analysis is derived from principles of statistical inference. Sample charts offer examples of evaluating baselines and…. This year's paper provides more detail on management's use of SPC and control charts and discusses their integration into an executive summary using the popular color-cod3ed dashboard methodology.
Fluor Hanford has applied SPC in a non-traditional that is non-manufacturing manner. Shewhart's year-old control-chart methodologies have been updated to modern data processing , but are still founded on his sound, tried and true principles. After more than 50 years of producing nuclear weapons, Hanford--which covers square miles in southeastern Washington state--is now focused on three outcomes: Crude versus case-mix-adjusted control charts for safety monitoring in thyroid surgery. Patient-safety monitoring based on health-outcome indicators can lead to misinterpretation of changes in case mix.
This study aimed to compare the detection of indicator variations between crude and case-mix-adjusted control charts using data from thyroid surgeries. The study population included each patient who underwent thyroid surgery in a teaching hospital from January to May Patient safety was monitored according to two indicators, which are immediately recognisable postoperative complications: Each indicator was plotted monthly on a p- control chart using exact limits. We evaluated the outcomes of thyroidectomies. The overall proportions of immediate recurrent laryngeal nerve palsy and hypocalcaemia were 7.
The single special cause of variation that occurred was only detected by the case-mix-adjusted p- chart.
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There was good agreement in the detection of indicator variations between crude and case-mix-adjusted p- charts. The joint use of crude and adjusted charts seems to be a reasonable approach to increase the accuracy of interpretation of variations in outcome indicators. Detecting defective electrical components in heterogeneous infra-red images by spatial control charts. Distribution network components connect machines and other loads to electrical sources. If resistance or current of any component is more than specified range, its temperature may exceed the operational limit which can cause major problems.
Therefore, these defects should be found and eliminated according to their severity. Although infra-red cameras have been used for inspection of electrical components, maintenance prioritization of distribution cubicles is mostly based on personal perception and lack of training data prevents engineers from developing image processing methods. New research on the spatial control chart encouraged us to use statistical approaches instead of the pattern recognition for the image processing.
In the present study, a new scanning pattern which can tolerate heavy autocorrelation among adjacent pixels within infra-red image was developed and for the first time combination of kernel smoothing, spatial control charts and local robust regression were used for finding defects within heterogeneous infra-red images of old distribution cubicles. This method does not need training data and this advantage is crucially important when the training data is not available. A high-impact practice is to incorporate experiential learning projects when teaching difficulty subject matters so as to enhance students' understanding and interest in the course content.
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