Emphasis is increasingly being placed on the monitoring and comparison of clinical outcomes between healthcare providers. Funnel plots have become a standard graphical methodology to identify outliers and comprise plotting an outcome summary statistic from each provider against a specified ‘target’ together with upper and lower control limits. With discrete probability distributions it is not possible to specify the exact probability that an observation from an ‘in-control’ provider will fall outside the control limits. However, general probability characteristics can be set and specified using interpolation methods. Guidelines recommend that providers falling outside such control limits should be investigated, potentially with significant consequences, so it is important that the properties of the limits are understood.Control limits for funnel plots for the Standardised Mortality Ratio (SMR) based on the Poisson distribution were calculated using three proposed interpolation methods and the probability calculated of an ‘in-control’ provider falling outside of the limits. Examples using published data were shown to demonstrate the potential differences in the identification of outliers.p). The second method resulted in such an observation falling outside either limit with a probability that could be either greater or less than p, depending on the expected number of events. The third method led to a probability that was always greater than, or equal to, p.The first interpolation method ensured that the probability of an observation of an ‘in control’ provider falling outside either limit was always less than a specified nominal probability (The use of different interpolation methods can lead to differences in the identification of outliers. This is particularly important when the expected number of events is small. We recommend that users of these methods be aware of the differences, and specify which interpolation method is to be used prior to any analysis. However, when the outcome follows a discrete probability distribution it is not possible to specify the exact probability with which an observation from an ‘in-control’ provider will fall outside of the limits Clinical outcomes are very often discrete counts  and these are usually compared through the use of the Standardised Mortality Ratio (SMR). The SMR is defined as the ratio of the observed number of events to the number expected using the outcomes of a reference population and it is usually assumed that the observed number of events is an observation from a Poisson distribution Given that it is not possible to stipulate the exact probability that an observation from an ‘in-control’ provider will fall outside the control limits, because of the discrete nature of the outcome, there are several ways in which the probability characteristics of the control limits for the SMR can be specified. It is often required that the probability of an observation from an ‘in control’ provider falling outside either the upper or the lower control limit is always less than the specified nominal probability: for example, the probability of falling above the upper limit, or below the lower limit, of 95% control limits is always less than 0.025. Another option is to construct the limits so that the probability of an observation of falling outside of the limits is always greater than or equal to the nominal probability. A further approach is some combination of these to obtain limits that produce a probability that is ‘closest’ to the nominal probability.Interpolation methods are often used to improve the appearance of the control limits on funnel plots by producing smooth, rather than jagged, control limits and also to ensure that no observed point can fall ambiguously directly on the limit In this paper we describe three different interpolation methods and investigate their probability characteristics and interpretation.O) to the expected number of events (E): i.e. SMR = O/E, where O is an integer value.The Standardised Mortality Ratio (SMR) is defined as the ratio of the observed number of events (p)% control interval is required. Ideally with such an interval, the probability that an observation from an ‘in-control’ healthcare provider will fall within the control limits is 1–2p. Hence, the probability that the observation will fall above the upper control limit is p , and the probability it will fall below the lower control limit is also p. For outcomes that are observations from a discrete probability distribution it is not possible to draw limits which fulfil these criteria exactly and three approximation methods are described below.It is assumed that a 100 >p. Similarly, the upper control limit U can be defined as Uo/E, where Uo is the largest integer such that P(O ≥ Uo) >pThe lower control limit O can only take integer values (as it represents an observed number of events) it is not possible for an observed value of the SMR to take a value that is greater than U/Eo but less than (Uo+1)/E. Hence, the upper limit U can, in fact, take any value in the range U/Eo ≤ U < (Uo+1)/E and still satisfy the requirement of being the maximum value for which an observed SMR from an ‘in control’ provider will fall above the limit with a probability of less than the nominal probability p. Similarly, the lower interpolated limit will lie between L/Eo and L−1)/E.(oSince U/Eo and (Uo+1)/E (L/Eo and L−1)/E(oUo and (Uo+1) and the lower limit between Lo and (L−o1).Method 1 comprises plotting the interpolated upper limit at a weighted average between (oU+1)/E  and the pU−o0.5)/E up to, but not including, (Uo+0.5)/E. Similarly, the lower control limit will lie in the region from (Lo+0.5)/E down to, but not including, (L−o0.5)/E. Since the values of the interpolated upper control limit can take values both less than and greater than Uo/E, the probability that the observed SMR from an ‘in control’ provider will fall above the upper limit, or below the lower limit, can be greater than or less than p depending on the expected number of events. This method ensures that on average the probability of ‘in-control’ providers falling outside each limit is p. This is similar to the use of mid-P confidence intervals An alternative interpolation method (Method 2) comprises drawing the control limits ‘closest’ to the nominal probability U−o1)/E up to, but not including, U/Eo. Similarly the lower limit is specified to lie from (Lo+1)/E down to, but not including, L/Eo. Therefore, in this case the probability of an observation falling above the upper limit or below the lower limit is always greater than or equal to the nominal probability p. Method 3 comprises plotting the upper control limit at a point that is a weighted average between than Uo and (U−o1) and the lower limit between Lo and (L+o1)  .E) for each of the methods up to E≤30. The median probability was calculated for E≤10 and compared to 250<E≤500.The three interpolation methods were initially compared by plotting the true probability of an observation from an ‘in-control’ provider falling outside the upper and lower 95% control limits against the expected number of events , a survey of neonatal care in the East Midlands and Yorkshire regions of the UK For the examples, the reported SMRs were plotted on the funnel plot showing 95% control limits calculated using the three interpolation methods.SAS v 9.2 was used for all analyses and to produce the Figures.E) for each of the interpolation methods for E≤30  and the median probability of falling below the lower limit was 0.0084 (range 0.0000 to 0.0250). For 250<E≤500 the median probability of falling above the upper limit was 0.0236 (range: 0.0217 to 0.0250) and below the lower limit was 0.0235 (range: 0.0214 to 0.0250).For Method 1 (first row of p) depending on the value of E. The median probability of falling above the upper limit was 0.0228 (range: 0.0010 to 0.0488) and below the lower limit was 0.0163 (range: 0.0000 to 0.0500) for E≤10. When the E was larger, the probability tended towards the nominal probability and the median probability of falling above the upper limit when E was 250<E≤500 was 0.0250 (range: 0.0231 to 0.0269) and the median probability of falling below the lower limit was 0.0250 (range: 0.0231 to 0.0269).As expected, with Method 2 (second row of E≤10 was 0.0383 (range: 0.0250 to 0.2149) and below the lower limit was 0.0554 (range: 0.0250 to 0.9990). When the expected number of events was larger (250<E≤500) the median probability of falling above the upper limit was 0.0265 (range: 0.0250 to 0.0288) and the median probability of falling below the lower limit was 0.0266 (range: 0.0250 to 0.0291). For the lower limit of this method, when E<2.3 the probability of falling below the lower limit is greater than 0.1, and these values are missing from The median probability of being identified as falling above the upper limit with Method 3  was less than 2 and the limits constructed using the three different methods are shown. It can be seen that although interpolation methods have been used and the control limits ‘smoothed’ these do not necessarily produce monotonic functions. Only one point fell above the upper control limit obtained using Methods 1 and 2: point A. However, three further points  fell above the limits calculated using Method 3 and would have been identified as potential outliers using the limits created by this interpolation. As the difference between the methods represents, at most, one observed event, points B, C and D would be identified as potential outliers by all of the methods if the number of observed deaths for each of these surgical procedures was increased by one.The data from the Oxford John Radcliffe Hospital NHS Trust are shown in The Neonatal Survey (TNS) E from 5 to 30. However, point E falls above the upper limit obtained from Method 2 and would be identified as a potential outlier using this interpolation method. Two further points (F & G) lie above the limits obtained using Method 3.Using the data from The Neonatal Survey (TNS), no points fell above the upper control limits calculated using Method 1: shown in Therefore, use of Method 1 would result in no PCTs being identified as potential outliers, Method 2 would result in one PCT being identified and Method 3 would result in three PCTs being identified.Funnel plot limits are increasingly used as a method for displaying outcomes from healthcare providers. Providers which are identified as falling outside the funnel plot control limits are viewed as outliers and usually then subject to some form of investigation p. This probability can sometimes be substantially less than p especially when the expected number of events E is small. On the other hand, using Method 3 the probability of such an observation falling outside each limit is always greater than, or equal to, p. For small values of E the actual probability can be much larger than p. Compared to Method 3, the use of Method 1 will reduce the probability of providers being falsely identified as outliers by chance alone (i.e. increased specificity) but will increase the probability of not identifying providers with truly divergent rates of outcome (i.e. reduced sensitivity).It has been shown that when the outcome statistic is based on count data (in this case the SMR) it is not possible to specify the exact probability of an observation from an ‘in-control’ provider falling outside of the control limits p. This probability is either greater than or less than p depending on the value of E. However, it is not obvious whether the probability is greater or less than p at any particular value of E, in other words for any particular healthcare provider. Although as the true probability is not constant across all values of E for any of the methods (and can never be so) this characteristic could perhaps be viewed as only a minor disadvantage of Method 2. However, Methods 1 and 3 do at least allow us to know that the probability will always be less than (Method 1), or greater than (Method 3), the nominal probability.Method 2 offers a compromise as the probability of an observed SMR from an ‘in control’ provider lying outside each of the control limits is ‘on average’ equal to not more than 0.025 yet typically achieves probabilities of 0.016 (when E≤10), or an approach giving narrower intervals for which the actual probability could be greater than 0.025 but is quite close to 0.025? The answer to that is likely to depend on the risks and benefits of providers being identified, falsely or otherwise, as outlier. If, for example, the outcome being compared is paediatric death, a conservative approach increasing the probability of identifying healthcare providers with truly divergent rates of poor outcome may be wanted and Method 3 the most appropriate choice with a risk that some providers will be unnecessarily investigated. Conversely, if the risks and costs of unnecessary investigation of healthcare providers falsely identified outweigh the risk of missing some providers with divergent rates of outcome, for example minor infection, Method 1 may be most appropriate choice. However, in the absence of special circumstances perhaps Method 2 should be the default method as it has the characteristic that the probability that an observation from an ‘in-control’ provider will fall outside either control limit with a probability that overall is closest to the nominal probability: although for any particular provider this may be far from the nominal probability.This issue is not unique to funnel plot control limits but also arises when estimating confidence intervals for any discrete probability distribution In reality, the differences seen between the interpolation methods were particularly apparent and potentially important when the expected number of events was small. Indeed the difference in the limits from the three methods is, at most, only 1 observed event. Such a difference is unlikely to be important when the number of events is large. However, this can be an important difference when small datasets are used or the outcome is rare. Funnel plot control limits are used, and are increasingly likely to be used, to try to identify potentially poorly performing healthcare providers when the number of events is small. While the reporting of hospital-wide outcome statistics such as the Summary Hospital–level Mortality Indicator (SHMI) Our examples, using data from the Oxford John Radcliffe Hospital NHS Trust and The Neonatal Survey, have shown that the choice of interpolation method can result in the labelling of a provider as a potential outlier using one method whilst not by another, leading to real implications for that provider. Funnel plots are recommended to be used to identify providers for further investigation with potentially outlying performance Ux+0.5)/E There are further methods for choosing the interpolated value for the control limits. However other potential methods were not investigated in this paper because they offered no advantage over the three methods included here. For example the “midpoint convention”, i.e. . The arguments and conclusions set out in this paper will hold whichever discrete distribution is assumed.Interpolation methods can be used to improve the aesthetics and interpretability of control limits on funnel plots as well as set their probability characteristics. It has been shown in this paper that the different interpolation methods presented alter the probability of a health care provider being identified as an outlier. Care should be taken to understand the properties of the limits drawn before using them to identify outliers. All methods here potentially have a use depending on the clinical question of interest but the choice of method should be undertaken prior to analysis.


In vitro dissolution time profiles at six different sampling times were used as target data in training the neural network for formulation optimization. A multi layer perceptron with one hidden layer was constructed using Matlab®, and the number of nodes in the hidden layer was optimized by trial and error to develop a model with the best predictive ability. The results revealed that a neural network with nine nodes was optimal for developing and optimizing formulations. Simulations undertaken with the training data revealed that the constructed model was useable. The optimized neural network was used for optimization of formulation with desirable release characteristics and the results indicated that there was agreement between the predicted formulation and the manufactured formulation. This work illustrates the possible utility of artificial neural networks for the optimization of pharmaceutical formulations with desirable performance characteristics. An artificial neural network was used to optimize the release of salbutamol sulfate from hydrophilic matrix formulations. Model formulations to be used for training, testing and validating the neural network were manufactured with the aid of a central composite design with varying the levels of Methocel Pharmaceutical formulations are complex systems in which the properties and performance characteristics are influenced by numerous formulation and process factors that may not be easily understood. Pharmaceutical optimization has been defined as the implementation of systematic approaches to establish the best possible combination of materials and/or process variables under a given set of conditions that will result in the production of a quality pharmaceutical product with predetermined and specified characteristics each time it is manufactured . The useANN are computational tools that emulate the interconnected neurological structures of the human brain and the ability of the human brain to learn and solve problems through pattern recognition . ANN simviz., an input, hidden and output layer. Each layer has a number of neurons or nodes that are fully interconnected with neurons in the neighboring layers as shown in The most commonly used network architecture for pharmaceutical applications is the multi-layer perceptron, which consists of three layers, The input layer consists of one or more input nodes or processing elements that distribute input data to nodes located in the hidden layer of the ANN. Each node in the input layer can represent an independent variable, for example, an amount of polymer in a formulation or machine operating conditions such as compression force for a tablet press. The input layer does not process any information, but serves as a distribution point for information to be delivered to the hidden layer. The hidden layer can be made up of one of more layers of parallel nodes  has been used extensively for the manufacturing of tablets ,29,30,31® ,33,34 an® ,24 have ulations ,36. Thes2β agonist, is a water-soluble salt of salbutamol that is used for the treatment of bronchoconstriction and bronchospasm in patients with reversible obstructive airway disease and chronic obstructive pulmonary disease [Salbutamol sulfate, a short-acting  disease ,38. Salbin vitro release profile that was similar to a reference formulation, viz., Asthalin®8 ER . The levels of Methocel® K100M, xanthan gum, Carbopol® 974P and Surelease® as the granulating fluid were varied using formal experimental design, specifically a central composite design and the formulation was optimized using ANN to develop a formulation with a similar in vitro dissolution profile to that of the reference formulation. In addition, the ANN architecture was optimized by analyzing the efficiency of ANN that have different numbers of nodes in the hidden layer and to investigate the feasibility of using the 2f similarity factor for formulation optimization. The objective of this study was to develop an optimized hydrophilic matrix formulation for salbutamol sulfate with an ® K100M (HPMC) , Avicel® PH101 , xanthan gum , Surelease® , Carbopol® 974P NF , magnesium stearate  and colloidal silica  were used as received. All other reagents were at least of analytical reagent grade and used as received.Salbutamol sulfate was donated by Aspen-Pharmacare . Methocel® R2008a  and was used for studying the impact of chosen formulation variables on the extent of drug release from matrix formulations. The independent variables, viz., the amounts of Methocel® K100M, xanthan gum, Carbopol® 974P and percent composition of Surelease® as the granulating fluid that were assessed are listed in In vitro release data from the central composite design was used to train the ANN for formulation optimization. A central composite design for formulation optimization was generated using the Model Based Calibration Toolbox of Matlab® is a 25% w/w aqueous dispersion of ethylcellulose. Surelease® was diluted for use as the granulating liquid and dispersion were prepared by accurately weighing the correct amount of dispersion on a top loading balance  and diluting by weight with HPLC-grade water to concentrations of 4, 8, 12, 16 and 20% w/w. Batch sizes of 1000 tablets were manufactured for each formulation. Matrix tablets were manufactured by dry blending salbutamol sulfate and the appropriate quantities of Methocel® K100M, xanthan gum, Carbopol® 974P and Avicel® PH101 in a Saral® Rapid Mixer and Granulator  in a 5 L bowl using a speed of 100 rpm on the main impeller for 15 min. Thereafter, 120 g of Surelease® diluted to the desired concentration with water (4–20% w/w) was gradually sprayed onto the powder blend using a manual spray. The bed was agitated using speeds of 120 rpm and 1000 rpm for the main impeller and chopper, respectively. The wet mass was mixed for an additional 5 min at the same speed and removed from the granulator and allowed to dry on wax paper for 24 h at a temperature of 22 ºC. Thereafter, the granules were sieved and the fraction between 315 and 800 mm was collected and weighed, after which magnesium stearate and colloidal silica equivalent of 1% w/w and 0.5% w/w, respectively, were sieved and added to the blend that was mixed for a further 3 min at 100 rpm using the main impeller only. Finally, the lubricated granules were compressed into tablets using 9 mm biconvex punches on a Manesty® B3B rotary tablet press to a uniform weight of 220 mg. Surelease® Bio-Dis dissolution apparatus  was used for the assessment of the in vitro release characteristics of salbutamol sulfate formulations. A model VK 750D digitally controlled water circulation/heater  was used to maintain the temperature of the dissolution medium at 37 ± 0.5 ºC. A mesh of pore size 177 µm was used to retain the dosage form in the inner tubes and a dip speed of 10 dpm was used as the agitation rate. The dosage forms were maintained in 200 mL of buffers at pH 1.2, 4.5, 6.0 for 1, 1 and 2 h respectively and at pH 6.8 for the rest of the dissolution test and samples were collected after 1, 2, 4, 6, 8 and 12 h from commencement of the dissolution studies. A VanKel® filter . A 1.5 mL aliquot was placed in a sample vial and 100 µL of a terbutaline sulfate solution (400 µg/mL) was added to the vial such that the final concentration of the internal standard was approximately 25 µg/mL. The samples were analyzed using a validated HPLC method to determine the percent drug released at different stages of the dissolution test.A 2 mL aliquot of dissolution medium was removed from the dissolution vessels using an electronic pipette (Boeckel & Co (GmbH & Co), Hamburg, Hamburg, Germany) and filtered through a 0.45 µm Millipore® Model 7010 injector  fitted with a 20 µL fixed volume loop and a 250 µL GASTIGHT® Model 1725 syringe , a Linear UV/VIS-500 Model 6200-9060 detector ; and a Spectra Physics SP 4600 integrator . Separation was achieved under isocratic conditions using a mobile phase consisting of 20% v/v ACN in 18 mM phosphate buffer at pH = 4, containing 15 mM sodium octane sulphonate and a Phenomenex® Hyperclone® column, 5 μm, 150 mm × 4.6 mm  at ambient temperature (22 ºC). Samples were monitored by UV detection at 220 nm. The volume of injection was 20 μL and a flow rate of 1.0 mL/min was used for the separation.HPLC analysis was performed using a modular HPLC system that consisted of a Model P100 dual piston pump , a Model AS100 autosampler , which was equipped with a Rheodyne® R2008a , was used to write mathematical code for training and evaluating the ANN developed and used for formulation optimization.Commercially available software, Matlabi.e., a three-layer back propagation network was chosen for the purposes of this study. Four input factors corresponding to different levels of Methocel® K100M (1x), xanthan gum (2x), Carbopol® 974P (3x) and Surelease® (4x) were used as units in the input layer of the ANN. The data generated from the central composite design was used as the training set for the ANN. The percent drug dissolved at different stages of the dissolution test (n = 6) was used as output the layers or the target data during ANN training. Specifically, ihy = % salbutamol sulfate released, such that i = 1, 2, 4, 6, 8 and 12 h. There were 180 (30 × 6) input-target data pairs that were used to train, test and validate the neural network. The data was split into three categories, viz., the training, test and validation data sets, where 67% of the data set was used for training and 33% as the test data. The validation set was selected from the test data and constituted 67% the test data set. A neural network composed of an input and output layer with one hidden layer, in vitro dissolution testing was initially randomized to allow for efficient training of the network. Prior to training, the mapminmax function was used to scale the inputs and targets so that the values for these fell within a range of -1 to +1 based on the highest and lowest values in the data sets. Scaling the factors to this range is useful for efficient training of the network since it prevents bias in training if some of the values are significantly bigger or smaller than the other values in the training data set. If mapminmax is used to scale the target value, then the output of the network will be trained to produce outputs in the range -1 to +1 and these must then be converted back to original values by using the reverse of this function. The input and target data from A sigmoidal function (logsig) (Equation 1) was used as the transfer function for the hidden layer and back propagation of errors. Where, Ʃ = sum of the input to the node. A purelin function was used for the output layer. The Levenberg–Marquardt algorithm for back-propagation with a gradient descent and momentum weight and bias learning function, was used to train the network. The Mean Squared Error (MSE) was used as the performance function and training was terminated after either 200 validation failures or 800 epochs or iterations of the network, whichever came first. versus original values and computing the correlation coefficient for each of the responses in the output layer. These results were then compared, and the closer the value was to 1 then the better the predictive capability of the model. Several training sessions with different numbers of nodes (3–10) in the hidden layer were conducted in order to resolve the optimal ANN structure and each of these models were compared in this way. The optimal network architecture was then selected for further application and this final model was retrained with the entire data set to obtain a network model that could be used for formulation optimization and further simulations. Activation of the neural network was achieved by simulating the neural network using normalized training data. A post-process activation on the predicted data set was then required to convert it back to the original input range. In the final step, the predicted data was then compared with the original data set by plotting the predicted 2f similarity factor (Equation 2) [Formulation optimization was conducted using the ation 2)  for the n = is the number of dissolution sample times, t = the time sample index, tR = the mean percent dissolved at time t for the reference dissolution profile and tT = the mean percent dissolved at time t for the test dissolution profile. Functions for formulation optimization using simulation of the trained ANN and the 2f similarity factor were written in the Matlab® editor . All possible permutations of the formulation variables, viz., Methocel® K100M, xanthan gum, Carbopol® 974P and Surelease®, within the experimental domain were generated using a brute force method and were then simulated using the trained ANN. The resultant simulated profiles were compared with the dissolution profiles of the reference formulation, Asthalin®8 ER . The function was used to determine the formulation that had the highest value for similarity. This formulation was subsequently manufactured and dissolution testing was conducted using USP Apparatus 3 as previously described. Where, The dissolution test results are shown in in vitro dissolution profiles depicted in The The number of nodes in the hidden layer that are required to produce a good predictive network depends on the complexity of the problem to be solved, the number of nodes in both the input and output layers, and the size of the training data set. Furthermore, the amount of noise in the target data, network architecture, the required accuracy of the prediction and the training algorithm that is used are also important factors that determine the number of nodes that are required in the hidden layer . The num2R values for each of the response factors was selected as the optimal architecture to use. Although several approaches, including Kolmogorov’s theorem  and CarpThe impact of changing the number of nodes in the hidden layer of an ANN on the predictive ability of the ANN is depicted in 2R for all stages of the dissolution test data. As the number of nodes is increased to above nine, it is also apparent that there is a decrease in the efficiency of the network, which is likely due to overtraining of the network. Inspection of The results reveal that when a neural network that had nine nodes in the hidden layer was used, the lowest or best validation error was observed at epoch 317 and was equivalent to 0.0249. The low error indicates that the model may be used to accurately predict the relationship between input and target data pairs and may therefore be used for prediction of outputs given a set of inputs. The correlation data for each of the different data points are summarized in vs. the experimentally determined or observed values in the test data set are shown in The results from the prediction of test data generated by the ANN These results show that an ANN that has nine nodes in the hidden layer has good predictive capabilities and therefore may be used for formulation optimization and simulation of the impact of formulation variables on the dissolution rate of salbutamol sulfate from hydrophilic matrix formulations. The network was therefore trained with all data and was used for the formulation optimization exercise. in vitro release profile for hypothetical formulations was written in Matlab® code  and used to simulate different in vitro release profiles. This strategy was adopted as it would be useful to determine the predictive ability of the trained network and to establish whether the network could be used to predict “unseen” data within a data set. For example, the network could be used to test a posed question such as “What would the percent drug dissolved at the different dissolution time points be from a formulation that contains 30 mg Methocel® K100M, 75 mg xanthan gum, 5 mg Carbopol® 974P that was granulated using a 16% w/w solution of Surelease®?” . The neural network is then asked to find the corresponding solution to the posed question and the percent drug released after 1, 2, 4, 6, 8 and 12 h would be 32.5%, 43.4%, 60.9%, 74.4%, 85.8% and 96.4% respectively. The corresponding in vitro release profile that was obtained for the manufactured formulation with the same composition revealed that 32.6%, 43.6%, 60.7%, 74.3%, 85.1% and 96.2% of salbutamol sulfate were released at the same time points. The in vitro dissolution profile that was generated using the simulation function was compared using the 2f similarity factor, which indicated that the profiles were indeed similar because a value of 94.6 was calculated. A function for simulating the ®8 ER . A summary of the proposed formulation composition is listed in in vitro release data and the 2f similarity factor for that hypothetical formulation. The brute force method was used to predict the composition of a hydrophilic matrix formulation with the desirable release characteristics similar to the reference formulation, Asthalinin vitro dissolution profile generated for the optimized formulation following manufacture is shown in comparison with the predicted and reference formulations in The ®8 ER  tablets. The 2f similarity factor was calculated to be 86.0. The correlation between the manufactured formulation derived from the optimization procedure using ANN and that for the predicted formulation is shown in It is evident that there are similarities between the dissolution profiles of the manufactured formulation and that of Asthalin® R2008a  was used to write code for the training and evaluation of a neural network. Formulation variables and in vitro dissolution profiles from a central composite study were used for training, testing and validating the network models that were developed. MatlabThe efficiency of the network was dependent on the number of nodes in the hidden layer and the numbers of nodes were tested by means of a trial and error approach using between three and ten neurons. The optimal number of nodes that produced a good predictive model was nine. Once the number of nodes had been established, the data was once again used to train a network that was then applied to the optimization of a sustained release matrix formulation for salbutamol sulfate. 2f similarity factor was used to establish whether formulation optimization had been successful by comparing ANN predicted dissolution profiles to that generated from the reference formulation, Asthalin®8 ER . A brute force method was applied to generate permutations for simulation into the model and the combination of formulation variables that resulted in the highest 2f value was selected. The resultant model formulation was then manufactured using a wet granulation procedure. The resultant formulation was found to perform satisfactorily and an 2f value of 86.0 was calculated for the comparison, clearly indicating that the dissolution profiles were similar. These results demonstrate the potential utility of ANN models for formulation development and optimization. Defining dosage form performance criteria from the outset is vitally important in developing a model formulation with the desired physical and quality characteristics. The


Out-of-hospital cardiac arrest (OHCA) is a frequent and acute medical condition that requires immediate care. We estimate survival rates from OHCA in the area of Stockholm, through developing an analytical tool for evaluating Emergency Medical Services (EMS) system design changes. The study also is an attempt to validate the proposed model used to generate the outcome measures for the study.This was done by combining a geographic information systems (GIS) simulation of driving times with register data on survival rates. The emergency resources comprised ambulance alone and ambulance plus fire services. The simulation model predicted a baseline survival rate of 3.9 per cent, and reducing the ambulance response time by one minute increased survival to 4.6 per cent. Adding the fire services as first responders  increased survival to 6.2 per cent from the baseline level. The model predictions were validated using empirical data.We have presented an analytical tool that easily can be generalized to other regions or countries. The model can be used to predict outcomes of cardiac arrest prior to investment in EMS design changes that affect the alarm process, e.g. (1) static changes such as trimming the emergency call handling time or (2) dynamic changes such as location of emergency resources or which resources should carry a defibrillator. Out-of-hospital cardiac arrest (OHCA) is a frequent and acute medical condition that requires immediate care ,2. ImmedRational decisions about the organisation cannot be made without qualified estimations of the outcomes resulting from a specific intervention. In the present paper, we present an analytical tool for evaluating EMS system design changes and their impact on cardiac arrest outcomes. We evaluate marginal changes in the response time  in the County of Stockholm, Sweden. Both extended and shortened response times were included as well as two alternative organisation forms of EMS defibrillation . The latter form was useful for analysing the potential of alternative first-responders, which has been shown to improve the survival rate among patients with OHCA .To estimate the survival rates, we use geographic information systems (GIS) simulation and combine it with register data on survival rates. GIS has previously been applied to locate potential areas of high and low rates of survival from OHCA ,8, as weWe had a unique opportunity to validate the simulation results compared to published empirical data. As part of the Saving Lives in the Stockholm Area (SALSA) project, all fire stations in the County of Stockholm were first provided with automated external defibrillators (AEDs) and fire and rescue vehicles were then dispatched in parallel with ambulances to all OHCA cases . The outWe used a general model to calculate the number of surviving OHCA patients in a specific region, pre-determined by the EMS system design and response strategies. ‘Surviving’ was defined as being alive 30 days after the OHCA, which in 94 per cent of the cases implies a successful neurological functioning for the survivors  . As the wherei who suffer an OHCA and who can be saved by emergency resource j is given by1. The number of individuals in region i = annual incidence of OHCA in region i  comprise either ambulance alone or ambulance plus fire services.In our case the region is the County of Stockholm and the emergency resources (j in region i is given by a function of the time (t) from OHCA to defibrillation, i.e.2. The survival rate of patients resuscitated by emergency resource wherej in region i is given by3. The number of patients surviving as a result of emergency resource N = total number of time periods (minutes)i,t = population in region i reached by emergency resource j at time tbi = total population in region iPOPi,t = probability in region i that a patient has ventricular fibrillation (VF) at time tVFi,t = probability in region i that a patient having VF survives at time tsFinally, to obtain the marginal effects of an intervention, the procedure was repeated for the relevant alternatives and the difference in survival rates could thus be established.The geographic region chosen was the County of Stockholm, where the total population on 31 December 2007 was 1 949 516 (Statistics Sweden). In 2006 the incidence rate of patients with OHCA, where some type of resuscitation measure was started, was 816 and the probability that OHCA occurred after ambulance personnel arrival was 15 per cent .The first step of our analytic model was to establish the gain in time from an OHCA to defibrillation achieved by dispatching fire services in parallel to ambulances. By using a geographic accessibility platform, PinPoint Sweden (PiPoS), developed by the Swedish Agency for Growth Policy Analysis, we arrived at the share of the population reached per minute by (1) ambulance, (2) fire services and (3) ambulance plus fire services. The simulation was performed by measuring the drive time plus the station reaction time from ambulance/fire stations to each person’s home in the County of Stockholm on 31 December 2007. The speed limits of the road network were assumed to be an approximation of the speed of the emergency vehicles. In localities, speed was assumed to be reduced by 20 per cent due to e.g. traffic congestion. ‘A locality consists of a group of buildings normally not more than 200 metres apart from each other, and must fulfil a minimum criterion of having at least 200 inhabitants’ . Only thThe station reaction time, i.e. the time from when the emergency services are alerted until the first vehicle departs, was estimated for ambulances but we have data for that time interval for fire services. The time was set to 90 seconds for all ambulance stations. Normally there is no stipulated station reaction time. Instead a ‘priority 1’ (highest priority) alarm is supposed to be responded to ‘immediately’. However, in practice and to achieve comparability with the fire services, 90 seconds is a good approximation and there is no reason to expect fire services and ambulance to have significantly different reaction times . For the fire services the station reaction time was more complicated, since there are both full-time and part-time stations. Full-time fire-fighters usually had a station reaction time of 90 seconds, while the time was longer and varied for part-time stations. Fortunately, the Swedish Rescue Services Agency had data on each fire station’s reaction time and these were included in the simulation.To be able to perform the simulation, we also needed to assume a specific location where OHCAs occur. Most OHCAs, 65–74 per cent, occur in the person’s home ,15,16. WThe GIS simulation only displayed part of the alarm process. In addition to the station reaction time and the driving time to incident , which were simulated, we had to account for the process before and after this period of time in the case of an OHCA . With help from EMS experts in the SALSA project, we approximated both the emergency call handling time and the preparation time to be 1.5 minute on average. Now, summarising the total time from OHCA to defibrillation yields 2+1.5+GIS simulation time+1.5 minutes = 5 minutes plus GIS simulation time. Since the minimum station reaction time is 1.5 minutes, the shortest time from OHCA to defibrillation would be 5+1.5=6.5 minutes when driving time to incident is shorter than 1 minute. This implies that any cardiac arrest could not have treatment in < 6.5 minutes in the model.Time is extremely important for successful treatment of OHCA and we used data from SCAR to estimate the probability of survival depending on the length of time from OHCA to defibrillation. A number of other factors also influenced the outcome, e.g. whether the OHCA was witnessed by a bystander, whether CPR was started prior to arrival of emergency services and the severity of the arrhythmia recorded by the first emergency responder . These fFigure Figure Based on the results from the simulations, we estimated the number of surviving patients (β). The baseline level of survivors per year was 26 (ambulance), or 3.9 per cent of the total number of OHCA patients. Engaging the fire services as first responders as well resulted in 42 survivors per year, implying that the number of additional lives saved by parallel dispatch of emergency resources was 16 per year. Changing the time from incidence to defibrillation affected the number of survivors Figure . E.g. shWith all due respect for models, we were fortunate to be able to compare the modelled results of parallel dispatch with the ‘real’ outcome of the ‘Saving Lives in the Stockholm Area’ (SALSA) project. The results and a detailed description of the project are presented in Hollenberg et al. . As partThe effects of the project were measured and evaluated during a pilot period from 1 December 2005 to 31 December 2006. A total of 863 patients with OHCA, where some type of resuscitation measure was started, were included and the proportion of patients alive after 1 month increased significantly from 4.4 to 6.8 per cent. Since the incidence of OHCA patients in the County of Stockholm was approximately 650–700 in 2006, the estimated number of additional lives saved by the project was 16 per year.The analytic model predicted that 26 patients would survive when only an ambulance was dispatched and that 42 patients would survive when ambulance plus fire services were dispatched (baseline level). Thus, the simulation tells us that 16 extra patients would survive through an intervention of dual dispatch, which is also what ‘reality’ tells us. We believe that the compliance of the analytic model with the results of the ‘real’ situation (SALSA) validates our model, although there were a number of uncertainty factors in both cases.A historical control of the baseline level of survival was also possible. The model predicted that approximately 3.9 per cent of the OHCA patients would survive (ambulance EMS only). Hollenberg et al.  estimateThe SALSA project has continued its dispatch of fire and rescue vehicles with ambulances. It has also expanded the first responders to include all police vehicles (approximately 120 more defibrillators). Unpublished results indicate that survival from OHCA has further increased since the pilot period.We have presented a model that combines geographic information systems (GIS) simulations of the lengths of emergency services times from an out-of-hospital cardiac arrest (OHCA) incident to defibrillation with data on survival rates from the Swedish Cardiac Arrest Register (SCAR). Simulations of ambulance alone as well as ambulance plus fire services were utilised as emergency resources. The results can be used to support rational planning processes regarding interventions that affect the alarm process for OHCAs, e.g. by using economic evaluations . When inImplementation of policies, e.g. changes in regions or dynamic changes such as location of emergency resources or which resources should carry a defibrillator, requires new GIS simulations of the type we showed for dual dispatch. Trimming of the static time intervals in the alarm process  is important and can be directly impacted by e.g. public education  and optimizing call handling . Marginal effects of changes in these intervals on survival rates can be evaluated directly by our model.The model has several limitations that could potentially bias the results. Among other things, it was assumed that all OHCAs occur in the patients’ homes and that the risk is identical in all homes, despite the fact that OHCAs have been shown to have definite time-geographic distribution patterns . E.g. coAlso, although a flat rate reduction of the driving speed in localities was included in the model, we are uncertain whether it correctly captures variations in the driving speed of emergency vehicles. Traffic congestion, road works and the choices of route may be factors that complicate the simulation. The model does not take vertical distances, e.g. floors above street level, into account, which naturally leads to further delays in arrival at patients’ side. Actual call-to-on-scene times for real OHCA events could be used to validate the speed limit based numbers as well as the assumption that the response teams are always in their stations.Moreover, there are uncertainties regarding the estimations of the static time intervals, e.g. the emergency call handling time. In our model the emergency call handling time is estimated to be 1.5 minutes on average, yet according to SOS Alarm AB, the company responsible for handling 112 emergency calls and coordinating rescue work, it might be as long as 4 minutes on average for ‘priority 1’ ambulance calls . If so, there certainly exists potential to improve this time interval. Using SOS Alarm AB’s figure of 4 minutes, the simulation model yields the baseline numbers of survivors of 15 (ambulance) and 24 (ambulance plus fire services); the baseline level of survivors is as low as 2.2% and the additional lives saved through dual dispatch is 9 instead of 16 per year. One minute shorter emergency call handling time would yield a large benefit to the OHCA population, +3 survivors (ambulance) and +5 survivors (ambulance plus fire services).It is clear that a sensitivity analysis of the results is necessary to provide a good basis for decisions. One further opportunity for improvement is to address variability in the model. We used point estimates for all components of the model. Variability around each of these estimates can have a compounding effect and ultimately affect the accuracy of the model. Assessment of the effects of errors in assumptions for values of variables used in the model should be addressed in some way, either internalized in the model or as a sensitivity analysis of the outcome e.g. .With the limitations, the simulation results for dual dispatch comply well with the results from a ‘real life’ intervention. In addition, Pell et al. (2001) estimated that a reduced ambulance response time from arrival at the scene within 14 minutes in 90 per cent of all emergency calls to arrival within 8 minutes would increase the survival rate from 6 to 8 per cent; reducing the response time to 5 minutes would increase the survival rate to 10–11 per cent [r (number of patients) and s  we may calculate effects of other changes as well, e.g. the effect of earlier CPR or the effect of faster response to other diagnoses than OHCA. Before analysing the impact of the EMS design changes on other diagnoses, we need to establish a relation between survival rates as a function of time depending on the intervention considered.Although the analytic model was calibrated for the County of Stockholm, it can be generalised to other Swedish counties or regions, as well as other countries. The data that is needed to calculate the number of patients surviving as a result of a specific EMS design change in a region is very general and easily available (see Section 2). By adjusting the model specification for The geographic location of defibrillators is possibly the most interesting factor to elaborate on and it also affects the marginal benefits of static response time changes, e.g. the increase in number of survivors was larger after introducing dual dispatch. When we consider geographic placement, the model is useful for analysing the effect of defibrillator placement in emergency vehicles . One natural step to further increase OHCA survival would be to introduce public access defibrillation (PAD), i.e. to place AEDs in the community for use by laypersons. The analytic model does not apply to this setting with stationary AEDs. However, although evaluations of PAD trials have shown a positive impact of survival, the cost-effectiveness of these programs remains uncertain. This is due to the large resources involved and the limited potential to save lives (less than 5 percent of OHCA occur in large public buildings) -23.In summary, despite the complexity of modelling an intervention of this type, we believe that the results at hand are useful for deploying effective EMS system design strategies. The possibility of testing where defibrillators should be placed geographically is deemed particularly useful and focuses on first responder defibrillation, which has potential to have an effect in private homes where the vast majority of OHCAs happen . ExtensiThe author declare that he have no competing interest.


In this paper we use a simple analysis based on properties of the axial field generated by symmetrical multipoles to reveal all possible distributions of two coaxial pairs of circular windings, which result in systems featuring zero octupole and 32 pole magnetic moments (six-order systems). Homogeneity of magnetic field of selected systems is analyzed. It has been found that one of the derived systems generates homogenous magnetic field whose volume is comparable to that yielded by the eight-order system. The influence of the current distribution and the windings placement on the field homogeneity is considered. The table, graphs and equations given in the paper facilitate the choice of the most appropriate design for a given problem. The systems presented may find applications in low field electron paramagnetic resonance imaging, some functional f-MRI (nuclear magnetic resonance imaging) and bioelectromagnetic experiments requiring the access to the working space from all directions. Magnetic fields of a specific spatial distribution are used in many areas of physics and technology and analytic approaches to the problem of designing systems, which are capable of generating the desirable fields may be found in the comprehensive surveys papers –4 and liIn many applications it is required that the field be highly homogenous over some specified volume. This is of particular importance in magnetic resonance imaging experiments. The systems used for in vivo medical diagnostic studies most often employ solenoidal superconducting electromagnets that are expensive and in certain applications pose disadvantages associated with the limited access to the region of uniform field. In electron paramagnetic resonance imaging (EPRI)  and in sIn this paper, we consider the system consisting of two coaxial pairs of circular loops with the same radius. The use of properly distributed windings of the same radius makes the radial access to the uniform field possible and does not impose restriction on the axial access, which may sometime occur in systems based on spherical harmonic expansion , 10 withWe use a simple analysis based on properties of the axial magnetic field with the aim to reveal the possible distributions of windings, that result in systems featuring zero octupole and 32 magnetic moments, i.e., generating the central magnetic field in which the sixth-order term is the first one non-vanishing in the field expansion. The table, formulae, and graphs given in the paper facilitate the choice of design, which is the most suitable for the problem at hand.The system presented generates extended volume of uniform magnetic field, which can be accessed from all directions. It may be suitable for very-low field MRI and EPRI as well as bioelectromagnetic experiments . The higz-axis, be located symmetrically with respect to the origin, and separated by the distance Consider a system of two circular current loops of the same radius Let us now focus the attention on how the axial field behaves outside the coil system and in its central region. To show the behavior of the distant axial mentclass2pt{minimIt is seen the distant axial field may be viewed as the superposition of magnetic multipoles at the system origin. Due to symmetry of current distribution all coefficients The first coefficients are found to be:The first term in the field expansion entclass1pt{minimarom Eqs.  that theBy expanding in the Taylor series the function in square brackets of Eq.  around tBut for the Helmholtz condition /Bcenter plotted at ±1, ±5, and ±10 ppm intervals for the ideal eight-order Lee-Whiting design and our 9/4 system, respectively. It is seen that that the field error contours of both systems compare excellently and the volume of the homogenous magnetic field is similar.Hereafter, we consider in more detail the In Ref.  the Amp–In Table NI)2/(NI)1 depends solely on the number of windings of the outer and inner coils. As an example of such assemblies, we have analyzed the arrangements comprising nine circular current loops in the outer coils and four loops in the inner coils. The loops are deposited side by side around z coordinates corresponding to the Lee-Whiting and our 9/4 contracted designs. The expected distributions of the magnetic field generated by the two designs are shown in Fig. The real assemblies with coils connected in series consist of many turns of wire fed with equal current and their Amp–turn ratio (R = 1 m) and fed with the current 1 A generates magnetic field whose value in the center is 89.5 × 10−7 T (89.5 mG). The field homogeneous to 50 ppm, which meets, e.g., EPRI requirements, extends axially to 0.22R and radially to 0.3R from the center. In the case of setup that uses the Lee-Whiting coordinates, the field value is 89.9 × 10−7 T and the field homogeneous to 50 ppm extends axially to 0.11R and radially to 0.17R from the center.The setup based on the 9/4 contracted system having the unit radius  field of the two-pair coil system may be easily shielded. To show the behavior of the distant axial The first three turns in the expansion are readily found to be those for So, we see that the external (stray) field of the system is dominated by the dipolar term and decreases as  by Eqs. , 13c canFor example, if the outer system has twice the radius of the inner one, we need the current flowing in it to be four times smaller than that in the inner system to achieve cancellation. Adding the outer system cancels the total dipole moment not effecting the homogeneity of the central field, but slightly reducing the field value. For the case considered it amounts to 12.5 %.The behavior of the stray field is shown in Fig. It is clear that the confinement provides a simple system with the reduced stray field, which in some circumstances may prove to be useful.R−1, hence systems of smaller radius and/or carrying higher currents will generate higher magnetic fields. Yet, to avoid heating problems they will require thermal and current stabilization. This refers particularly to long-time experiments. Based on the design presented, we plan to build 90 G magnet for micro-EPRI experiments, which should shed some light on the problems associated with practical implementation of the system. Finally, we would like to add that the high-field version of the system can be easily shielded by confinement of the system inside another one with bigger radius. This exerts no significant influence on the center field, but reduces greatly the stray field outside.The possible distributions of two coaxial pairs of circular windings resulting in systems featuring zero octupole and 32 pole moments are given. Analysis given in the paper shows that one of the derived system, which has the Amp–turn ratio of 9/4, generates homogeneous magnetic field whose volume compares excellently with that of the eight-order Lee-Whiting design, but is less sensitive to the impact of current distribution and the precision of windings location. Moreover, the system may be more easily adopted for the serial power supply. The ideal 9/4 system of the unit radius fed with 1 A current generates the magnetic field of 89.5 mG, which is about 10 times stronger than that yielded by the Helmholtz pair. The field is proportional to the number of Amp–turns and scales with


When they possess, but have not yet consumed information, people may suffer from the appearance of wastefulness. This apparent wastefulness may provide a potential explanation for the observed violation of the permanence feature. Our findings indicate that, as the material age evolves into the information age, information-exchange behavior has not evolved significantly away from material-exchange behavior.One of the basic features of information is permanence. This feature states that the consumption of information by one consumer does not affect the availability of that information to other consumers. We present examples in two studies indicating that both laymen and experts of information exchange may be motivated to make choices that violate the permanence feature in  But if you have an idea and I have an idea, and we exchange ideas, we each now have two ideas.”––George Bernard Shaw (1856–1950)dosa or lassis (foods) would have been reasonable but refusing to read a magazine offered to occupy time while on a train was irrational. It is not surprising that Ivy was puzzled. A retrospective evaluation of such a decision reveals that the rejection of Ivy’s offer runs counter to the most distinguishing and frequently referred to feature of information consumption: that information is essentially permanent.We will begin with a personal experience of the author of this manuscript (Shu Li) that made him aware of the concept of information permanence many years ago. When a group of Final-Year-Project students were traveling on Singapore’s MRT (Mass Rapid Transit), a student, Ivy, offered to lend her friend a recently published fashion magazine to kill time but was rejected with a statement to the effect that the potential recipient had subscribed to the magazine and the issue would arrive that same day. Ivy later insisted that something must be wrong - she thought refusing to eat offered e.g., food and drink).Ivy’s intuition that her friend’s refusal was irrational coincides with the permanence feature in information consumption. This concept states that the consumption of information by one consumer does not affect the availability of that information for use by other potential users lah,” implies an “as if” explanation, i.e., “if I read the offered magazine to kill time, what will I do with the magazine I subscribed to?” In explaining why people make choices that compromise their own self-interest, Arkes (1996) suggested that people may eschew a behavior that is in their own best interest to avoid the appearance of wastefulness What is likely to be responsible for the observed violation of the permanence feature in information consumption? The words of Ivy’s friend might give some clue as to the reason for this violation. The statement, “I have subscribed to this magazine, and this issue will arrive today The goal of this study was to investigate whether the permanence feature would be violated in a situation similar to the one demonstrated by Ivy and her friend.Why should we treat ideas differently from cake? Presumably we should do so because we cannot eat a piece of cake and have it too, but we can share an idea and still have it. The first experiment in Study 1 was designed to test whether people would accept offered information differently from an offered material good, as predicted by the permanence feature.goods-type  and goods-possession . The questionnaires presented to the participants were composed of four hypothetical scenarios, which were presented with the order balanced across the subjects. The scenarios, which we call the Accepting Problem, read as follows, with the only differences between the scenarios in parentheses:Two hundred and nineteen graduate students  at the Graduate University of the Chinese Academy of Sciences were recruited to fill out a short questionnaire in exchange for a small gift. A two-factor within-subject design was employed with the factors being Suppose you were going on a group tour on which you did not know any of the others in the group. On the trip, one group member spontaneously offered (you a chance to view a DVD of a movie you have never viewed/you a roll of film you have not brought) valued at ¥150. Would you accept the offer if you (had/had never) purchased one?Following each scenario, participants were instructed to rate their willingness to accept or reject the stranger’s offer on a 4-point scale from 1 (definitely willing to accept) to 4 (definitely willing to reject). The study was approved by the Institutional Review Board of the Institute of Psychology, the Chinese Academy of Sciences. Because the protocol was judged to pose low risk and the data were analyzed anonymously, oral consent was recommended and obtained from study participants before data collection.F = 40.823, p<.001), a significant goods-possession effect  = 81.628, p<.001) and a significant interaction effect, F = 79.970, p<.001. A simple effect analysis of the interaction showed that people were consistently willing to reject the offered roll of film regardless of whether they had possessed it (M = 3.12) or not (M = 3.10), F = .116, n.s. In contrast, people were more willing to accept the offered DVD they had never possessed (M = 2.22) but prone to reject if they already had it (M = 3.18), F = 132.804, p<.001.The results are summarized in a piece of cake, which has been aptly described as something that you cannot have and eat, too. When faced with an offer of information, however, people were more reluctant to accept the information if they already possessed it, but had not yet consumed it than if they did not possess it. The DVD used in our questionnaire was treated exactly the same as an idea  when our participants had not possessed it but somewhat like a piece of cake when our participants had possessed but not yet consumed it. The latter case constitutes an “Ivy-type” violation of the permanence feature in the sense that the availability of a piece of information was affected by the consumption of that information.The results of Study 1a showed that people treated information and material items offered by others differently. When faced with a material offering, the participants were equally reluctant to accept the material regardless of whether they had possessed the item or not. The film used in our questionnaire was treated exactly the same as Experts have been shown to make more accurate and reliable judgments than novices in a variety of fields Study 1b was therefore designed with a two-fold goal. The first was to examine whether the violation of the permanence feature would be absent from an expert sample. The second was to examine whether the observed violation could be explained by the “psychology of waste” Following the same procedure used in Study 1a, data were collected from one hundred full-time employees  of the State Intellectual Property Office of the People’s Republic of China (SIPO), who were well-trained in the field of information exchange and thus presumably more sensitive to the permanence feature. In order to test the “Psychology of Waste” explanation, all respondents were asked to indicate which of the following best described their rationale for accepting or rejecting the stranger’s offer:The offered product could be re-used by the provider if I used it.The offered product could not be re-used by the provider if I used it.It would be a waste of my previously purchased product.Other (Please write down your reason in the space provided)The study was approved by the Institutional Review Board of the Institute of Psychology, the Chinese Academy of Sciences. Because the protocol was judged to pose low risk and the data were analyzed anonymously, oral consent was recommended and obtained from study participants before data collection.F = 143.072, p<.001), a significant goods-possession effect  = 30.675, p<.001) and a significant interaction effect, F = 50.911, p<.001.The mean rating of the willingness to accept or reject the stranger’s offer is shown in F = 2.458, n.s.) and that no significant interaction occurred between the three factors  = 1.561, n.s.). Apparently the permanence feature in information consumption was not considered even when the scenarios were posed to professionals who worked at the State Intellectual Property Office (SIPO).Combining the data from the expert sample with the data from the student sample, a three-factor  analysis revealed that no significant difference existed between the student sample and the expert sample  would no longer take into account that “the offered product could be re-used by the provider if I used it”, but instead thought that it was “a waste of my previously purchased product”. The emergence of waste as a consideration may provide a possible explanation as to why people were prone to reject the information-goods they already possessed.The result of Studies 1a and 1b consistently showed that the permanence feature in information consumption was obeyed when participants did not already possess the offered information but was violated when participants already possessed the offered information. The “psychology of waste” appears to explain this observed violation of the permanence feature.In Study 1c, we went one step further in an attempt to determine whether people made choices that were contrary to the permanence feature through real-life responses rather than through responses to hypothetical scenarios. The rationale behind this experiment was to test the idea that if there was motivation to avoid the appearance of wastefulness, people might be reluctant to utilize an identical piece of information that was available before utilizing a piece of information they already possessed.Forty-three graduate students  at the Graduate University of the Chinese Academy of Sciences participated in this experiment.Reader) and were asked to choose one to be mailed to their postal address. The selected issue was then put into an envelope and addressed by the participant.The participants were recruited to participate in an unrelated experiment on brand recognition. Before participating in the brand experiment, they were led to separate breakout rooms upon arrival and told that they would be given a magazine for participating in the brand experiment. Participants were offered two back issues of a very popular Chinese magazine . However, the test showed that they were overwhelmingly willing to kill time by reading the magazine that would NOT be sent to them (p<.001). This latter finding indicates that the participants were motivated to kill time by reading a free magazine.Of the 43 participants observed, 79.1 percent (34) chose NOT to read the magazine that would be sent to them, while 20.9 percent (9) chose to read the magazine that would be sent to them. Of the 34 participants who chose not to read the chosen magazine, 97.1 percent (33) chose to read the magazine that would NOT be sent to them, while 2.9 percent (only 1 person) consistently chose not to read either magazine. A Chi-square test showed that participants were more reluctant to read the magazine that they selected to be sent to them  strengthens the idea that the permanence feature is considered in the absence of psychological waste but violated in the presence of it.The participants’ actual responses  at the Graduate University of the Chinese Academy of Sciences were recruited to fill out a short questionnaire in exchange for a small gift. The scenarios, which we call the Suppose you were going on a group tour on which you did not know any of the others in the group. You carried with you (a DVD of a movie/a roll of film) valued at ¥150. On the trip, you found one group member that had never viewed the movie/had not brought the same kind of film. Would you offer the group member (the DVD/the film) if he/she (had previously/had never) purchased one?Following each scenario, the participants were instructed to rate their willingness to offer the stranger the DVD/film on a 4-point scale from 1 (definitely willing to offer) to 4 (definitely unwilling to offer). The study was approved by the Institutional Review Board of the Institute of Psychology, the Chinese Academy of Sciences. Because the protocol was judged to pose low risk and the data were analyzed anonymously, oral consent was recommended and obtained from study participants before data collection.F = 2.412, n.s.) on the rating of willingness to provide a stranger with an offer, but a significant goods-type effect  = 138.103, p<.001), and a significant interaction effect, F = 10.476, p<.001. A simple effect analysis of the interaction showed that people were consistently unwilling to offer a roll of film regardless of whether the stranger already possessed one (M = 2.89) or not (M = 2.96)  = 1.067, n.s.). In contrast, people were more willing to offer a DVD that the stranger did not possess (M = 1.63), but less willing to offer one if the stranger already possessed it (M = 1.88),  = 8.688, p<.01). Therefore, a violation of the permanence feature was also observed from the perspective of offering information.Following the same logic as Study 1b, an expert sample rather than a student sample was used to test whether experts of information exchange are more immune to the violation.The material and procedure in Study 2b were exactly the same as Study 2a except that a test of the “Psychology of Waste” was added. Participants were asked to indicate which of the following best described their reasons for offering or not offering the DVD/film:The offered product could be re-used by me if the recipient used it.The offered product could not be re-used by me if the recipient used it.It would be a waste of the recipient’s previously purchased product.Other (Please write down your reason in the space provided.).The data were collected from 123 full-time employees  at SIPO. The study was approved by the Institutional Review Board of the Institute of Psychology, the Chinese Academy of Sciences. Because the protocol was judged to pose low risk and the data were analyzed anonymously, oral consent was recommended and obtained from study participants before data collection.F = 30.347, p<.001).A two-factor, repeated-measures ANOVA of the experts’ rating of willingness to offer information/material to a stranger  revealedF = 33.002, p<.001), together with a significant interaction effect between the three factors  = 11.367, p<.05). Most interestingly, a simple effect analysis of the interaction showed that the experts of information exchange were more unwilling to offer information than were the students, especially in the “possessed but not consumed” condition  = 51.164, p<.001). This finding suggests that the permanence feature was violated more severely when the problems were posed to professionals who were assumed to have a clearer understanding of the feature.Combining the data of the expert sample with that of the student sample, a three-factor  analysis revealed a significant difference between the student sample and the expert sample  at the Graduate University of the Chinese Academy of Sciences participated in this experiment. The participants were recruited to participate in an unrelated experiment on decision making. In our experiment, one actual participant and one undercover researcher were seated in a classroom. The real participant was offered two back issues of the same popular Chinese magazine as was used in Study 1c for participating in the decision making experiment. Meanwhile, the researcher was offered one of the two back issues in the presence of the actual participant and asked to put it into a self-addressed envelope to be sent by mail.While they were waiting for the upcoming decision making experiment, the actual participant was asked whether he/she would be willing to offer one of his/her two issues to the “bored” researcher to kill time. The participant’s decision about which issue to offer was recorded. The study was approved by the Institutional Review Board of the Institute of Psychology, the Chinese Academy of Sciences. Because the protocol was judged to pose low risk and the data were analyzed anonymously, oral consent was recommended and obtained from study participants before data collection.p<.05). Specifically, of the 21 participants in scenarios in which Issue 8 was to be mailed to the confederate researcher, 76.2 percent (16) chose to offer the other issue (Issue 9) to the “bored stranger”; whereas only 23.8 percent (5) chose to offer the same issue (Issue 8). Similarly, in the situation in which Issue 9 was to be mailed to the researcher, 71.4 percent (15) of the 21 participants chose to offer the other issue (Issue 8); whereas only 28.6 percent (6) chose to offer the same issue (Issue 9).The participants’ decisions are summarized in Study 2c provided behavioral evidence that considering the possible wastefulness of another may also lead to a violation of the permanence feature.Taken together, both the hypothetical and real decisions in Study 2 generally demonstrated that the violation of the permanence feature from the perspective of information offering was robust.An earlier study found that the subjective value of information follows the predictions of endowment effect theory if you have previously purchased it” or “if he/she had previously purchased one”) in both the Accepting Problem and the Offering Problem used in Studies 1 and 2 was presented to the participants. In order to counterbalance the order of scenario presentation, the problems were presented in two different versions . Following each of the two possessed-information scenarios, the participants were asked to indicate their willingness to accept/offer the DVD on a 4-point scale.Ninety-four full-time employees  at SIPO were recruited to fill out a short questionnaire. Only the already possessed DVD condition  to 7 (definitely agree):“My/his previously purchased DVD will be wasted if I/he viewed the offered one.”In addition, to test for the endowment effect, we also asked the respondents to indicate the extent of their agreement with the following points on a 7-point scale from 1 (definitely disagree) to 7 (definitely agree):For the Accepting Problem“The DVD that I previously purchased is more valuable than the one that the group member offered.”“The DVD that the group member offered is more valuable than the one that I had previously purchased.”For the Offering Problem“The group member would think that the DVD that they had previously purchased is more valuable than the one that I offered.”“The group member would think that the DVD that I offered is more valuable than the one that they had previously purchased.”A between-subject design was used in the present study. We randomly assigned the participants to one of the two versions. Of the 94 participants, 46 responded to the Accepting-Offering version and the remaining 48 responded to the Offering-Accepting version. The study was approved by the Institutional Review Board of the Institute of Psychology, the Chinese Academy of Sciences. Because the protocol was judged to pose low risk and the data were analyzed anonymously, oral consent was recommended and obtained from study participants before data collection.M = 3.32) to accept or reject the stranger’s DVD offer in the Accepting Problem and the mean rating of willingness (M = 2.94) to provide the stranger a DVD in the Offering Problem revealed similar results  to those obtained in Studies 1 and 2.Analyses of the mean rating of willingness (“My purchased DVD will be wasted if I viewed the offered one” in the Accepting Problem, but disagreed with the point, “The DVD that I previously purchased is more valuable than the one that the group member offered,” and the point “The DVD that the group member offered is more valuable than the one that I previously purchased.” A similar result was obtained in the Offering Problem (see As shown in blem see . Study 3e.g., During the long history of evolution, people have become accustomed to living in a world designed for material exchange. Although society has evolved into an information age, the characteristics associated with materials remain and are part of our daily lives. In this study, we found that the “psychology of waste” “Waste not” is a rule that most of us have used in material-exchange behavior It should be noted that in the present study limit options were provided to explain the decisions for acceptance or offering. In Studies 1b and 2b, some participants selected the “other” option, indicating there were other reasons behind their decisions. As the aim of the present paper was to test whether the psychology of waste could provide a possible explanation, we did not further explore other reasons behind participants’ decisions. However, future research should explore other possibilities in greater detail.Our findings, on one hand, imply that the impermanence feature of material consumption is deeply rooted in the human mind. On the other hand, it suggests that the permanence feature in information consumption is not as appealing as would be expected. In exploring the nature of material goods, we may eventually detect violations other than the “Ivy-type”. We believe that further interpretation of the results as well as further research is called for before making conclusions about the imperishable nature of information.Business Software Alliance shows that the worldwide commercial value of unlicensed software hit $51.4 billion and the piracy rate reached 43% in 2009 Every year, the governments of various countries, developed and developing alike, exert efforts to end piracy. For example, data from the


In critical care units most of the patients’ physiological parameters are sensed by commercial monitoring devices. These devices can also supervise whether the values of the parameters lie within a pre-established range set by the clinician. The automation of the sensing and supervision tasks has discharged the healthcare staff of a considerable workload and avoids human errors, which are common in repetitive and monotonous tasks. Urine output is very likely the most relevant physiological parameter that has yet to be sensed or supervised automatically. This paper presents a low cost patent-pending device capable of sensing and supervising urine output. The device uses reed switches activated by a magnetic float in order to measure the amount of urine collected in two containers which are arranged in cascade. When either of the containers fills, it is emptied automatically using a siphon mechanism and urine begins to collect again. An electronic unit sends the state of the reed switches via Bluetooth to a PC that calculates the urine output from this information and supervises the achievement of therapeutic goals. This provides clinicians with invaluable information for interpreting the patient’s state. In most cases, these devices can also supervise whether the values of the physiological parameters they sense remain within a pre-established range set by the clinician. This range represents the values considered as acceptable for each parameter. If a parameter does not fall within its acceptable range, audible warnings to alert the health care staff are generated ,2.These devices discharge the healthcare staff of a considerable workload, since they need not continuously supervise whether the physiological parameters of every patient lie within the acceptable range. They also avoid human errors, which are common in any repetitive and monotonous task such as the supervision of physiological parameters ,4.Arguably, the most relevant physiological parameter which is still measured and supervised manually by healthcare staff is urine output. Urine output is the best indicator of the state of the patient’s kidneys. If the kidneys are producing an adequate amount of urine it means that they are well perfused and oxygenated. Otherwise, it is a sign that the patient is suffering from some complication. Urine output is required for calculating the patient’s water balance, which is essential in the treatment of burn patients . FinallyTo measure urine output in critical care units, a Foley catheter is introduced through the patient’s urethra until it reaches his/her bladder. The other end of the catheter is connected to a graduated container that collects the urine. Periodically the nursing staff manually records the reading of the container of every patient, and operates a valve which releases the urine into a larger container. Thus, the healthcare staff does not benefit from the advantages of having a digital recording of this physiological parameter, or of the continuous and automatic supervision of its values.In critical care units of first world countries, measurements of every patient’s urine output are taken hourly, 24 times a day, 365 days a year. In the case of emerging countries, often only burn patients—for whom urine output monitoring is of paramount importance—have this parameter recorded every hour, while the remaining critical patients have it recorded every 2 or 3 hours. This disparity in criteria is due to the more reduced availability of healthcare staff in the critical care units of emerging countries.It can even be argued that the recording interval currently employed in first world countries—once every hour—is also a compromise between avoiding risk states for the patient and relieving the nursing staff from an excessive burden. A system capable of automatically monitoring urine output would decrease the workload associated with this task and, at the same time, permit supervision to take place on a more continuous basis.On the other hand, there are abundant studies in the medical literature showing that critical patients’ outcome improves when the number of nurses –10 and tFrom this point of view, the processes related to sensing and supervising urine output are a potential gold mine for saving time. Currently, in developed countries every hour a nurse must visit each of the patient’s bed of the critical care unit to manually record urine output. The nurse must put on gloves since he/she is going to manipulate body fluids, walk to the patient’s bed, take the measure visually, write it down, open the valve that releases urine from the graduated container to the plastic bag, wait for the urine to drain, close the valve, check that the valve is properly sealed and check if the plastic bag needs to be emptied. This procedure takes at least 2 minutes. In a critical care unit with 15 patients, this means 30 minutes per hour; 12 hours a day. Typically, nurses work in shifts of six hours. Therefore, each day two complete nursing shifts are required only for tasks related to supervising urine output. If these tasks were automated by a low cost device, we may obtain an improvement in patient outcomes equivalent to the improvement that would be obtained when two nurses per day are added to the staffing of the unit.This paper presents a patent-pending device  capable 2.The problem of automating urine output supervision has only begun to be tackled recently. There are several factors which probably have contributed to this. On the one hand, automatic urine output measurement requires a device capable of sensing very small amounts of liquid flow (as low as 3–5 milliliters per hour). This precludes the use of some common industrial solutions to measure the height of a column of liquid such as ultrasound sensors  or commeContact with the urine also means that the component is in indirect contact with the patient’s bladder through a Foley catheter. Therefore, the components that come into contact with the urine have to be sterilized before use. Finally, urine usually is acidic, although sometimes it can be slightly basic—its pH varies between 4.5 and 8. It contains Uric acid (between 25 and 75 mg/L), Urea (between 15 and 34g/24h), Sodium (between 130 and 260 mEq/24h), Potassium (less than 90 mEq/24h), Chlorine (between 110 and 250 mEq/24h), and Copper (less than 30 mcg/24h), among other components . Thus, i±25 mL). Urinfo 2000 cannot supervise therapeutic goals and its readings are not transmitted to a central station/PC. Thus it requires the nursing staff to take the measures from the device’s display, which is placed next to the patient’s bedside. At present, Medynamix has been bought by Flowsense, a construction services company that is starting to market the device  represents the possibility of C being precisely x. A fuzzy number  represents the core, core(C) = {x ∈ ℝC| π(x) = 1}, and ]α, β. A fuzzy constraint can be induced by an item of information such as “x has a high value”, where “high value” will be represented by Cπ=high. Given a precise number x ∈ ℝ, Cπ=high(x) ∈  represents the possibility of C being precisely x; i.e., the degree with which x fulfills the constraint induced by “high value”.A fuzzy number ml/kg · h. Our tool allows them to indicate the weight of the patient (P) and the therapeutic goals represented by the trapezoidal possibility distribution Uπ . If Uπ(iu) = 1 then the urine output is within the range of ideal values. If Uπ(iu) = 0 then either the urine output is less than the minimum acceptable value (the patient has oliguria or anuria), or greater than the maximum acceptable (the patient has polyuria). The closer Uπ(iu) is to 1, the closer the amount of urine produced by the patient is to the ideal value, and the closer Uπ(iu) is to 0, the closer the patient is to oliguria or polyuria.If Uπ(iu) is represented by a color code used when drawing the graph of urine output. Red represents the null compatibility (the patient is clearly in oliguria or in polyuria), followed by orange, yellow, green and black, which represents the total compatibility . Therefore, the urine output graph provides instant visual feedback on the patient’s state [In the tool ’s state .Uπ; i.e., when the patient clearly has oliguria. The time at which the alarm is triggered is given by:α is the beginning of the support of Uπ, P is the patient’s weight, V S is the effective volume of the small container, and SVσ is the standard deviation of the effective volume of the small container. The correction V S + 2 · SV σ is applied to avoid false positives caused when the small container does not release its contents when urine output reaches its average effective volume, but some more urine accumulates before releasing.The tool generates an audible warning when the small container is not filled within the maximum time allowed by Uπ; i.e., when the patient clearly has polyuria. This alarm is triggered when the small container fills before the time given by:δ is the end of the support of Uπ. The correction V S − 2 · SV σ is applied to avoid false positives caused when the small container releases its contents before reaching its effective volume.Similarly, the tool also produces an audible warning when the small container fills faster than the minimum time allowed by 6.The measurement accuracy of our device depends on the urine production rate of the patient. As ± 0.32 mL, with a confidence interval of 95% the measurement error will be under 10.9%. After taking m measures from the small container the expected value for the amount of liquid that has flowed is m · 5.82, and the standard deviation of the aggregate result of these measures is m = 2, the maximum error with a confidence interval of 95% will be 3.9%; already under 5%. As The maximum uncertainty in the measurements occurs when a single measure of the small container is available. Given that its effective volume is 5.82 These estimates do not take into account the error caused by the inability to measure urine production during the draining of the small container. This error grows with the number of measures taken from the small container. However, as One might think that the measurement error caused by the inability to measure the urine produced while the small container is drained could be corrected on a per small container evacuation basis rather than per large container evacuation. When the large container is evacuated once, we could estimate how much urine has flowed during these intervals, and use this estimate to correct future measures of the small container. However, the urine production rate of the patient  can vary rapidly, hence the interest in a continuous monitoring of urine output. Furthermore, in patient monitoring one should always lean towards the side of caution. A system which considers that the production of urine during the small container draining is the worst case scenario for the patient’s health (zero) it is preferable to a system that may be more accurate, but that under certain conditions may overestimate urine output, and therefore could lead clinicians to think the patient is in a better state than he/she really is. Hence we made the decision not to apply any correction.i.e., the graduated container is not tilted in any direction and the nurse’s eyes are in the same horizontal plane as the graduated container. In practice, the average visual measurements error has been reported to be as high as 26% [The measurement error of the device is very low when compared to errors committed by the nursing staff when they take measures visually. For example, in the commercial urine meter Unometer 500—the one used in the ICU of the hospital we collaborate with—the separations between the coarser measurement divisions in the 500 mL graduated container where urine is collected correspond with 20 mL. Therefore, the minimum measurement error is 4%. However, this is a highly optimistic estimation of the error. It assumes that the visual measurements are taken in ideal conditions; h as 26% .Our device provides feedback on the status of the patient’s kidneys more frequently than is currently available in critical care units –hourly. For example, for a patient of 80 kilos, which should produce at the very least 40 milliliters of urine per hour, our device could warn of a deviation from the therapeutic goals in approximately 9 minutes. This allows the clinician to react more promptly to complications in the state of the patient. Therefore, the device has the potential of improving patient outcomes.The complexity of our device is not greater than the complexity of commercial urine meters, which often require a small container embedded within a large container, valves that communicate both containers with each other and with a plastic bag, mechanisms to prevent the containers from overflowing, filtered openings to equalize pressures, etc. The materials required to build our prototype are also similar to the ones required for commercial urine meters, with the exception of the additional need of two reed switches, two floats and two magnets. The rest of the pieces that are part of our solution do not have to be discarded because they are not in contact with the patient’s urine, nor do they suffer significant degradation caused by its operation. Thus, their cost can be amortized over a long period of time and their impact on the overall cost of the solution is negligible. Therefore, the price of mass production of our device should be only slightly higher than the commercial urine meters price.An automatic supervision of the patient’s urine output can also improve patient outcomes indirectly by reducing the healthcare staff workload. As we argued in the introduction of the article, in a critical care unit of a developed country with 15 patients, approximately 12 hours per day are necessary for tasks related to supervising urine output. Typically, nurses work in shifts of six hours. Therefore, each day two complete nursing shifts are required for these tasks. Automating the supervision of urine could produce an improvement in patient outcomes similar to the improvement obtained if two nurses per day were added to the staffing of the unit ,9.In the contexts where the additional cost of the device could be a barrier to adoption, the institutions that provide healthcare services may opt for a staff reduction to offset the higher costs of the device. In this way, a more precise and continuous monitoring of urine output would be obtained without an increase in costs.7.We have presented a device capable of automatically sensing and supervising the urine output of critical care patients. The device comprises two containers of different volumes, a small one that receives the urine coming from the patient’s bladder, and a greater volume container in which the first container releases its content when it gets full. Both containers release their content automatically when they are filled using a siphon mechanism. The containers are equipped with reed switches that are activated by a magnet that is attached to a float located inside the containers. These reed switches allow us to identify the instants at which the containers get filled with urine. An electronic unit sends via Bluetooth the information provided by the reed switches to a PC which calculates the urine output from the switches’ state, and supervises the achievement of the therapeutic goals established by the clinician.The error in the measures of the device depends on the patient’s urine production. For most common scenarios, it is below 2%. This is significantly lower than the error committed by the nursing staff when they take measures visually. In addition, the device provides feedback on the status of the patient’s kidneys every 5 to 10 minutes (depending on the patient’s weight), while currently that information is available hourly. This early warning of deviations from the therapeutic goals could translate into improved patient outcomes.The cost of mass production of the device is slightly higher than the cost of the commercial urine meters currently in use. However, the device frees a considerable amount of time for the healthcare staff. The time saving can yield cost savings to the institutions that provide health services. Conversely, the saved time can be repurposed for more relevant tasks, and thus it can generate an improvement in patient outcomes.The overall result is a low cost device that permits both a more precise and more continuous monitoring of urine output than is currently possible in critical care units, and that therefore has the potential of improving patient outcomes. The next step is to take the device to the clinical routine. This will require a commercial partner capable of producing the device in the appropriate sterile conditions and distributing it to the hospitals.