Quantifying patient satisfaction with process metrics using a weighted bundle approach.

Background:Current patient satisfaction assessment results are delayed and obtained from select patient surveys. As a result, these assessments may not represent the experience of the entire patient population. This study developed a method to measure and evaluate all patients\u27 experiences while they are within the care episode and link it to processes within the organisation. Methods:Using the Six Sigma methodology, sites assembled diverse teams to categorise and analyse negative experience comments from patients to understand the drivers of dissatisfaction. These customer expectations lead to the development of the four components in the Patient Experience Bundle (PEB): communication, environment, basic needs/comfort and logistics. Individual process elements were ranked to create a numerical relationship between service and the needs expressed by the voice of the customer. Sites created surveys incorporating questions that focused on the bundle elements and measured daily bundle compliance. Graphical analysis and hypothesis testing enabled sites to determine key drivers of patient dissatisfaction within the bundle elements. Improvement strategies were developed and implemented to address the key drivers of patient dissatisfaction. Results:After implementing process improvements focused on issues identified by the PEB, bundle compliance improved from an average of 51% to an average of 82.5% and Press Ganey Likelihood to Recommend (PG LTR) scores improved from an average of 64.73% to an average 74.64%. The data demonstrated that the trends in improving PEB are followed by meaningful changes in PG LTR scores. Conclusion:This work is built on the identification of common elements of care that impact patient satisfaction and detailed mathematical analysis of the relationship between factors. Using the bundle concept, these improvement efforts maintain highly reliable processes to drive outcomes and provide real-time feedback on patient experience

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths

Thermoelectric power factor under strain-induced band-alignment in the half-Heuslers NbCoSn and TiCoSb

Band convergence is an effective strategy to improve the thermoelectric performance of complex bandstructure thermoelectric materials. Half-Heuslers are good candidates for band convergence studies because they have multiple bands near the valence bad edge that can be converged through various band engineering approaches providing power factor improvement opportunities. Theoretical calculations to identify the outcome of band convergence employ various approximations for the carrier scattering relaxation times (the most common being the constant relaxation time approximation) due to the high computational complexity involved in extracting them accurately. Here, we compare the outcome of strain-induced band convergence under two such scattering scenarios: i) the most commonly used constant relaxation time approximation and ii) energy dependent inter- and intra-valley scattering considerations for the half-Heuslers NbCoSn and TiCoSb. We show that the outcome of band convergence on the power factor depends on the carrier scattering assumptions, as well as the temperature. For both materials examined, band convergence improves the power factor. For NbCoSn, however, band convergence becomes more beneficial as temperature increases, under both scattering relaxation time assumptions. In the case of TiCoSb, on the other hand, constant relaxation time considerations also indicate that the relative power factor improvement increases with temperature, but under the energy dependent scattering time considerations, the relative improvement weakens with temperature. This indicates that the scattering details need to be accurately considered in band convergence studies to predict more accurate trends.Comment: 21 pages, 8 figures. arXiv admin note: text overlap with arXiv:1905.0795

Using technology to improve the management of development impacts on biodiversity

Funder: The research was funded through a long‐term collaboration between Conservational International and Chevron.Abstract: The mitigation hierarchy (MH) is a prominent tool to help businesses achieve no net loss or net gain outcomes for biodiversity. Technological innovations offer benefits for business biodiversity management, yet the range and continued evolution of technologies creates a complex landscape that can be difficult to navigate. Using literature review, online surveys, and semi‐structured interviews, we assess technologies that can improve application of the MH. We identify six categories (mobile survey, fixed survey, remote sensing, blockchain, data analysis, and enabling technologies) with high feasibility and/or relevance to (i) aid direct implementation of mitigation measures and (ii) enhance biodiversity surveys and monitoring, which feed into the design of interventions including avoidance and minimization measures. At the interface between development and biodiversity impacts, opportunities lie in businesses investing in technologies, capitalizing on synergies between technology groups, collaborating with conservation organizations to enhance institutional capacity, and developing practical solutions suited for widespread use

Shaping Methods to Accelerate Reinforcement Learning: From Easy to Challenging Tasks

Reinforcement learning (RL) is one of the most active research areas in artificial intelligence. In RL an agent tries to maximize the total amount of reward it receives while interacting with an environment. The reward is used to improve the policy. Conventional methods of reinforcement learning perform well for simple tasks, but as the task becomes more complex, these methods fail to converge fast or converge to a suboptimal policy. Hence, new methods of RL are needed that can handle complex tasks. As humans, we simplify a task that is difficult to learn by first learning simplified versions of the task, before moving back to the original task. This idea of starting from simpler tasks and gradually increasing complexity, until the original task is solved, can also be exploited in RL, where it is called shaping. In order to accelerate learning in the original task, shaping methods transfer the knowledge and experiences from the easy task (source task) to the original task (target task). It is believed that the process of gradually increasing the complexity significantly reduces the difficulty of the learning problem. However, sometimes the total required time to solve the easy task plus the original task is larger than starting from scratch. In order to reduce this time, an essential decision in shaping is when to transfer learning from an easier task to a more difficult one. Transferring too early may mean the controller has not learned enough in the easy task, diminishing the usefulness of shaping. Transferring too late could make the controller waste learning time in the easy task, without making significant progress toward solving the original, complex task. If we switch the task at a proper point then the total learning time will decrease. The first part of this thesis is devoted to a thorough empirical study of the shaping methods. In the next part we try to find a suitable performance index that can be used as a switching criterion to reduce the total time needed to learn a task.Delft Center for Systems and ControlMechanical, Maritime and Materials Engineerin

Efficacy of Sulfosulforon (Apyrus) and Metham Sodium (Vapam) Herbicides on Control of Broomrape (Orobanche aeygptiaca) in Tomato Fields

In order to evaluate the effect of Metham Sodium and Sulfosulforon dosages on broomrape control and tomato yield an experiment was conducted using randomized complete block design with 4 replications in Mashhad Iran. Treatments were Metham Sodium and Sulfosulforon at the rates of 26.5, 53, 79.5, and 106 gr.ha-1, at the rates of 400, 600.800, and 1000 kg.ha-1. The results showed that Metham Sodium was more effective than Sulfosulforon. The highest  dry matter, number of broomrape foliage and tomato yield were obtained by using 1000 and 800 kg. ha-1 of Metham Sodium. Sulfosulforon was applied as post emergence once in this experiment. It seems that because of this reason efficiency is less than that of  comparison with Metham Sodium. Thus pre and post multi – applications of the herbicide was suggested during cropping season

A machine learning approach to predict the S&P 500 absolute percent change

Abstract Models of the stock market often focus on predicting the direction of the stock market. Instead of following this approach, we created a model to predict the daily absolute percent change of the S&P 500. An accurate model of this metric would greatly increase profitability of option trading strategies such as straddles and iron condors. In this project, novel features were created based on historical data and fed to machine learning algorithms such as Decision Trees, Rule Based Classifiers, K-mean Clusters, and Kernels. Based on our findings, Decision Trees and Kernels showed an accuracy of 80% when predicting absolute percent change, while Rule Based Classifiers had an accuracy of 88%

Comparative effects of massage therapy and bandage on shoulder pain, edema and dysfunction after the modified radical mastectomy

Background: Mastectomic patients experience complications such as edema of the operated hand, shoulder pain and dysfunction. This study was conducted to compare the effects of manual lymph drainage and low pressure bandage on shoulder pain and dysfunction after modified radical mastectomy. Materials and Methods: In this clinical trial study, 90 women with radical mastectomy referred to the oncology ward of Imam Khomeini hospital (Tehran) were randomly assigned to three groups: the massage, massage+bandage and control groups. Groups were trained how to use manual lymph drainage massage and low pressure bandage. Shoulder pain intensity and dysfunction were measured at 7 and 30 days post-surgery. Edema was measured at 24 hours post-surgery and also 30 days post-intervention. Data were analyzed using descriptive and inferential statistics (one-way ANOVA and Kruskal-Wallis). Results: There was no statistically significant difference among three groups in the means of three measured variables (the arm circumference, shoulder pain intensity and dysfunction) before the intervention. Moreover, the results showed a significant reduction in the means of shoulder pain intensity and dysfunction among the three groups on the 30th intervention day (P=0.001), but the difference was not significant in the mean of arm circumference. Conclusion: Lymphatic drainage massage and low pressure bandage are effective in reducing post-mastectomy complications