Fixture-Based Design Similarity Measures for Variant Fixture Planning

One of the important activities in process planning is the design of fixtures to position, locate and secure the workpiece during operations such as machining, assembly and inspection. The proposed approach for variant fixture planning is an essential part of a hybrid process planning methodology.The aim is to retrieve, for a new product design, a useful fixture from a given set of existing designs and their fixtures. Thus, the variant approach exploits this existing knowledge. However, since calculating each fixture's feasibility and then determining the necessary modifications for infeasible fixtures would require too much effort, the approach searches quickly for the most promising fixtures based on a surrogate design similarity measure. Then, it evaluates the definitive usefulness metric for those promising fixtures and identifies the best one for the new design

Using Neural Networks to Generate Design Similarity Measures

This paper describes a neural network-based design similarity measure for a variant fixture planning approach. The goal is to retrieve, for a new product design, a useful fixture from a given set of existing designs and their fixtures. However, since calculating each fixture feasibility and then determining the necessary modifications for infeasible fixtures would require too much effort, the approach searches quickly for the most promising fixtures. The proposed approach uses a design similarity measure to find existing designs that are likely to have useful fixtures. The use of neural networks to generate design similarity measures is explored.This paper describes the back-propagation algorithm for network learning and highlights some of the implementation details involved. The neural network-based design similarity measure is compared against other measures that are based on a single design attribute

Spoilage Identification of Beef Using an Electronic Nose System

A commercially available Cyranose-320. conducting polymer-based electronic nose system was used to analyze the volatile organic compounds emanating from fresh beef strip loins (M. Longisimmus lumborum) stored at 4°C and 10°C. Two statistical techniques, i.e., linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), were used to develop classification models from the collected sensor signals. The performances of the developed models were validated by two different methods: leave-1-out cross-validation, and bootstrapping. The developed models classified meat samples based on the microbial population into “unspoiled” (microbial counts \u3c6.0 log10 cfu/g) and “spoiled” (microbial counts \u3e 6.0 log10 cfu/g). Overall, quadratic discriminant-based classification models performed better than linear discriminant analysis based models. For the meat samples stored at 10°C, the highest classification accuracies obtained by the LDA method with leave-1-out and bootstrapping validations were 87.10% and 85.87%, respectively. On the other hand, classification by QDA and subsequent validation by leave-1-out and bootstrapping provided highest accuracies of 87.5% and 97.38%, respectively. For samples stored at 4°C, the LDA method provided highest classification accuracies of 79.17% and 85.64% using leave-1-out and bootstrapping validation, respectively. When the QDA method was used, the highest classification accuracies obtained for the samples stored at 4°C were 87.50% and 98.48%, respectively, with leave-1-out and bootstrapping validations

Reliability and validity of an enhanced paper grip test; a simple clinical test for assessing lower limb strength

Background The paper-grip-test (PGT) involves pulling a small card from underneath the participant’s foot while asking them to grip with their hallux. The PGT is shown to be effective in detecting foot muscle-weakening but its outcome is operator-dependent. To overcome this limitation, an enhanced PGT (EPGT) is proposed that replaces the pass/fail outcome of the PGT with a continuous measurement of the pulling force that is needed to remove the card (EPGT-force). Research question Is the EPGT-force an accurate, reliable and clinically applicable measurement of strength? Methods Reliability and clinical applicability were examined in two ways. Firstly, two examiners measured EPGT-force for twenty healthy volunteers in a test/retest set-up. EPGT force was measured using a dynamometer, the hallux grip force was measured using a pressure mat. The clinical applicability of the EPGT was tested in ten people with diabetes. Postural sway was also measured. Results Interclass correlation coefficients (ICC) revealed excellent inter-rater reliability (ICC > 0.75). Intra-rater reliability was excellent for the first examiner (ICC = 0.795) and good for the second (ICC = 0.703). Linear regression analysis indicated that hallux grip force accounted (on average) for 83%±4% of the variability in EPGT force. This strong relationship between EPGT force and hallux grip force remained when the test was performed in a clinical setting with the latter accounting for 88% in EPGT force variability. Spearman rank order correlation showed that people with diabetes with a higher difference in EPGT force between limbs swayed more. Significance EPGT force is a reliable and accurate measurement of hallux grip force. Hallux grip force was previously found to be strongly correlated to the strength of all muscle groups of the foot and ankle and to the ability to maintain balance. The proposed EPGT could be used to monitor muscle weakness in clinics for better falls-risk assessment

Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology)

Over the last twenty years, newly developed chemical sensor systems (so called “electronic noses”) have made odor analyses possible. These systems involve various types of electronic chemical gas sensors with partial specificity, as well as suitable statistical methods enabling the recognition of complex odors. As commercial instruments have become available, a substantial increase in research into the application of electronic noses in the evaluation of volatile compounds in food, cosmetic and other items of everyday life is observed. At present, the commercial gas sensor technologies comprise metal oxide semiconductors, metal oxide semiconductor field effect transistors, organic conducting polymers, and piezoelectric crystal sensors. Further sensors based on fibreoptic, electrochemical and bi-metal principles are still in the developmental stage. Statistical analysis techniques range from simple graphical evaluation to multivariate analysis such as artificial neural network and radial basis function. The introduction of electronic noses into the area of food is envisaged for quality control, process monitoring, freshness evaluation, shelf-life investigation and authenticity assessment. Considerable work has already been carried out on meat, grains, coffee, mushrooms, cheese, sugar, fish, beer and other beverages, as well as on the odor quality evaluation of food packaging material. This paper describes the applications of these systems for meat quality assessment, where fast detection methods are essential for appropriate product management. The results suggest the possibility of using this new technology in meat handling

How different are corporate social responsibility motives in a developing country? Insights from a study of Indian agribusiness firms

Against the backdrop of increasing foreign direct investment flows in the developing economies in Asia the investigation of topical aspects of corporate social responsibility (CSR) in the region increases in importance. We examine the CSR motives of four large indigenous agribusiness firms in India with a view to assess the validity of the claim that CSR in this country, compared to developed countries, is influenced substantially more by moral, cultural and religious considerations and less by self-interest and profit seeking. Unlike numerous other investigations of CSR that rely on questionnaires and company reports, our data are drawn from in-depth interviews and theme analysis revealing some intricate motives behind CSR behavior and business conditions that inspire them. Our findings challenge some previously reported results and indicate that the degree to which such behavior is affected by the state of economic development and cultural differences may be smaller than is often argued

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Elective cancer surgery in COVID-19-free surgical pathways during the SARS-CoV-2 pandemic: An international, multicenter, comparative cohort study

PURPOSE As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19–free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19–free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19–free surgical pathways. Patients who underwent surgery within COVID-19–free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19–free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score–matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19–free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION Within available resources, dedicated COVID-19–free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Elective Cancer Surgery in COVID-19-Free Surgical Pathways During the SARS-CoV-2 Pandemic: An International, Multicenter, Comparative Cohort Study.

PURPOSE: As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19-free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS: This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19-free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS: Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19-free surgical pathways. Patients who underwent surgery within COVID-19-free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19-free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score-matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19-free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION: Within available resources, dedicated COVID-19-free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks