Operational excellence assessment framework for manufacturing companies

Operational Excellence (OE) is a consequence of an enterprise-wide practises based on correct principles that can be classified under four dimensions; Culture, Continuous Process Improvement, Enterprise Alignment and Results. To achieve OE, organisations have to attain a high maturity level and measurable success in the four dimensions as assessed externally by accredited institutions or consultants. External assessment is costly and can be inaccurate due to the lack of in depth knowledge of the organisation by external assessors, on the contrary, self-assessment of an organisations OE is cost effective and accurate if performed with a complete tool which assesses all four dimensions of OE. A complete OE self-assessment tool is currently unavailable, thus this study focuses on the development of a complete OE self-assessment tool. Using a matrix to critically evaluate and compare existing self-assessment tools in areas such as dimensions assessed, scoring criteria and usability, a complete self-assessment tool is then developed based on the combination of existing assessment tools. The tool is validated through the application, by managers, within a manufacturing company that already implements aspects of lean in order to self-assess its OE. The results of the assessment form the basis on which a roadmap to achieving OE is then developed

Letter from Jay Stanley Jackson as well as a photo of the same

Letter concerning a position in the English department at Utah Agricultural College, as well as a photo of Jay Stanley Jackso

Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients

While neuroevolution (evolving neural networks) has a successful track record across a variety of domains from reinforcement learning to artificial life, it is rarely applied to large, deep neural networks. A central reason is that while random mutation generally works in low dimensions, a random perturbation of thousands or millions of weights is likely to break existing functionality, providing no learning signal even if some individual weight changes were beneficial. This paper proposes a solution by introducing a family of safe mutation (SM) operators that aim within the mutation operator itself to find a degree of change that does not alter network behavior too much, but still facilitates exploration. Importantly, these SM operators do not require any additional interactions with the environment. The most effective SM variant capitalizes on the intriguing opportunity to scale the degree of mutation of each individual weight according to the sensitivity of the network's outputs to that weight, which requires computing the gradient of outputs with respect to the weights (instead of the gradient of error, as in conventional deep learning). This safe mutation through gradients (SM-G) operator dramatically increases the ability of a simple genetic algorithm-based neuroevolution method to find solutions in high-dimensional domains that require deep and/or recurrent neural networks (which tend to be particularly brittle to mutation), including domains that require processing raw pixels. By improving our ability to evolve deep neural networks, this new safer approach to mutation expands the scope of domains amenable to neuroevolution

ES Is More Than Just a Traditional Finite-Difference Approximator

An evolution strategy (ES) variant based on a simplification of a natural evolution strategy recently attracted attention because it performs surprisingly well in challenging deep reinforcement learning domains. It searches for neural network parameters by generating perturbations to the current set of parameters, checking their performance, and moving in the aggregate direction of higher reward. Because it resembles a traditional finite-difference approximation of the reward gradient, it can naturally be confused with one. However, this ES optimizes for a different gradient than just reward: It optimizes for the average reward of the entire population, thereby seeking parameters that are robust to perturbation. This difference can channel ES into distinct areas of the search space relative to gradient descent, and also consequently to networks with distinct properties. This unique robustness-seeking property, and its consequences for optimization, are demonstrated in several domains. They include humanoid locomotion, where networks from policy gradient-based reinforcement learning are significantly less robust to parameter perturbation than ES-based policies solving the same task. While the implications of such robustness and robustness-seeking remain open to further study, this work's main contribution is to highlight such differences and their potential importance

The Potential for LISA -Type Nitrogen Use Adjustments in Mainstream U.S. Agriculture

Concern about environmental impacts of nitrogen fertilizer use is increasing. Mainstream agriculture is dependent on nitrogen fertilizer and use patterns are polluting water resources. A five cent tax on nitrogen fertilizer is shown to have three benefits. National nitrogen fertilizer use is estimated to decline about 10 percent. Use of legume-produced nitrogen increases and crop use of nitrogen declines only 5 percent. A reduction in wasted legume-produced nitrogen equal to 2.5 percent of nitrogen application in the baseline occurs due to more growing of legumes and other crops in rotation. The nitrogen tax is not without costs. Soil erosion and pesticide use are estimated to increase 2.2 and 1.7 percent, respectively, in response to the tax

The Impact of Long-Term Disruptions on Academic Success in Higher Education and Best Practices to Help Students Overcome Them

Disruptions in the delivery of academic content in higher education had been minimal as recently as 2020. Learning obstacles have not only become more frequent but also exhibit increased duration due to climate related natural disasters, and most recently, global health pandemics. The primary investigators explore the negative impacts these events have had on students’ academic success, and the best practices implemented by colleges and universities to help students overcome these issues, and persist in attaining personal and academic goals. Analysis will examine institutional-based efforts for academic student support, guidance, and motivation together with the students’ resiliency efforts. Analysis will also describe the effects on the online delivery of academic content. Findings demonstrate that one of the best ways institutions can encourage academic success is through self-resiliency, and that proactive steps mitigate stressful events

Sentiment Analysis of Customer Reviews in Zomato Bangalore Restaurants Using Random Forest Classifier

Natural Language Processing is one part of Artificial Intelligence and Machine Learning tomake an understanding of the interactions between computers and human (natural) languages.Sentiment analysis is one part of Natural Language Processing, that often used to analyzewords based on the patterns of people in writing to find positive, negative, or neutralsentiments. Sentiment analysis is useful for knowing how users like something or not.Zomato is an application for rating restaurants. The rating has a review of the restaurantwhich can be used for sentiment analysis. Based on this, writers want to discuss the sentimentof the review to be predicted. The method used for preprocessing the review is to make allwords lowercase, tokenization, remove numbers and punctuation, stop words, andlemmatization. Then after that, we create word to vector with the term frequency-inversedocument frequency (TF-IDF). The data that we process are 150,000 reviews. After thatmake positive with reviews that have a rating of 3 and above, negative with reviews that havea rating of 3 and below, and neutral who have a rating of 3. The author uses Split Test, 80%Data Training and 20% Data Testing. The metrics used to determine random forest classifiersare precision, recall, and accuracy. The accuracy of this research is 92%. The precision ofpositive, negative, and neutral sentiment are 92%, 93%, 96%. The recall of positive, negative,and neutral sentiment are 99%, 89%, 73%. Average precision and recall are 93% and 87%.The 10 words that affect the results are: “bad”, “good”, “average”, “best”, “place”, “love”,“order”, “food”, “try”, and “nice”

Genomic presence of recombinant porcine endogenous retrovirus in transmitting miniature swine

The replication of porcine endogenous retrovirus (PERV) in human cell lines suggests a potential infectious risk in xenotransplantation. PERV isolated from human cells following cocultivation with porcine peripheral blood mononuclear cells is a recombinant of PERV-A and PERV-C. We describe two different recombinant PERV-AC sequences in the cellular DNA of some transmitting miniature swine. This is the first evidence of PERV-AC recombinant virus in porcine genomic DNA that may have resulted from autoinfection following exogenous viral recombination. Infectious risk in xenotransplantation will be defined by the activity of PERV loci in vivo