Role of Artificial Intelligence, Automation, and Machine Learning in Sustainable Plastics Packaging markets: Progress, Trends, and Directions

The optimisation of manufacturing processes in terms of resource consumption, waste minimization, and pollutant emissions is gaining prominence, especially in small and medium-sized enterprises (SMEs). The advent of digital technology and the subsequent explosion in data volume is another key factor. There is great potential in the data collected from a wide variety of devices and systems, which is why even smaller businesses require access to clever, dynamic analytic models. Sustainable packaging solutions are gaining significance as the world struggles to address global environmental issues. These solutions are being developed with a growing contribution from artificial intelligence (AI). Artificial intelligence is being used to create sustainable and environmentally friendly packaging. Artificial intelligence (AI)-driven technology can be used, for instance, to determine which packing materials and designs are optimal for a given product. Artificial intelligence can also be used to determine the best methods of packaging, such as those that make the most of recyclable materials or that optimise packaging lines. The term "plastic production automation" refers to the use of automated systems and machines in the production of plastic goods. Computer-aided design (CAD), robots, and other cutting-edge technologies are used to automate and optimise production. In this article, we describe the findings of a study that aimed to determine whether or not small and medium-sized enterprises (SMEs) in the plastics processing industry may benefit from the use of machine learning methods in order to optimise energy consumption and reduce the number of wrongly made plastic parts. The machine data in a plastics manufacturing facility for the automobile industry were recorded and analysed in terms of the material and energy fluxes for this purpose. To find areas for optimisation, these data were trained using machine learning techniques. The project also sought to solve the challenge of analysing manufacturing processes with significant non-linearities and time-invariant behaviour by employing Big Data techniques and self-learning controls. Machine learning can help with this if there is enough data to train the system

The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics

The plastic industry and consumer demand have both exploded since the 1950s. Plastic waste in the ocean has also skyrocketed, growing by a factor of 10 since 1980. Many animal species can't survive this kind of pollution. This is probably bad for people. Impacts plankton, which in turn modifies the carbon cycle. Effects global warming by adding to it. This list, by the way, is not comprehensive. When scarce resources are used inefficiently and without good planning, a great deal of waste is generated, which has a negative impact on the natural world. The notion of a circular economy (CE) has shown encouraging signs of being adopted at industrial and governmental levels as an alternative for the conventional but wasteful linear manufacturing lines. Through careful planning and subsequent reuse, recycling, and remanufacturing, CE strives to maximise the value of raw materials over a product's entire life cycle. Two cutting-edge technologies that can considerably aid in the widespread acceptance and application of CE in actual practises are artificial intelligence (AI) and machine learning (ML). This research delves into how AI applications are being included into CE

GR-303 Dempster-Shafer Theory of Computation

To implement the Dempster-Shafer Theory algorithm as well as check the prediction accuracy using this mentioned algorithm in the iris dataset. The actual aim of this paper is to understand and implement the Dempster-Shafer Theory using the python programming language. To reflect doubt and inaccuracies in the evidence, one might use a belief structure that is defined as a set function m that satisfies. The conjecture of this report is to get the closable prediction accuracy that can help to understand future improvement as well as the gap of the applied implementation technique

COLECCIÓN MARÍA DOLORES RAMÍREZ RODRÍGUEZ [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

Late-onset renal vein thrombosis: A case report and review of the literature

AbstractINTRODUCTIONRenal vein thrombosis, a rare complication of renal transplantation, often causes graft loss. Diagnosis includes ultrasound with Doppler, and it is often treated with anticoagulation or mechanical thrombectomy. Success is improved with early diagnosis and institution of treatment.PRESENTATION OF CASEWe report here the case of a 29 year-old female with sudden development of very late-onset renal vein thrombosis after simultaneous kidney pancreas transplant. This resolved initially with thrombectomy, stenting and anticoagulation, but thrombosis recurred, necessitating operative intervention. Intraoperatively the renal vein was discovered to be compressed by a large ovarian cyst.DISCUSSIONCompression of the renal vein by a lymphocele or hematoma is a known cause of thrombosis, but this is the first documented case of compression and thrombosis due to an ovarian cyst.CONCLUSIONEarly detection and treatment of renal vein thrombosis is paramount to restoring renal allograft function. Any woman of childbearing age may have thrombosis due to compression by an ovarian cyst, and screening for this possibility may improve long-term graft function in this population