Estimation of Time for Manufacturing of Injection Moulds Using Artifi cial Neural Networks-based Model

Jedna od najvažnijih aktivnosti u izvedbi projektno orijentiranih poslovnih procesa ocjena je potrebnih tehnoloških vremena i troškova. To je rana faza procjene koju provode visoko kvalifi cirani interni stručnjaci. Jedna od najvećih zapreka u toj fazi je precizno defi niranje odnosa između karakteristika proizvoda i potrebnih tehnoloških vremena za izradu kalupa. Ovaj rad predlaže pristup rješavanju tog problema korištenjem umjetnih neuronskih mreža. Razvijeni model pokazuje da je moguće postići prihvatljivu točnost procjene korištenjem lako dostupnih ulaznih podataka.One of the most crucial activities for a successful business is project time and cost estimation. This is an early estimation process which is usually handled by highly skilled, in-house experts. One of the main obstacles in this process is to accurately defi ne the relationship between product properties and the machining hours necessary to manufacture the mould. This article suggests how to address this problem by using artifi cial neural networks (ANN). The developed model shows that it is possible to achieve admissible accuracy of the estimation by using easily obtainable input data

Semantic Frame Identification with Distributed Word Representations

Abstract We present a novel technique for semantic frame identification using distributed representations of predicates and their syntactic context; this technique leverages automatic syntactic parses and a generic set of word embeddings. Given labeled data annotated with frame-semantic parses, we learn a model that projects the set of word representations for the syntactic context around a predicate to a low dimensional representation. The latter is used for semantic frame identification; with a standard argument identification method inspired by prior work, we achieve state-ofthe-art results on FrameNet-style framesemantic analysis. Additionally, we report strong results on PropBank-style semantic role labeling in comparison to prior work

PANC Study (Pancreatitis: A National Cohort Study): national cohort study examining the first 30 days from presentation of acute pancreatitis in the UK

Abstract Background Acute pancreatitis is a common, yet complex, emergency surgical presentation. Multiple guidelines exist and management can vary significantly. The aim of this first UK, multicentre, prospective cohort study was to assess the variation in management of acute pancreatitis to guide resource planning and optimize treatment. Methods All patients aged greater than or equal to 18 years presenting with acute pancreatitis, as per the Atlanta criteria, from March to April 2021 were eligible for inclusion and followed up for 30 days. Anonymized data were uploaded to a secure electronic database in line with local governance approvals. Results A total of 113 hospitals contributed data on 2580 patients, with an equal sex distribution and a mean age of 57 years. The aetiology was gallstones in 50.6 per cent, with idiopathic the next most common (22.4 per cent). In addition to the 7.6 per cent with a diagnosis of chronic pancreatitis, 20.1 per cent of patients had a previous episode of acute pancreatitis. One in 20 patients were classed as having severe pancreatitis, as per the Atlanta criteria. The overall mortality rate was 2.3 per cent at 30 days, but rose to one in three in the severe group. Predictors of death included male sex, increased age, and frailty; previous acute pancreatitis and gallstones as aetiologies were protective. Smoking status and body mass index did not affect death. Conclusion Most patients presenting with acute pancreatitis have a mild, self-limiting disease. Rates of patients with idiopathic pancreatitis are high. Recurrent attacks of pancreatitis are common, but are likely to have reduced risk of death on subsequent admissions. </jats:sec