One of the most important issues in Transhipment Container Terminal (TCT) management is to have fairly reliable and affordable predictions about vessel arrival. Terminal operators need to estimate the actual time of arrival in port in order to determine the daily demand for each work shift with greater accuracy. In this way, the resources required (human resources, equipment as well as spatial resources) can be allocated more efficiently. Despite contractual obligations to notify the Estimated Time of Arrival (ETA) 24 hours before arrival, ship operators often have to revise it due to unexpected events like weather conditions, delay in a previous port and so on. For planners the decision-making processes related to this topic can sometimes be so complex without the support of suitable methodological tools. Specific models should be adopted, in a daily planning scenario, to provide a useful support tool in TCTs. In this study, we discuss an exploratory analysis of the data affecting delays registered at a Mediterranean TCT. We present some preliminary results obtained using data mining techniques and propose a Classification and Regression Trees (CART) model to reduce the range of uncertainty of ship arrivals in port. This approach is compulsory to manage vast amounts of unstructured data involved in estimating of vessel arrivals.

This study aims to explore if the arthroscopically assisted reduction and internal fixation (ARIF) technique is superior to the traditional open reduction and internal fixation (ORIF) technique in the treatment of tibial lateral plateau fractures. Forty patients with tibial plateau fractures (Schatzker type I–III) treated with ARIF or ORIF from 2012 to 2017 were included in this retrospective study. All patients received pre-operative radiographs and CT scans. The patients were divided into two groups (ARIF or ORIF). All patients had a minimum follow-up of 12 months and an average follow-up of 44.4 months. The clinical and radiographic outcomes were evaluated according to the Knee Society Score (KSS) and the modified Rasmussen radiological score. Satisfactory clinical and radiological results were found in 39 out of 40 (97.5%) patients. KSS and modified Rasmussen radiological score were significantly better in ARIF group. The mean KSS was 92.37 (± 6.3) for the ARIF group and 86.29 (± 11.54) for the ORIF group (p < 0.05). The mean modified Rasmussen radiographic score was 8.42 (± 2.24) for the ARIF group and 7.33 (± 1.83) for the ORIF group (p = 0.104). Worst clinical and radiological results were related to concomitant intra-articular lesions (p < 0.05). Meniscal tears were found and treated in 17 out of 40 (42.5%) patients. The overall complication rate was 10%. Both ARIF and ORIF provided a satisfactory outcome for the treatment of Schatzker I–III tibial plateau fractures. However, ARIF led to better clinical results than ORIF. No statistically significant differences were found in perioperative complications, radiological results, and post-traumatic knee osteoarthritis. Level III

The number of physically active individuals who develop knee and hip arthritis and who undergo arthroplasties of these joints ie ever increasing. It has become necessary to develop evaluation scales which address the specific issues raised by such individuals. The High Activity Arthroplasty Score is one such scales, originally developed in English. The HAAS-I was developed by means of forward-backward translation, a final review by an expert committee and a test of the pre-final version to establish its correspondence with the original English version. The psychometric testing included reliability by means of internal consistency (Cronbach’s alpha) and test–retest reliability (intraclass correlation coefficients) and construct validity by Pearson’s correlations with a pain intensity numerical rating scale (NRS), the Western Ontario and McMaster University index (WOMAC, for THA subjects), the Knee injury and Osteoarthritis Outcome Scale (KOOS; for TKA subjects) and the Short-Form 36 Health Survey (SF-36). The questionnaire was administered to 67 subjects with THA and 61 with TKA and proved to be acceptable. The questionnaire showed good internal consistency (0.85 for THA and 0.91 for TKA) and a high level of test–retest reliability (ICC = 0.97 with 95% CI 0.95–0.98 for THA; ICC = 0.95 with 95% CI 0.92–0.98 for TKA). There was a moderate correlation between the HAAS-I and NRS (r = − 0.40), there was a high correlation between the HAAS-I and WOMAC (r = − 0.68) and there were moderate to high correlations between the HAAS-I and SF-36 subscales (r = 0.34 to 0.63) for THA. There was a moderate correlation between the HAAS-I and NRS (r = − 0.77); there was a high correlation between the HAAS-I and KOOS subscales (r = − 0.79 to r = − 0.91); and there were low correlations between the HAAS-I and SF-36 subscales (r = 0.01 to 0.29) for TKA. The HAAS-I was successfully translated into Italian and proved to have good psychometric properties that replicated the results of existing versions. Its use is recommended for clinical and research purposes.

Non-destructive, fast, and accurate methods of dating are highly desirable for many heritage objects. Here, we present and critically evaluate the use of near-infrared (NIR) spectroscopic data combined with three supervised machine learning methods to predict the publication year of paper books dated between 1851 and 2000. These methods provide different accuracies; however, we demonstrate that the underlying processes refer to common spectral features. Regardless of the machine learning method used, the most informative wavelength ranges can be associated with C-H and O-H stretching first overtone, typical of the cellulose structure, and N-H stretching first overtone from amide/protein structures. We find that the expected influence of degradation on the accuracy of prediction is not meaningful. The variance-bias decomposition of the reducible error reveals some differences among the three machine learning methods. Our results show that two out of the three methods allow predictions of publication dates in the period 1851-2000 from NIR spectroscopic data with an unprecedented accuracy of up to 2 years, better than any other non-destructive method applied to a real heritage collection.