Il canis ingens, il Capricorno e l’aragosta nella Cena Trimalchionis

This paper focuses on the exegesis of some Petronian animals. As for the chained dog, the painted one (29.1) and the one in the flesh (64.7-9 and 72.7-9), we recognise both a dense network of epic references to the catabasis of Aeneas and ironic allusions to Seneca and to the popular tradition. Besides that, in our opinion, the epic language used to describe the fall of Ascyltus and Encolpius into the fishpond (72.7) emphasises the game between art and life which pervades the Cena. In the second part we defend the manuscript reading super capricornum locustam marinam (35.4).

Smart Archive Generation Using Computer Vision, NLP and Big Data

Gaining insights from the dense network of interrelated documents involved in E&P projects requires experience, knowledge, and awareness about the existence of the required data. This framework aims to facilitate the decision-making process while consuming shorter time periods and lower costs, without sacrificing the accuracy of the data and decreasing the probability of human errors. The high complexity of E&P Projects results in a dense network of interrelated documents which are produced to cover the various aspects and details of the project. Gaining insights from old data requires experience, knowledge, and awareness about the existence of the required data. Accordingly, the knowledge accumulated over the time from various projects can be considered a key asset, since it can be leveraged to perform more informed decisions. This paper presents a framework that aim at capturing organizational knowledge locked in paper-based datasets and store it in a structured digital format that facilitates its retrieval and enables analyses which help uncover valuable insights. This research aims to generate valuable data from existing archives while causing minimal disturbance to existing business processes and workflows. The framework performs four main functions: image processing, text recognition, Data Analytics and Data storage. Initially the text recognition module; which is performs Image Processing to enhance the quality of the scanned files, and optical character recognition using LSTM which extracts the text contained in images. The Data Analytics Module, then cleanses and mines the extracted text using Big Data Analytics tools. Text Matching and searching is performed on the Spark Dataframe using regular expressions to identify different attributes and their different types. Finally, the data is stored in a SQL Database. In order to measure the workflow's accuracy a manual baseline was generated for a sample project. The accuracy is measured using field-level verification, since it was found to be the most fit-for-purpose, as it allows to measure the accuracy of the workflow on the level of each field.

Reconstruction of the late first millennium AD harbor site of Sembiran and analysis of its tradeware

The site of Sembiran on the northern coast of Bali was an important trading harbor with demonstrated intensive links to the Indian subcontinent, the Western Indian Ocean, and Mainland Southeast Asia between the second century BC and the second century AD. Using a combination of excavation and geophysical survey, we have newly mapped a dense network of subsurface structures, which we interpret to be foundations for harbor infrastructure dated to the eighth to ninth centuries AD that were subsequently covered by shoreline aggradation. An assemblage of eighth to twelfth centuries AD Chinese tradeware in dated contexts from our excavations of these shoreline structures and additional trenches further inland suggests a renewal in trade activities at Sembiran, coinciding with the growth of Chinese maritime trade in Island Southeast Asia.

From Siliciclastic to Bioclastic Deposits in the Gulf of Naples: New Highlights from Offshore Ischia and Procida–Pozzuoli Based on Sedimentological and Seismo-Stratigraphic Data

This study discusses the siliciclastic to bioclastic deposits (in particular, the rhodolith deposits) in the Gulf of Naples based on sedimentological and seismo-stratigraphic data. The selected areas are offshore Ischia Island (offshore Casamicciola, Ischia Channel), where a dense network of sea-bottom samples has been collected, coupled with Sparker Multi-tip seismic lines, and offshore Procida–Pozzuoli (Procida Channel), where sea-bottom samples are available, in addition to Sparker seismic profiles. The basic methods applied in this research include sedimentological analysis, processing sedimentological data, and assessing seismo-stratigraphic criteria and techniques. In the Gulf of Naples, and particularly offshore Ischia, bioclastic sedimentation has been controlled by seafloor topography coupled with the oceanographic setting. Wide seismo-stratigraphic units include the bioclastic deposits in their uppermost part. Offshore Procida–Pozzuoli, siliciclastic deposits appear to prevail, coupled with pyroclastic units, and no significant bioclastic or rhodolith deposits have been outlined based on sedimentological and seismo-stratigraphic data. The occurrence of mixed siliciclastic–carbonate depositional systems is highlighted in this section of the Gulf of Naples based on the obtained results, which can be compared with similar systems recognized in the central Tyrrhenian Sea (Pontine Islands).

Hybrid Dense Network with Dual Attention for Hyperspectral Image Classification

Hyperspectral images (HSIs) have been widely used in many fields of application, but it is still extremely challenging to obtain higher classification accuracy, especially when facing a smaller number of training samples in practical applications. It is very time-consuming and laborious to acquire enough labeled samples. Consequently, an efficient hybrid dense network was proposed based on a dual-attention mechanism, due to limited training samples and unsatisfactory classification accuracy. The stacked autoencoder was first used to reduce the dimensions of HSIs. A hybrid dense network framework with two feature-extraction branches was then established in order to extract abundant spectral–spatial features from HSIs, based on the 3D and 2D convolutional neural network models. In addition, spatial attention and channel attention were jointly introduced in order to achieve selective learning of features derived from HSIs. The feature maps were further refined, and more important features could be retained. To improve computational efficiency and prevent the overfitting, the batch normalization layer and the dropout layer were adopted. The Indian Pines, Pavia University, and Salinas datasets were selected to evaluate the classification performance; 5%, 1%, and 1% of classes were randomly selected as training samples, respectively. In comparison with the REF-SVM, 3D-CNN, HybridSN, SSRN, and R-HybridSN, the overall accuracy of our proposed method could still reach 96.80%, 98.28%, and 98.85%, respectively. Our results show that this method can achieve a satisfactory classification performance even in the case of fewer training samples.

Preparation of Modified PVA Copolymer by DAAM/ADH and Cross-Linking Mechanism for Paper

Herein, a novel modified polyvinyl alcohol(DA-IPVA) used as sizing agent was prepared by using diacetone acrylamide(DAAM) as graft monomer and N-(isobutoxymethyl)acrylamide(IBMA) as self-cross-linking monomer. The effect of the amount of DAAM on the properties of emulsion, film and sizing paper was discussed. The surface micro-structure of the sizing paper was characterized by SEM and AFM. The addition of DAAM/ADH and IBMA endowed DA-IPVA with cross-linked active group and increased cross-linking density and hydrophobicity after ADH was added into the emulsion and the cross-linking structure was formed. The enhancing mechanism of surface sizing agent for paper was revealed. The DA-IPVA can be cross-linked on the surface and inside of the paper to form a dense network structure, which improves the bonding force between the fibers. When the mole fraction of DAAM of the cross-linking monomer is 8%, the surface sizing performance of the paper is obviously improved compared with the base paper. The dry and wet strength is increased by 266.5% and 334.3% respectively, and the folding resistance is increased 2946.67%. This study can have a profound impact on the development of the technology of cross-linking surface sizing agent for paper.

Performance Evaluation of a Multi-Hop Cluster Based Algorithm for Vehicular Ad-hoc Networks

Vehicular ad hoc networks are characterized as the ad hoc networks with dynamic and dense network topology which faces issues like routing, data congestion, and overhead. One technique which has proved to be useful in managing VANETs is clustering. Clustering is a technique to divide the network into smaller, distributed and more stable hierarchical structure. The parameters like speed, position, distance, direction and mobility are used for clustering the networks. Clustering helps in load balancing, improving scalability, efficient resource allocation and reducing overhead. In this paper a multi-hop cluster-based algorithm (MhCA) for VANET is proposed which uses Fuzzy TOPSIS for CH selection based on Rank Index of nodes. The flowchart of the algorithm along with the description of the algorithm is given below in the paper. Extensive simulation experiments are run using the ns3 and SUMO to evaluate & compare the performance of proposed algorithm with the existing multi-hop algorithms like VMaSC and n-hop. Keywords: CH, CM, CH Change Duration, CH Duration, OSM, NS3.