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The present study analyzes the representation of the anomaly in Oscar de profundis by Catherine Mavrikakis. This inexplicable event is paradoxically ever-present and completely absent in end of world literature. In particular, we will examine the role of ruins in the portrayal of the post-catastrophe scenario. This paper argues that the anomaly invites the reader to investigate further. However, they will only find ruins which are a constant reminder of what was lost and not an explanation of how these memories were erased from the collective conscience. The concept of depicting the anomaly through ruins will allow us to better understand how end of world literature may propose a critique of contemporary society.

There is a growing demand for detailed building façade models (Level-of-Detail 3: LoD 3) in a variety of applications. Despite the increasing number of papers addressing this issue in the literature, occlusions are still a significant problem when processing building façade elements. Conversely, depending on the view angle of the images, the detected elements might not be projected to their accurate locations causing uncertainties in their 3D locations. In this paper, we address the aforementioned issues utilizing multi-view images. Using a building footprint layer, we first locate the points belonging to buildings. We then detect the 2D windows and doors in images by combining Faster R-CNN and Segment Anything (SAM) deep learning models. The 2D borders are projected into the 3D object space using a pinhole camera model and collinearity equations. Utilizing the multi-view capabilities of mobile scanning systems, this method effectively mitigates uncertainties associated with occlusion and exterior orientation parameters (EOP). This study provides a comprehensive evaluation of 3D spatial accuracy, achieving an average of 84% Intersection over Union (IoU) accuracy for 12 different single-sided façades over 750 multi-view images for 312 windows and doors of various sizes with rectangular and curved shapes.

According to the infrared patch image (IPI) model theory, the infrared image background has a low rank and the target is sparse. The low-rank model can be used to separate the background and identify the target. However, in a noisy environment, the recognition effect will be affected. The higher the noise, the harder it would be to detect a small target. The residual strong fault and background edges could reduce the detection rate and increase false alarms. The traditional IPI model is adaptable to the background with the lower noise. This paper combines weighted nuclear norm minimization (WNNM) optimization with sparse representation based on the local IPI model. The background details are described more prominently by improving the nuclear norm weighting factor. The target is much easier to detect under the specific bright clouds and ground buildings background with high noise. At the same time, post-processing with image local contrast analysis is performed to compare traditional spatial filtering and local infrared patch image model algorithms. Our method has a good suppression effect on complex noise backgrounds and achieves a higher signal to clutter ratio gain (SCRG). It could also improve the target detection rate and reduce false alarms.

Accurate three-dimensional mapping and digital twinning provides a powerful tool for effective maintenance of civil infrastructure and supports efficient future planning of new developments. Three-dimensional mapping can be efficiently performed with a Mobile Mapping System (MMS) that records geospatial data from platform-mounted sensors. However, it is expensive to continuously update datasets by re-capturing with MMS. This paper outlines a novel method allowing camera-only approaches for updating and change detection. It resolves key issues with inherent resolution differences between MMS laser scanner point-clouds and camera images. An intermediary is used to register two disparate datasets. This novel approach to synthetic camera images (SCIs) bridges the differences between MMS point-clouds and camera images and aid in coarse registration of camera images to an outdoor MMS point-cloud. SCI coarse registration precision is maximized by generating surfaces, interpolating intensity values, and reducing noise with a median filter. Landmark features coarsely register the camera image to the MMS point-cloud. The coarse registration is most precise when the whole scene is captured either from the same location as the SCI or further from the scene. Landmarks precisely detect scenes when changes are less than 20%, and foliage does not exceed 20% of the camera image.

Ultraviolet (UV)-reflectance is an essential signal of many plant species, which use wavelength-selective pigments in floral reproductive structures to determine the color of flowers and how they appear to their aerial pollinators, primarily bees. This paper presents a pollinator-inspired remote-sensing system incorporating UV reflectance into a flower detector for strawberry crops. We designed a compact, cost-effective UV-sensitive camera for aerial remote sensing over crop rows. Our camera and a deep-learning algorithm comprise our Nature-Inspired Detector (NID) system. We trained YOLOv5 and Faster R-CNN on our dataset of strawberry images incorporating the UV spectrum (300–400 nm). Our results showed that NID-based YOLO V5 outperformed NID-based Faster R-CNN in training time (0.3 vs. 4.5–5.5 hours) and mean Average Precision—mAP (0.951 vs. 0.934). We also present the field-test of our NID-based YOLOv5 system on a drone platform to validate its ability to detect strawberry flowers.

In this paper, the central Indian Ocean (60°–95°E, 0°–37°S) has been selected as the research area, and Argo salinity data are used as the measured values. The Catboost algorithm is introduced for the first time to retrieve sea surface salinity, and a comparison is made with the traditional artificial neural network (ANN) and random forest (RF) machine learning algorithm. The results show that: (1) Through linear fitting with the Argo salinity, the R2 of the three machine learning methods are 0.9299, 0.88 and 0.83, respectively. The corresponding RMSE were 0.2360, 0.3004, and 0.3156 psu, and MAE were 0.1816, 0.2486, and 0.2641 psu, respectively. (2) The spatial distribution of salinity of Argo and SMOS was compared with the inversion results of the model. It was found that the salinity of the sea area was lower at (83°–88°E, 24°–27°S) and (68°–72°E, 17°–20°S), and higher at 30°–35° south latitude, showing consistent with Argo. (3) The stability of the model was independently verified using the data from January to March 2020, and it was found that the R2 of the RF model shows the best stability, while the R2 of the ANN model shows the worst stability.

(The ancient tradition concerning Pythagoras of Samos’ role as the founder of physiognomy): This paper collects and analyzes ancient sources that refer to Pythagoras of Samos as the founder of physiognomy. Interest in physiognomy, which deals with the relationship between the body and the spirit, has become more popular over the last few years; however, many aspects of research into the topic are still obscure and worthy of further study. Physiognomy was assumed by ancient authors to be the method by which candidates were selected to enter the Pythagorean community, used by the Greek philosopher and his followers. Information about Pythagoras, who – by simply looking at somebody – could recognize their character appears, in the works of Aulus Gellius, Hippolytus, Porphyry, Iamblichos, Proclos and Olympiodorus. Apart from discussing testimonia about Pythagoras and his physiognomic examinations, the text explores the role of physiognomy during the selection process for the Pythagorean community in order to provide a better understanding of elite traditions within society. The paper also supplies further insight with regards to the methodology behind physiognomical surveys, which were popular during antiquity.

(Tiberius’ Actions in Selected Regions as Instruments of Diplomatic Policy from 20 BCE to 37 CE): This article is devoted to the diplomatic activities of Tiberius which can be divided into two main phases. The first period concerns the time before Tiberius became emperor (20 BCE–14 CE), whilst the second relates to his time as ruler of the Empire (14–37). The composition of the work is geographical, because in some regions Tiberius acted as a diplomatic envoy of Augustus and later as the emperor. The paper discusses his role and how effective his actions were with regards to the following regions: (I) Armenia and Parthia; (II) Pannonia; (III) Germania; (IV) Thrace; (V) Cappadocia, Commagene, Amanus and Pontus; (VI) Greek cities; (VII) Palestine and Syria.

Introduction In the new globalized world, noise pollution has started to become a public health problem. We searched for publications about noise pollution in our study. In our study, we examined the noise pollution articles in the Web of Science (WoS) database. Material and Methods A total of 2722 papers were identified which were published between 2001 and 2020, finally, 1815 articles were analyzed. VOSviewer (version 1.6.11) tool used for bibliometric web visualizations. Results When trend analysis was applied to the number of articles by years, a statistically significant increase was detected. The United States contributed the most publications. The countries with the most articles per million population were determined to be Scotland, Singapore, and Ireland, respectively. Most articles on noise pollution were published in the journal Applied Acoustics. The three most used keywords are “noise pollution”, “noise” and “traffic noise”. Conclusions This study showed that there has been a trend of an increasing number of articles on noise pollution in recent years. Also it can be considered that this bibliometric study will help identify gaps in the current research data.