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Abstract

The present review aims to improve the scope and coverage of the phylogenetic matrices currently in use, as well as explore some aspects of the relationships among Paleogene penguins, using two key skeletal elements, the humerus and tarsometatarsus. These bones are extremely important for phylogenetic analyses based on fossils because they are commonly found solid specimens, often selected as holo− and paratypes of fossil taxa. The resulting dataset includes 25 new characters, making a total of 75 characters, along with eight previously uncoded taxa for a total of 48. The incorporation and analysis of this corrected subset of morphological characters raise some interesting questions considering the relationships among Paleogene penguins, particularly regarding the possible existence of two separate clades including Palaeeudyptes and Paraptenodytes , the monophyly of Platydyptes and Paraptenodytes , and the position of Anthropornis . Additionally, Notodyptes wimani is here recovered in the same collapsed node as Archaeospheniscus and not within Delphinornis, as in former analyses.
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Authors and Affiliations

Martín Chávez Hoffmeister
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Abstract

Skin cancer is the most common form of cancer affecting humans. Melanoma is the most dangerous type of skin cancer; and early diagnosis is extremely vital in curing the disease. So far, the human knowledge in this field is very limited, thus, developing a mechanism capable of identifying the disease early on can save lives, reduce intervention and cut unnecessary costs. In this paper, the researchers developed a new learning technique to classify skin lesions, with the purpose of observing and identifying the presence of melanoma. This new technique is based on a convolutional neural network solution with multiple configurations; where the researchers employed an International Skin Imaging Collaboration (ISIC) dataset. Optimal results are achieved through a convolutional neural network composed of 14 layers. This proposed system can successfully and reliably predict the correct classification of dermoscopic lesions with 97.78% accuracy.

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Authors and Affiliations

Abeer Mohamed
Wael A. Mohamed
Abdel Halim Zekry
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Abstract

Pine wood nematode (Bursaphelenchus xylophilus) (Aphelenchida: Parasitaphelencidae) is one of the most harmful agents in coniferous forests. The most important vectors of pine wood nematode are considered to be some Monochamus species (Col.: Cerambycidae), which had been forest insects with secondary importance before the appearance of B. xylophilus. However, the continuous spreading of the nematode has changed this status and necessitated detailed biological and climatological investigation of the main European vector, Monochamus galloprovincialis. The potential distribution area of M. galloprovincialis involves those areas where the risk of the appearance of pine wood nematode B. xylophilus is significant. The main objective of our analysis was to obtain information about the influencing effects of North Atlantic Oscillation (NAO) on the potential European range of B. xylophilus and its vector species M. galloprovincialis based on the connection between the mean temperature of July in Europe, the distribution of day-degrees of the vector and the NAO index. Our assessment was based on fundamental biological constants of the nematode and the cerambycid pest as well as the ECMWF ERA5 Global Atmospheric Reanalysis dataset. Our hypothesis was built on the fact that the monthly mean temperature had to exceed 20°C in the interest of an efficient expansion of the nematode. In addition, the threshold temperature of the vector involved in the calculations was 12.17°C, while the accumulated day-degree (DD) had to exceed the annual and biennial 370.57°DD for univoltine and semivoltine development, respectively. Our finding that a connection could be found between a mean temperature in July above 20°C and NAO as well as between the accumulated day-degrees and NAO can be the basis for further investigations for a reliable method to forecast the expansion of pine wood nematode and its vector species in a given year.

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Authors and Affiliations

Katalin Somfalvi-Tóth
Sándor Keszthelyi
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Abstract

The limitation in approachability to rainfall data sources with an appropriate spatial-temporal distribution is a signifi-cant challenge in different parts of the world. The development of general circulation models and mathematical algorithms has led to the generation of various rainfall products as new sources with the potential to overcome the shortage in data-scarce basins. In this study, the performance of the PERSIANN-CCS and CMORPH satellite-based rainfall product, as well as the ERA5 and ERA-Interim reanalysis, was evaluated based on detection skill and quantitative metrics in a daily, month-ly and seasonal time scales in the Dez basin located in the southwest of Iran. The basin has a wide topographic variation and scattered rain gauge stations. Overall results denote that the ERA5 dataset has the best performance in all statistic veri-fication than other rainfall products. Based on the daily evaluation of all rainfall products, the false alarm rate (FAR) is higher than 0.5, so none of the datasets could capture the temporal variability of rainfall occurrence. This study has covered the western parts of the Zagros steep slopes in which the topographic conditions have a significant effect on the activity of rainfall systems. On a monthly scale, the mean value of the correlation coefficient (CC) for ERA5, ERA-Interim, PER-SIANN-CCS, and CMORPH was equal to 0.86, 0.85, 0.51, 0.39, respectively. The results of seasonal evaluation suggested that all datasets have better rainfall estimation in autumn and winter, and the capability of all datasets dramatically de-creased in the spring. The current paper argues that the ERA5 reanalysis typically outperforms ERA-Interim and can be considered as a reliable rainfall source in the future hydrological investigation in the southwest of Iran.
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Authors and Affiliations

Mostafa Khoshchehreh
1
ORCID: ORCID
Mehdi Ghomeshi
1
Ali Shahbazi
1
Hossein Bolboli
1
Hamed Saberi
2
Ali Gorjizade
1

  1. Shahid Chamran University of Ahvaz, Faculty of Water Science Engineering, Department of Water and Hydraulic Structures, Golestan Blvd., Ahvaz, 6135783151, Iran
  2. Khorramshahr University of Marine Science and Technology, Faculty of Engineering, Khorramshahr, Iran
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Abstract

Cyber-attacks are increasing day by day. The generation of data by the population of the world is immensely escalated. The advancements in technology, are intern leading to more chances of vulnerabilities to individual’s personal data. Across the world it became a very big challenge to bring down the threats to data security. These threats are not only targeting the user data and also destroying the whole network infrastructure in the local or global level, the attacks could be hardware or software. Central objective of this paper is to design an intrusion detection system using ensemble learning specifically Decision Trees with distinctive feature selection univariate ANOVA-F test. Decision Trees has been the most popular among ensemble learning methods and it also outperforms among the other classification algorithm in various aspects. With the essence of different feature selection techniques, the performance found to be increased more, and the detection outcome will be less prone to false classification. Analysis of Variance (ANOVA) with F-statistics computations could be a reasonable criterion to choose distinctives features in the given network traffic data. The mentioned technique is applied and tested on NSL KDD network dataset. Various performance measures like accuracy, precision, F-score and Cross Validation curve have drawn to justify the ability of the method.
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Authors and Affiliations

Shaikh Shakeela
1
N. Sai Shankar
1
P Mohan Reddy
1
T. Kavya Tulasi
1
M. Mahesh Koneru
1

  1. ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India
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Abstract

The article presents the simulation results of a single-pixel infrared camera image reconstruction obtained by using a convolutional neural network (CNN). Simulations were carried out for infrared images with a resolution of 80 × 80 pixels, generated by a low-cost, low-resolution thermal imaging camera. The study compares the reconstruction results using the CNN and the ℓ1 reconstruction algorithm. The results obtained using the neural network confirm a better quality of the reconstructed images with the same compression rate expressed by the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
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Authors and Affiliations

Sebastian Urbaś
1
ORCID: ORCID
Bogusław Więcek
1
ORCID: ORCID

  1. Institute of Electronics, Lodz University of Technology, Al. Politechniki 6, 90-924 Lodz, Poland

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