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Based on the geometry and structural concepts of calligraphy, this paper comprehensively considers the optimization of character structure and geometric lines and realizes the optimization of the echo relationship between strokes so as to obtain the design of Chinese characters that meet the aesthetic requirements of calligraphy. Based on the quantitative statistics of 3650 character components “structure body” and structural data, we summarize the finer structural guidance for design teaching. Using the calligraphy layout optimization method to optimize the calligraphy and its artistic design, the visual effect of the beautified Chinese characters can be observed. The findings indicate that the graphical design of Chinese character calligraphy is entirely based on geometric transformation. In the statistical analysis of the geometric structures of 3650 Chinese character calligraphy, there are 3391 types of geometric structures. The growth rate of the number of types gradually slows down when the structure depiction reaches the 4th layer. Therefore, the structure depiction should be divided into 8 layers, which can make the design teaching more refined. The optimized beautification degree of the logo design of a brand in the wine industry and the logo of the emblem of a sports meeting is more beautiful and aesthetically pleasing. Therefore, the method of this paper can achieve the beautification effect.

PurposeTo evaluate the utility of deep learning-based image reconstruction (DLIR) algorithm in unenhanced abdominal low-dose CT (LDCT).Materials and methodsTwo patient groups were included in this prospective study: 58 consecutive patients who underwent unenhanced abdominal standard-dose CT reconstructed with hybrid iterative reconstruction (SDCT group) and 48 consecutive patients who underwent unenhanced abdominal LDCT reconstructed with high strength level of DLIR (LDCT group). The background noise and signal-to-noise ratio (SNR) of the liver, pancreas, spleen, kidney, abdominal aorta, inferior vena cava, and portal vein were calculated. Two radiologists qualitatively assessed the overall image noise, overall image quality, and abdominal anatomical structures depiction. Quantitative and qualitative parameters and size-specific dose estimates (SSDE) were compared between SDCT and LDCT groups.ResultsThe background noise was lower in LDCT group than in SDCT group (P = 0.02). SNRs were higher in LDCT group than in SDCT group (P < 0.001–0.004) except for the liver. Overall image noise was superior in LDCT group than in SDCT group (P < 0.001). Overall image quality was not different between SDCT and LDCT groups (P = 0.25–0.26). Depiction of almost all abdominal anatomical structures was equal to or better in LDCT group than in SDCT group (P < 0.001–0.88). The SSDE was lower in LDCT group (4.0 mGy) than in SDCT group (20.6 mGy) (P < 0.001).ConclusionsDLIR facilitates substantial radiation dose reduction of > 75% and significantly reduces background noise. DLIR can maintain image quality and anatomical structure depiction in unenhanced abdominal LDCT.

Background The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them. The library implements a wide variety of cheminformatics algorithms ranging from chemical structure canonicalization to molecular descriptor calculations and pharmacophore perception. It is used in drug discovery, metabolomics, and toxicology. Over the last 10 years, the code base has grown significantly, however, resulting in many complex interdependencies among components and poor performance of many algorithms. Results We report improvements to the CDK v2.0 since the v1.2 release series, specifically addressing the increased functional complexity and poor performance. We first summarize the addition of new functionality, such atom typing and molecular formula handling, and improvement to existing functionality that has led to significantly better performance for substructure searching, molecular fingerprints, and rendering of molecules. Second, we outline how the CDK has evolved with respect to quality control and the approaches we have adopted to ensure stability, including a code review mechanism. Conclusions This paper highlights our continued efforts to provide a community driven, open source cheminformatics library, and shows that such collaborative projects can thrive over extended periods of time, resulting in a high-quality and performant library. By taking advantage of community support and contributions, we show that an open source cheminformatics project can act as a peer reviewed publishing platform for scientific computing software. Graphical abstract CDK 2.0 provides new features and improved performance

The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems. Graphical Abstract

We present pyPept, a set of executables and underlying python-language classes to easily create, manipulate, and analyze peptide molecules using the FASTA, HELM, or recently-developed BILN notations. The framework enables the analysis of both pure proteinogenic peptides as well as those with non-natural amino acids, including support to assemble a customizable monomer library, without requiring programming. From line notations, a peptide is transformed into a molecular graph for 2D depiction tasks, the calculation of physicochemical properties, and other systematic analyses or processing pipelines. The package includes a module to rapidly generate approximate peptide conformers by incorporating secondary structure restraints either given by the user or predicted via pyPept, and a wrapper tool is also provided to automate the generation and output of 2D and 3D representations of a peptide directly from the line notation. HELM and BILN notations that include circular, branched, or stapled peptides are fully supported, eliminating errors in structure creation that are prone during manual drawing and connecting. The framework and common workflows followed in pyPept are described together with illustrative examples. pyPept has been released at: https://github.com/Boehringer-Ingelheim/pyPept . Graphical Abstract

The paper examines settlements and mobility of their population; it examines the seasonal relocations of population of a specific area of Greece in the past when this type of relocations were systematic. It identifies settlements with seasonal migrations and outlines all interrelations between migration and settlement. A particular typology of the settlements is developed based on the character of the seasonal abandonment of the area source data originate from the census taken during the time studied. The percentage of population changes for each settlement reveals its character as regards the seasonal abandonment of the village; the results are mapped with the assistance of an appropriate geo-information system. The results of the mapping determine the area where historical transport routes are expected to be found. Additionally, they identify an area of further research into the socio-economic structure of the settlements and the cultural landscapes. The results complement the available literature and the research in subjects relating to the transportation of populations in the framework of human geography and dynamics in the framework of settlement geography. The typology of the settlements can contribute to the investigation of modern or historical phenomena of mobility and their depiction in the geographical space.

In emergencies, Twitter has become an essential way for people to communicate. Since many people use smartphones, they can easily report events they see. Because of this, more organizations are using Twitter to monitor what is happening digitally. Twitter and similar online platforms provide a place to share their personal experiences and local events with others. People often post about their daily lives, things happening in their neighborhoods, and their activities to keep others informed. In this study, we focus on predicting disasters on Twitter using an improved Bidirectional Encoder Depiction from the Transformers (BERT) model. Twitter has become a crucial communication tool during emergencies, allowing people to report events they witness quickly. As a result, organizations are increasingly using Twitter for digital monitoring. Our research utilizes a modified version of the BERT model, a powerful language representation technique. This modified BERT model enables us to understand better tweets related to disasters and improve the accuracy of our predictions. Using Twitter data, we train our improved BERT model to recognize patterns and signals associated with different types of disasters, such as natural calamities or accidents. We aim to efficiently detect and respond to potential disasters in real time by doing so. Our experiments' results demonstrate our BERT effectiveness, showing improved disaster prediction performance compared to traditional models like the long short-term memory model (LSTM) and gated recurrent unit model (GRU). This research contributes valuable insights into disaster management strategies and allows leveraging social media data in disaster preparedness and response efforts.

Tanzimat dönemi yazarlarının ikinci kuşağı arasında yer alan Sami Paşazade Sezai, edebiyata olan ilgisini çocukluk yıllarında kazanmış önemli bir edebi şahsiyettir. Bu ilginin temel kaynağı, doğup büyüdüğü konakta babası Sami Paşa’nın, dönemin edebi ve ilmî simalarıyla gerçekleştirdiği nitelikli sohbetlerdir. Bu entelektüel ortamlarda yetişen Sezai, henüz on dört yaşında ilk eserini kaleme alarak edebi hayatına erken yaşta adım atmıştır. Zaman içerisinde edebi yetkinliğini artırmış ve özellikle Sergüzeşt adlı romanıyla büyük yankı uyandırmıştır. Söz konusu eser, dönemin toplumsal meselelerine, özellikle de kölelik kurumuna eleştirel bir bakış sunması bakımından dikkat çekicidir. Ayrıca, gündelik hayatın sıradan fakat anlam yüklü ayrıntılarını öne çıkardığı Küçük Şeyler adlı hikâye kitabıyla da Türk edebiyatında kendine özgü bir yer edinmiştir. Yazarlık hayatının ilk dönemlerinde Namık Kemal’in etkisiyle romantik ve coşkulu bir söylem benimseyen Sezai, zamanla Batı edebiyatını tanıması ve pozitivist düşünürlerin metinlerini okumasıyla birlikte realizme yönelmiştir. Sergüzeşt romanında romantizm ile realizm arasında geçişken bir anlatım tarzı gözlemlenirken, Küçük Şeyler hikâyelerinde realizmin hâkim üslup olarak benimsendiği görülmektedir. Özellikle mekân tasvirlerinde realizmin tüm imkânlarını ustalıkla kullanan yazar, resim sanatına duyduğu ilginin de etkisiyle insanı, çevreyi ve doğayı ayrıntılı ve canlı betimlemelerle ele alır. Sami Paşazade Sezai, yüksek tabakadan bir aileye mensup olmasına karşın, hikâyelerinde toplumun alt sınıflarına mensup karakterlere yer vermeyi tercih etmiştir. Hikâyelerinde yer alan tipler; hizmetçiler, cariyeler, küçük memurlar ve halk arasında karşılaşılabilecek sade bireylerden oluşmaktadır. Yazar, çoğu hikâyesinde tek bir olayı veya bireyi öne çıkarmaktan ziyade, merak unsuru taşıyan yapıyı hikâyenin geneline yayar. Bu bağlamda, ayrıntıya verdiği önem ve derinlikli tasvirleriyle, sıradan olanı görünür kılar ve hikâye sanatındaki ustalığını ortaya koyar. Bu çalışmada, Küçük Şeyler adlı eserde yer alan tasvirlerin biçimsel özellikleri, hangi edebi akımın niteliklerini taşıdığı ve söz konusu tasvirlerin karakterlerle olan ilişkisi analiz edilecektir.

Recent works on Non-Photorealistic Rendering (NPR) show that object shape enhancement requires sophisticated effects such as: surface details detection and stylized shading. To date, some rendering techniques have been proposed to overcome this issue, but most of which are limited to correlate shape enhancement functionalities to surface feature variations. Therefore, this problem still persists especially in NPR. This paper is an attempt to address this problem by presenting a new approach for enhancing shape depiction of 3D objects in NPR. We first introduce a tweakable shape descriptor that offers versatile functionalities for describing the salient features of 3D objects. Then to enhance the classical shading models, we propose a new technique called Geometry-based Shading. This technique controls reflected lighting intensities based on local geometry. Our approach works without any constraint on the choice of material or illumination. We demonstrate results obtained with Blinn-Phong shading, Gooch shading, and cartoon shading. These results prove that our approach produces more satisfying results compared with the results of previous shape depiction techniques. Finally, our approach runs on modern graphics hardware in real time, which works efficiently with interactive 3D visualization.

The ripeness estimation of fruits plays a significant role in marketing and evaluation of quality. However, due to the subjectivity and slow speed in the case of manual assessment, the agriculture industry leads to the need for automation. In this research work, an efficient ANFIS based Pre-harvest Ripeness Estimation (APRE) technique is proposed for the ripeness estimation of fruits based on color. There are three main phases of the proposed work: Data Processing, Feature Selection and Fuzzy Logic Implementation. In the first phase, the data set of images of fruits is prepared in the image acquisition phase. Then images are pre-processed to make them equal in size. In Image Segmentation phase, a fruit is extracted from its background. In the next phase, two color features: red-green color difference and red-green color ratio are calculated based on extracted RGB color attributes and R-G is chosen based on performance comparison in terms of classification accuracy. In phase three, Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized for designing and implementing the classification system which classifies the fruits into six ripeness phases. The experimental results show that the APRE performs better than SVM, Decision Tree, and KNN in terms of accuracy, precision, recall, sensitivity and F-measure.