Professional publishing and Lithuanian books in the first two-thirds of the 19th century in Lithuania: The case of the Zawadzki firm

Successfully profiting from textbook publishing as the typographer at Vilnius University, Józef Zawadzki (1781‒1838) established one of the most important and most successful book publishing, production, and distribution companies of the 19th and the first half of the 20th century in the territories of former Polish-Lithuanian Commonwealt. The Zawadzki firm represented the new category of professional publishers. Over the first seventy years of the firm’s existence at the firm’s expense were published 122 Lithuanian books, as well as printed about 50 Lithuanian publications at their authors finances. The attitude of the members of Zawadzki family regarding the publishing of Lithuanian books essentially changed. While the Józef Zawadzki was indifferent towards them, in the middle of the 19th century Adam Zawadzki (1814‒1875) outcompeted other professional book publishers and distributors, and monopolized the publishing of Lithuanian books in the Samogitian Diocese, becoming not only the most important publisher of Lithuanian books, but also their printer and distributor. The successful realization of Adam Zawadzki’s business plans was partly due to his longstanding contacts with the most active figures of Lithuanian written culture, with whom he maintained a new form of cooperation based on authorial royalties, partly due effectively distribution of published matter, using first stationary bookstore in the periphery, located in the west of Lithuania (which served as a retail and wholesale trade enterprise), various ways of non-stationary book trade, services of a commercial library and advertising. Owing to Adam’s efforts, the Zawadzki firm made a significant contribution uniting main forces of authors and publishers of Lithuanian books in 19th century Lithuania, renewing the repertoire of Lithuanian books, as well as giving Lithuanian book publishing, production and distribution features characteristic for a modern business. A model of dealing with censorship through illegal publishing, developed with the publication Apej brostwą błaiwistes arba nusiturieima, was used throughout all the forty years of the press ban and helped raising several generations of literate Lithuanians and bringing Lithuania and Lithuania Minor closer together.

A two-sided gaming model for large-scale stable matching in sharing economy based on the probabilistic linguistic term sets

Sharing economy is significant for economic development, stable matching plays an essential role in sharing economy, but the large-scale sharing platform increases the difficulties of stable matching. We proposed a two-sided gaming model based on probabilistic linguistic term sets to address the problem. Firstly, in previous studies, the mutual assessment is used to obtain the preferences of individuals in large-scale matching, but the procedure is time-consuming. We use probabilistic linguistic term sets to present the preferences based on the historical data instead of time-consuming assessment. Then, to generate the satisfaction based on the preference, we regard the similarity between the expected preferences and actual preferences as the satisfaction. Considering the distribution features of probabilistic linguistic term sets, we design a shape-distance-based method to measure the similarity. After that, the previous studies aimed to maximize the total satisfaction in matching, but the individuals’ requirements are neglected, resulting in a weak matching result. We establish the two-sided gaming matching model from the perspectives of individuals based on the game theory. Meanwhile, we also study the competition from other platforms. Meanwhile, considering the importance of the high total satisfaction, we balance the total satisfaction and the personal requirements in the matching model. We also prove the solution of the matching model is the equilibrium solution. Finally, to verify the study, we use the experiment to illustrate the advantages of our study.

Characterization and Prediction of Dengue Virus Targeting Peptides Based on Combined Amino Acid Composition Descriptors Using Random Forest Algorithm.

Dengue virus peptides are emerging as potential therapeutics for dengue infection. Due to the important role of dengue peptides in curbing dengue infection, their identification has proven crucial in terms of infection biology. To calculate differences between amino acids and physiochemical attributes, statistical tests and F-scores were used in this work. The random forest algorithm was used to predict dengue peptides using grouped amino acid composition, transition and distribution. Here, we have used three descriptors; Amino acid content, Grouped Amino acid composition and Composition, transition and distribution features (CTDC). We have created models and compared with combined model. Using the grouped amino acid composition as input parameters for the random forest algorithm, Our classifier's overall accuracy increased to 88.80%, which was the greatest overall accuracy found in this investigation. Our classifier produced superior predicting outcomes when compared to previously developed algorithms. In conclusion, we looked at the differences in amino acids and physiochemical properties between dengue viral peptides, using the grouped amino acid composition to build a classifier that predicts these dengue virus inhibitory peptides.

An Analysis of Interpersonal Meaning of Modal Verbs in Abstracts from Scientific Papers: Case Study of Progress in Aerospace Sciences

The aims of this study are to explore the distribution features of modal verbs in abstracts from scientific papers, analyze the reasons for those features and figure out the role modal verbs play in achieving interpersonal meaning. The study has selected 60 abstracts randomly from Progress in Aerospace Sciences from 2015 to 2019 as research samples. It combines Halliday’s value of modality with Biber et al.’s two classifications to process modal verbs. The instruments adopted in this research are AntConc 3.2.4, manual sorting and SPSS Statistics 21. Based on the results, the study finds that: firstly, the overall occurrence of modal verbs in 60 abstracts is 59 times, among which low-value modal verbs are the most frequently distributed (74.6%), median-value modal verbs the second (22.0%) and high-value modal verbs the least (3.4%); secondly, the achievement of interpersonal meaning relies on value of modality, and when the value of modality is lower, a better degree of interpersonal meaning can be achieved; thirdly, high-value modal verbs achieve tough interpersonal meaning, median-value modal verbs achieve comfortable interpersonal meaning, and low-value modal verbs achieve harmonious interpersonal meaning.

3D Model Classification Based on Bayesian Classifier with AdaBoost

With the exponential increasement of 3D models, 3D model classification is crucial to the effective management and retrieval of model database. Feature descriptor has important influence on 3D model classification. Voxel descriptor expresses surface and internal information of 3D model. However, it does not contain topological structure information. Shape distribution descriptor expresses geometry relationship of random points on model surface and has rotation invariance. They can all be used to classify 3D models, but accuracy is low due to insufficient description of 3D model. This paper proposes a 3D model classification algorithm that fuses voxel descriptor and shape distribution descriptor. 3D convolutional neural network (CNN) is used to extract voxel features, and 1D CNN is adopted to extract shape distribution features. AdaBoost algorithm is applied to combine several Bayesian classifiers to get a strong classifier for classifying 3D models. Experiments are conducted on ModelNet10, and results show that accuracy of the proposed method is improved.

Hemodynamic Characteristics of Dilated Ascending Aorta in Patients with Bicuspid Aortic Valve

Objective: Aim of this study is to investigate the influence of aortic diameter on hemodynamic environment characteristics in patient with the bicuspid aortic valve (BAV) and dilated ascending aorta (AAo) .Methods: In this study, an MRI of one BAV patient with 4.5 cm AAo was collected and numerical model was constructed. Based on the images，the other three numerical models were constructed with different ascending aortic size with 4.0cm, 5.0cm and 5.5cm respectively while the size and the geometry of other parts were fixed. Then hemodynamics in these four models was simulated numerically and the flow patterns and loading distributions were investigated.Results: Hemodynamics environments in the AAo were simulated with different aortic size. As the aortic diameter increases, we find: 1. the blood flow becomes more disturbing;2.the wall pressure at ascending aortic is higher; 3. the wall shear stress at the ascending aortic decreases; 4.oscillatory shear index of the outer part on the proximal AAo increases;5. all these hemodynamic parameters described above are asymmetrically distributed in dilated AAo and more parts of aorta would be affected as the AAo dilatation progresses.Conclusions: The study revealed that the diameter of ascending aortic can significantly influence the magnitude and distribution of the dynamics. There are altered flow patterns, pressure difference, WSS and OSI distribution features in bicuspid aortic valve patients with vascular dilatation. As the extent of aortic dilatation increases especially exceed 5.5cm,this study support the recent guideline that aortic replacement should be considered .

Optimal Differential Pricing for Intercity High-Speed Railway Services with Time-Dependent Demand and Passenger Choice Behaviors under Capacity Constraints

Differential pricing of trains with different departure times caters to the taste heterogeneity of the time-dependent (departure time) demand and then improves the ticket revenue of railway enterprises. This paper studies optimal differential pricing for intercity high-speed railway services. The distribution features of the passenger demand regarding departure times are analyzed, and the time-dependent demand is formulated; a passenger assignment method considering departure periods and capacity constraints is constructed to evaluate the prices by simulating the ticket-booking process. Based on these, an optimization model is constructed with the aim of maximizing the ticket revenue and the decision variables for pricing train legs. A modified direct search simulated annealing algorithm is designed to solve the optimization model, and three random generation methods of new solutions are developed to search the solution space efficiently. Experimental analysis containing dozens of trains is performed on Wuhan-Shenzhen high-speed railway in China, and price solutions with different elastic demand coefficients ( ϕ ) are compared. The following results are found: (i) the optimization algorithm converges stably and efficiently and (ii) differentiation is shown in the price solutions, and the optimized ticket revenue is influenced greatly by ϕ , increasing by 7%–21%.

Target group distribution pattern analysis with bagged convolutional neural networks for UAV distribution pattern identification

Purpose The purpose of this study is to address the limitations of existing target group distribution pattern analysis methods and identify subtle distribution differences within and between the groups with no pre-specified distribution features. Classical work generally concentrates on either the group distribution tendency or shape as a whole and simply ignores the subtle distribution differences within the group. Other work is constrained to pre-defined spatial distribution features. Design/methodology/approach This study proposes a novel algorithm for target group distribution pattern analysis. This study first transforms the group distribution data with uncertain measurements into a distributional image. Upon that, a bagged convolutional neural network model is constructed to discriminate the delicate group distribution patterns. Findings Experimental results indicate that our method is robust to target missing and location variance and scalable with dataset size. Our method has outperformed the benchmark machine learning methods significantly in pattern identification accuracy. Originality/value Our method is applicable for complex unmanned aerial vehicle distribution pattern identification.