Ethnizität, Differenz und Hybridität in der Migration: Eine postkoloniale Perspektive

German society, nowadays, is marked by postcolonial immigration. This article tries to reconstruct ethnicity as historically based cultural identity that is not only open to the narration of collective experiences, but also to the recognition of difference, ambivalence, and change. These terms are also key-concepts in the post-colonial discourse of Anglo-American Cultural Studies, when culture and identity are discussed. Without the security of essentialist guarantee, but with the notion of ethnicity, that is devoted to different voices, the post-colonial critique tries to conceive of a political strategy, where marginalization is revalorised and the hybrid culture of the „borderlands” is promoted

An Effective Metaheuristic for Multiple Traveling Repairman Problem with Distance Constraints

Multiple Traveling Repairman Problem with Distance Constraints (MTRPD) is an extension of the NP-hard Multiple Traveling Repairman Problem. In MTRPD, a fleet of identical vehicles is dispatched to serve a set of customers with the following constraints. First, each vehicle's travel distance is limited by a threshold. Second, each customer must be visited exactly once. Our goal is to find the visiting order that minimizes the sum of waiting times. To solve MTRPD we propose to combine the Insertion Heuristic (IH), Variable Neighborhood Search (VNS), and Tabu Search (TS) algorithms into an effective two-phase metaheuristic that includes a construction phase and an improvement phase. In the former phase, IH is used to create an initial solution. In the latter phase, we use VNS to generate various neighborhoods, while TS is employed to mainly prohibit from getting trapped into cycles. By doing so, our algorithm can support the search to escape local optima. In addition, we introduce a novel neighborhoods’ structure and a constant time operation which are efficient for calculating the cost of each neighboring solution. To show the efficiency of our proposed metaheuristic algorithm, we extensively experiment on benchmark instances. The results show that our algorithm can find the optimal solutions for all instances with up to 50 vertices in a fraction of seconds. Moreover, for instances from 60 to 80 vertices, almost all found solutions fall into the range of 0.9 %-1.1 % of the optimal solutions' lower bounds in a reasonable duration. For instances with a larger number of vertices, the algorithm reaches good-quality solutions fast. Moreover, in a comparison to the state-of-the-art metaheuristics, our proposed algorithm can find better solutions

Dynamic characteristics of elastically supported beam subjected to a compressive axial force and a moving load

This paper discusses the dynamic characteristics of an elastically supported Euler-Bernoulli beam subjected to an initially loaded compressive force and a moving point load. The eccentricity of the axial force is taken into consideration. The time-histories for beam deflection and the dynamic magnification factors are computed by using the Galerkin finite element method and the implicit Newmark method. The effects of decelerated and accelerated motions on the dynamic characteristics are also examined. The influence of the axial force, eccentricity and the moving load parameters on the dynamic characteristics of the beams is investigated and highlighted

PROVISION CAPACITY OF SERVICE DELIVERY FACILITIES FOR CHILDREN WITH HEARING LOSS IN HAI PHONG, VIETNAM

Objective: Hearing loss is a commonly occurring disability that affects 466 million people worldwide. This study aimed at determining the actual situations of early intervention delivery facilities for children with hearing loss. Out of this affected population, 7% are children (34 million) who, along with their families, grapple with the serious lifelong problems that accompany the disease. Methods: This descriptive cross-sectional study was conducted with facilities investigated consisting of a school for the deaf, hospitals, an audiology center, and a social agency in Hai Phong province from January 2013 to December 2014. A sample composed of 353 children was also recruited. Results: The examined facilities suffer from shortcomings in provision capacity, which manifest in deficient materials, supplies and equipment, and human resources; the lack of interdisciplinary coordination of activities; inadequate community awareness; and the unaddressed need for early detection and intervention. The conditions of most of the children (98%) were detected by their families, and among those who were clinically diagnosed, the majority (76.8%) received such diagnosis at central hospitals. Hearing impairment among the children were detected, diagnosed, and subjected to intervention at a very late stage (on average, at ages 22.3, 34, and 32.5 months, respectively), thereby compelling up to 63.6% of the families to struggle with their children’s hearing loss. Conclusion: Solutions to current interventions are needed to enhance service delivery systems and guarantee early detection as well as timely and appropriate treatment

Vietnamese Word Segmentation with CRFs and SVMs: An Investigation

PACLIC 20 / Wuhan, China / 1-3 November, 200

Dynamic behavior of nonuniform functionally graded Euler-Bernoulli beams under multiple moving forces

The dynamic behavior of nonuniform Euler-Bernoulli beams made of transversely functionally graded material under multiple moving forces is studied by the finite element method. The beam cross-section is assumed to vary in the width direction by two different types. A simple finite element formulation, accounting for variation of the material properties through the beam thickness  and the shift in the physically neutral surface, is derived and employed in the study. The exact variation of the cross-sectional profile is employed in evaluation of the element stiffness and mass matrices. The dynamic response of the beam is computed with the aid of the implicit Newmark method. The numerical results show that the derived finite element formulation is capable to assess accurately the dynamic characteristics of the beam by using just several elements. The effect of the moving speed, material inhomogeneity and section profile on the dynamic behavior of the beams is investigated. The influence of the distance between the forces as well as the number of forces on the dynamic response is also examined and highlighted

Attentive Deep Neural Networks for Legal Document Retrieval

Legal text retrieval serves as a key component in a wide range of legal text processing tasks such as legal question answering, legal case entailment, and statute law retrieval. The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents. Based on good representations, a legal text retrieval model can effectively match the query to its relevant documents. Because legal documents often contain long articles and only some parts are relevant to queries, it is quite a challenge for existing models to represent such documents. In this paper, we study the use of attentive neural network-based text representation for statute law document retrieval. We propose a general approach using deep neural networks with attention mechanisms. Based on it, we develop two hierarchical architectures with sparse attention to represent long sentences and articles, and we name them Attentive CNN and Paraformer. The methods are evaluated on datasets of different sizes and characteristics in English, Japanese, and Vietnamese. Experimental results show that: i) Attentive neural methods substantially outperform non-neural methods in terms of retrieval performance across datasets and languages; ii) Pretrained transformer-based models achieve better accuracy on small datasets at the cost of high computational complexity while lighter weight Attentive CNN achieves better accuracy on large datasets; and iii) Our proposed Paraformer outperforms state-of-the-art methods on COLIEE dataset, achieving the highest recall and F2 scores in the top-N retrieval task.Comment: Preprint version. The official version will be published in Artificial Intelligence and Law journa

Impact of eco-innovation and sustainable tourism growth on the environmental degradation: the case of China

Climate complexities and global warming have made sustainable development a customary topic in environmental literature. Since then, various diggings have been happening in academia. Amongst them tourism and eco-innovation receives the heap due to its contribution to economic development. The study, thereby, examines the impact of tourism, economic growth and eco-innovation on environmental degradation in China. The secondary data has been extracted from World Development Indicators (WDI) database from 1988 to 2020. The nexus among the variables have been examined using Nonlinear Autoregressive Distributed Lagged (NARDL) model. Findings reveal that international tourism receipts, expenditures and number of tourist arrival, GDP, national income and inflation are positively correlated with environmental degradation, while sustainability- oriented eco-innovation is related negatively in case of China. This study has provided help to the regulators while developing new policies regarding environmental degradation by controlling emissions from economic and tourism development and using sustainability- oriented eco-innovation

Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm

The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people’s safety in many countries; therefore, modeling and forecasting the hydropower dam’s deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the defor mations of the hydropower dam. The second-largest hydropower dam of Viet nam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to opti mize the three parameters of the LSTM, the number of hidden layers, the learn ing rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art bench marks, sequential minimal optimization for support vector regression, Gaussian process, M5’ model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural net work. The result shows that the proposed CVOA-LSTM model has high fore casting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam’s deforma tions.Ministerio de Ciencia, Innovación y Universidades PID2020-117954RB-C2