Topological entanglement complexity of systems of polygons and walks in tubes

In this thesis, motivated by modelling polymers, the topological entanglement complexity of systems of two self-avoiding polygons (2SAPs), stretched polygons and systems of self-avoiding walks (SSAWs) in a tubular sublattice of Z3 are investigated. In particular, knotting and linking probabilities are used to measure a polygon fs selfentanglement and its entanglement with other polygons respectively. For the case of 2SAPs, it is established that the homological linking probability goes to one at least as fast as 1-O(n^(-1/2)) and that the topological linking probability goes to one exponentially rapidly as n, the size of the 2SAP, goes to infinity. For the case of stretched polygons, used to model ring polymers under the influence of an external force f, it is shown that, no matter the strength or direction of the external force, the knotting probability goes to one exponentially as n, the size of the polygon, goes to infinity. Associating a two-component link to each stretched polygon, it is also proved that the topological linking probability goes to unity exponentially fast as n → ∞. Furthermore, a set of entangled chains confined to a tube is modelled by a system of self- and mutually avoiding walks (SSAW). It is shown that there exists a positive number γ such that the probability that an SSAW of size n has entanglement complexity (EC), as defined in this thesis, greater than γn approaches one exponentially as n → ∞. It is also established that EC of an SSAW is bounded above by a linear function of its size. Using a transfer-matrix approach, the asymptotic form of the free energy for the SSAW model is also obtained and the average edge-density for span m SSAWs is proved to approach a constant as m → ∞. Hence, it is shown that EC is a ggood h measure of entanglement complexity for dense polymer systems modelled by SSAWs, in particular, because EC increases linearly with system size, as the size of the system goes to infinity

Trees whose 2-domination subdivision number is 2

A set $S$ of vertices in a graph $G = (V,E)$ is a $2$-dominating set if every vertex of $V\setminus S$ is adjacent to at least two vertices of $S$. The $2$-domination number of a graph $G$, denoted by $\gamma_2(G)$, is the minimum size of a $2$-dominating set of $G$. The $2$-domination subdivision number $sd_{\gamma_2}(G)$ is the minimum number of edges that must be subdivided (each edge in $G$ can be subdivided at most once) in order to increase the $2$-domination number. The authors have recently proved that for any tree $T$ of order at least $3$, $1 \leq sd_{\gamma_2}(T)\leq 2$. In this paper we provide a constructive characterization of the trees whose $2$-domination subdivision number is $2$

Generalized α − ψ-geraghty multivalued mappings on b-metric spaces endowed with a graph

In this paper, we provide some conditions for the existence of a coincidence point of single-valued and multivalued mappings involving generalized α − ψ-Geraghty contractions endowed with a graph. Our main results improve the existing results in the corresponding literature. We also present examples to support the obtained results.Publisher's Versio

Measuring Research Productivity of LIS Departments in the Middle East

The present study measures research productivity of library and Information science departments in the Middle East. Data were collected from 16 countries whose LIS departments had at least one article indexed in Clarivate Analytics Web of Science between 2014 and 2018. Journals’ Citation Report was also used to collect further data. In measuring research productivity, the number of departments’ articles indexed in the Web of Science database and the size of each department (number of faculties) is considered as output and input, respectively. Findings indicated that Bar Ilan University had the highest research productivity (3.7), followed by Shiraz University (1.17) and Haceteppe University (1.04). With respect to LIS Department Research Productivity    Occupied Palestine, Turkey, Jordan, Kuwait and Iran ranked first to fifth respectively. The results of this research not only can contribute towards identifying highly productive and influential departments, but could also lay the groundworks for a well oriented scientific policy and cooperation.https://dorl.net/dor/20.1001.1.20088302.2022.20.2.3.

The study of relationship between transformational leadership style and organizational performance in state offices of Sirjan City based on EFQM model

The present study is conducted to investigate the relationship between transformational leadership style and organizational performance in governmental offices of Sirjan city based on the EFQM model. The study has applied purpose and in terms of nature and method is descriptive - correlational. The statistical population includes all government staff in Sirjan city in 2014 that is more than 1815 people. According to Morgan table, among them, 317 subjects were selected by random class sampling method. The standardized questionnaire of transformational leadership style with 20 questions was used for data collection and standardized questionnaire of Quality Excellence Model of Europe with 42 questions was used for organizational performance. Both questionnaires had acceptable validity and their reliability based on Cronbach's alpha test was obtained to be 0.961 for transformational leadership style and 0.95 for organizational performance. Descriptive statistic and inferential statistic methods were used to analyze the data. The results of data analysis showed a significant and positive relationship between transformational leadership style and all dimensions with organizational performance in governmental offices of Sirjan city based on the EFQM model

HINT: High-quality INpainting Transformer with Mask-Aware Encoding and Enhanced Attention

Existing image inpainting methods leverage convolution-based downsampling approaches to reduce spatial dimensions. This may result in information loss from corrupted images where the available information is inherently sparse, especially for the scenario of large missing regions. Recent advances in self-attention mechanisms within transformers have led to significant improvements in many computer vision tasks including inpainting. However, limited by the computational costs, existing methods cannot fully exploit the efficacy of long-range modelling capabilities of such models. In this paper, we propose an end-to-end High-quality INpainting Transformer, abbreviated as HINT, which consists of a novel mask-aware pixel-shuffle downsampling module (MPD) to preserve the visible information extracted from the corrupted image while maintaining the integrity of the information available for highlevel inferences made within the model. Moreover, we propose a Spatially-activated Channel Attention Layer (SCAL), an efficient self-attention mechanism interpreting spatial awareness to model the corrupted image at multiple scales. To further enhance the effectiveness of SCAL, motivated by recent advanced in speech recognition, we introduce a sandwich structure that places feed-forward networks before and after the SCAL module. We demonstrate the superior performance of HINT compared to contemporary state-of-the-art models on four datasets, CelebA, CelebA-HQ, Places2, and Dunhuang

Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets

Skin cancer is the most common malignancy in the world. Automated skin cancer detection would significantly improve early detection rates and prevent deaths. To help with this aim, a number of datasets have been released which can be used to train Deep Learning systems - these have produced impressive results for classification. However, this only works for the classes they are trained on whilst they are incapable of identifying skin lesions from previously unseen classes, making them unconducive for clinical use. We could look to massively increase the datasets by including all possible skin lesions, though this would always leave out some classes. Instead, we evaluate Siamese Neural Networks (SNNs), which not only allows us to classify images of skin lesions, but also allow us to identify those images which are different from the trained classes - allowing us to determine that an image is not an example of our training classes. We evaluate SNNs on both dermoscopic and clinical images of skin lesions. We obtain top-1 classification accuracy levels of 74.33% and 85.61% on clinical and dermoscopic datasets, respectively. Although this is slightly lower than the state-of-the-art results, the SNN approach has the advantage that it can detect out-of-class examples. Our results highlight the potential of an SNN approach as well as pathways towards future clinical deployment.Comment: 10 pages, 5 figures, 5 table