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

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

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

Hybrid additive manufacturing of an electron beam powder bed fused Ti6Al4V by transient liquid phase bonding

Hybrid Additive Manufacturing (HAM) is a production strategy enhancing the flexibility of the already versatile Additive Manufacturing (AM) techniques. AM of Ti6Al4V, on the other hand, has been of great interest to numerous research works, thanks to the unique corrosion, biomedical and mechanical properties of the alloy. Hence, this research marks the first report on the HAM of Ti6Al4V by Transient Liquid Phase (TLP) bonding of an Electron Beam Powder Bed Fused (EB-PBF) sample to a conventional one. A copper interlayer was used for bonding, and the TLP process was performed at 890 degrees C and 970 degrees C for 60 min. Shear strength test was carried out and the results showed the highest shear strengths of 579.3 and 662.5 MPa for TLP bonding at 890 degrees C and 970 degrees C, respectively. By increasing the bonding temperature to 970 degrees C, no Cu-rich phases were observed in the microstructure, as opposed to the 890 degrees C samples, and a complete isothermal solidification without intermetallic phases was achieved. Moreover, the 970 degrees C TLP sample was featured with a much better microstructural integrity and homogeneity in both the base metals and the bonded zone. TLP bonding at 970 degrees C resulted in a more ductile fracture surface than that bonded at 890 degrees C. The strong differences between the two TLP bonds were primarily attributed to the faster diffusion rate of elements along the joint and base metal at higher temperatures. (C) 2022 The Author(s). Published by Elsevier B.V

Predicting the Performance of a Computing System with Deep Networks

Predicting the performance and energy consumption of computing hardware is critical for many modern applications. This will inform procurement decisions, deployment decisions, and autonomic scaling. Existing approaches to understanding the performance of hardware largely focus around benchmarking – leveraging standardised workloads which seek to be representative of an end-user’s needs. Two key challenges are present; benchmark workloads may not be representative of an end-user’s workload, and benchmark scores are not easily obtained for all hardware. Within this paper, we demonstrate the potential to build Deep Learning models to predict benchmark scores for unseen hardware. We undertake our evaluation with the openly available SPEC 2017 benchmark results. We evaluate three different networks, one fully-connected network along with two Convolutional Neural Networks (one bespoke and one ResNet inspired) and demonstrate impressive 2 scores of 0.96, 0.98 and 0.94 respectively

Diagnosis of multiple sclerosis by detecting asymmetry within the retina using a similarity-based neural network

Multiple sclerosis (MS) is a chronic neurological disorder that targets the central nervous system, causing demyelination and neural disruption, which can include retinal nerve damage leading to visual disturbances. The purpose of this study is to demonstrate the capability to automatically diagnose MS by detecting asymmetry within the retina, using a similarity-based neural network, trained on optical coherence tomography images. This work aims to investigate the feasibility of a learning-based system accurately detecting the presence of MS, based on information from pairs of left and right retina images. We also justify the effectiveness of a Siamese Neural Network for our task and present its strengths through experimental evaluation of the approach. We train a Siamese neural network to detect MS and assess its performance using a test dataset from the same distribution as well as an out-of-distribution dataset, which simulates an external dataset captured under different environmental conditions. Our experimental results demonstrate that a Siamese neural network can attain accuracy levels of up to 0.932 using both an in-distribution test dataset and a simulated external dataset. Our model can detect MS more accurately than standard neural network architectures, demonstrating its feasibility in medical applications for the early, cost-effective detection of MS