Abstract

High entropy alloys (HEAs), which are multi-component alloys having constituent elements in equi-atomic or near equi-atomic ratios, are receiving immense attention owing to their remarkable mechanical and physical properties. These unusual properties depend on one or more of the phases that these alloy systems constitute, namely solid solution (SS), intermetallic compound (IM), and amorphous (AM) phases. Therefore, phase prediction is crucial in selecting appropriate elements that lead to the formation of a HEA with desirable properties. In this work, machine learning (ML) models are used on a set of design parameters using multi-classification for HEA phase prediction. The ML models comprised ensemble-based (Random Forest and Stacked ensemble) and support vector machine (SVM) methods. To predict solid solution phase formation, researchers used atomic size difference and the parameter where is average melting point, is entropy of mixing and is mixing enthalpy. The features used in this study are , atomic size difference, average electron concentration, electronegativity difference, average melting point, mean atomic radius, mixing enthalpy, the entropy of mixing, and bulk modulus. The training dataset comprising of 601 as cast alloys is used with cross-validation for test data, and the phases SS, IM, AM, SS + IM, and IM + AM are predicted. The phase prediction accuracies are calculated using one-vs-rest, and precision-recall curves are plotted to determine the model performance. For phases AM, SS, and IM, the stacked ensemble displayed better accuracies when compared to SVM and Random Forest. The findings indicate that the stacked ensemble, which combines weak learners and meta-models, provides accuracy comparable to the neural network model accuracy reported in the literature. These findings provide insights to researchers and practitioners in selecting features and ML models while designing HEAs.

Abstract

Web of Things (WoT) extends the Internet of Things (IoT) paradigm to facilitate communications among smart things/devices and Web-based applications. In other words, WoT systems generally provide a Web interface for the monitoring and controlling of smart devices over the Web, for example, in applications, such as home automation, intelligent transportation system, smart healthcare, smart cities, and smart agriculture. However, this results in the generation of significant volume of data (i.e., big data) and, hence, the importance of big data analytics. There are also associated security and privacy implications. Therefore, in this article, we present a signature-based authentication and key agreement scheme for the WoT environment and prove its security. We also evaluate the performance of SCS-WoT and compare it against four other competing schemes. The findings show that SCS-WoT achieves better performance in terms of communication cost, computational cost, and security and functionality.

Abstract

The Smoothened receptor (SMO, a 7 pass transmembrane domain, Class F GPCR family protein) plays a crucial role in the Hedgehog (HH) signaling pathway, which is involved in embryonic development and is implicated in various types of cancer throughout the animal kingdom. In the absence of HH signaling, SMO is inhibited by Patched 1 (PTC1; a 12 pass transmembrane domain protein), which is localized in the primary cilia. HH binding leads to the dislocation of PTC1 from the cilia, thus making way for SMO to localize in the primary cilia, as an essential prerequisite for its activation. We have carried out MARTINI coarse-grained molecular dynamics simulations of SMO in POPC and in ciliary membrane models, respectively, to study the interactions of SMO with cholesterol and other lipid molecules in the ciliary membrane, and to gain molecular-level insights into the role of the primary cilia in shaping the functional dynamics of SMO. We are able to identify the interaction of membrane cholesterols with definite sites and domains within SMO and relate them with known cholesterol-binding sequence and structure motifs. We show that cholesterol interactions with the transmembrane domain TMD, unlike those with the cysteine-rich domain (CRD) and the intracellular domain (ICD), are through residues belonging to known cholesterol-binding motifs. Notably, a few persistent interactions of cholesterol with lower TM cholesterol-binding domains are governed by the presence of multiple cholesterol-binding motifs. These analyses have also helped to identify and define a strict cholesterol consensus motif (CCM), which may well steer cholesterol into the hitherto identified binding sites within the TMD of SMO. We have also reported the interaction of phosphatidylinositol 4-phosphate with the intracellular region of transmembrane (TM) helices (TM1, TM3, TM4, and TM5), intracellular loop1, helix8, and Arg/Lys clusters of the ICD. Structural analysis of SMO domains shows significant changes in the CRD and ICD, during the course of the simulation. Further detailed analysis of the dynamics of the TMD reveals the movements of TM5, TM6, and TM7, linked with the helix8, which are possibly involved in shaping the conformational disposition of the ICD. The movement of these TM helices could possibly be a consequence of interactions involving the extracellular domain and extracellular loops. In addition, our analysis also shows that phosphatidylinositol-4-phosphate (PI4P), along with some ICD cholesterols, are implicated in anchoring SMO in the membrane.

Abstract

The main objective of the current study is to estimate the seismic vulnerability of buildings in Kathmandu valley through fragility curves. Since the ground motion records are limited for the April 25, 2015, Nepal earthquake, a MATLAB code is written to generate the artificial ground motion records considering each mandal as a site location. Earlier written MATLAB code has been modified with a correction function to obtain synthetic accelerograms at each station. A spatial seismic hazard distribution is plotted through Geographical Information System (GIS) software. Seismic fragility curves are drawn to estimate the damage of buildings.

Abstract

The control of underactuated Euler–Lagrange systems with uncertain and switched parameters is an important problem whose solution has many applications. The problem is challenging as standard adaptive control techniques do not extend to this class of systems due to structural constraints that lead to parameterization difficulties. This note proposes an adaptive switched control framework that handles the uncertainty and switched dynamics without imposing structural constraints. A case study inspired by autonomous vessel operations is used to show the effectiveness of the proposed approach

Abstract

In recent years, we have witnessed a steady growth of linguistic information represented and exposed as linked data on the Web. Such linguistic linked data have stimulated the development and use of openly available linguistic knowledge graphs, as is the case with the Apertium RDF, a collection of interconnected bilingual dictionaries represented and accessible through Semantic Web standards. In this work, we explore techniques that exploit the graph nature of bilingual dictionaries to automatically infer new links (translations). We build upon a cycle density based method: partitioning the graph into biconnected components for a speed-up, and simplifying the pipeline through a careful structural analysis that reduces hyperparameter tuning requirements. We also analyse the shortcomings of traditional evaluation metrics used for translation inference and propose to complement them with new ones, both-word precision (BWP) and both-word recall (BWR), aimed at being more informative of algorithmic improvements. Over twenty-seven language pairs, our algorithm produces dictionaries about 70% the size of existing Apertium RDF dictionaries at a high BWP of 85% from scratch within a minute. Human evaluation shows that 78% of the additional translations generated for dictionary enrichment are correct as well. We further describe an interesting use-case: inferring synonyms within a single language, on which our initial human-based evaluation shows an average accuracy of 84%. We release our tool as free/open-source software which can not only be applied to RDF data and Apertium dictionaries, but is also easily usable for other formats and communities.

Abstract

Quantum computing hardware has progressed rapidly. Simultaneously, there has been a proliferation of programming languages and program optimization tools for quantum computing. Existing quantum compilers use intermediate representations (IRs) where quantum programs are described as circuits. Such IRs fail to leverage existing work on compiler optimizations. In such IRs, it is non-trivial to statically check for physical constraints such as the no-cloning theorem, which states that qubits cannot be copied. We introduce QSSA, a novel quantum IR based on static single assignment (SSA) that enables decades of research in compiler optimizations to be applied to quantum compilation. QSSA models quantum operations as being side-effect-free. The inputs and outputs of the operation are in one-to-one correspondence; qubits cannot be created or destroyed. As a result, our IR supports a static analysis pass that verifies no-cloning at compile-time. The quantum circuit is fully encoded within the def-use chain of the IR, allowing us to leverage existing optimization passes on SSA representations such as redundancy elimination and dead-code elimination. Running our QSSA-based compiler on the QASMBench and IBM Quantum Challenge datasets, we show that our optimizations perform comparably to IBM’s Qiskit quantum

Abstract

Matrix Algebra and Deep Neural Networks represent foundational classes of computational algorithms across multiple emerging applications like Augmented Reality or Virtual Reality, autonomous navigation (cars, drones, robots), data science, and various artificial intelligence-driven solutions. An accelerator-based architecture can provide performance and energy efficiency supporting fixed functions through customized data paths. However, constrained Edge systems requiring multiple applications and diverse matrix operations to be efficiently supported, cannot afford numerous custom accelerators. In this article, we present MxCore, a unified architecture that comprises tightly coupled vector and programmable cores sharing data through highly optimized interconnects along with a configurable hardware scheduler managing the co-execution. We submit MxCore as the generalized approach to facilitate the flexible acceleration of multiple Matrix Algebra and Deep-learning applications across a range of sparsity levels. Unified compute resources improve overall resource utilization and performance per unit area. Aggressive and novel microarchitecture techniques along with block-level sparsity support optimize compute and

Abstract

Blood cells carry important information that can be used to represent a person’s current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet’s architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells. Keywords: blood cell; new BCNet framework; deep transfer learning; multi-class identification; verification and validation

Abstract

According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model’s ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature. Keywords: lung disease; COVID-19; pneumonia; chest X-rays images; feature extraction; automatic detection; artificial intelligence; epidemic