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 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

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

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

Targeted image retrieval has long been a challenging problem since each person has a different perception of different features leading to inconsistency among users in describing the details of a particular image. Due to this, each user needs a system personalized according to the way they have structured the image in their mind. One important application of this task is suspect identifcation in forensic investigations where a witness needs to identify the suspect from an existing criminal database. Existing methods require the attributes for each image or suffer from poor latency during training and inference. We propose a new approach to tackle this problem through explicit relevance feedback by introducing a novel loss function and a corresponding scoring function. For this, we leverage contrastive learning on the user feedback to generate the next set of suggested images while improving the level of personalization with each user feedback iteration.

Abstract

An effective earthquake (Mw 7.9) struck Alaska on 3 November, 2002. It ruptured 340 km along three faults namely, the Susitna Glacier, Denali and Totschunda faults in central Alaska. The earthquake was recorded at 23 stations in Alaska and the Peak Ground Acceleration (PGA) of 0.32g was recorded at station PS10, which was located 3 km from the fault rupture. In this study, strike-slip Denali fault has been considered for studying the characteristics of ground motions through modified semiempirical approach. The ground motion records of the 2002 Denali earthquake are generated through MATLAB code. The results revealed that modified semi-empirical approach is fairly good in agreement with observed ground motion records at all stations. A perfect match is observed between Fourier amplitude spectra of simulated and observed ground motions at PS09 and CARLO stations. A good match is observed between elastic response spectra of observed and simulated ground motions. Keywords: Denali Earthquake, Fourier Amplitude Spectrum, Ground Motion Prediction Equation, Synthetic Accelerogram.

Abstract

Phase change materials are attractive as well as being selected as one of the incredibly fascinating materials relating to the high-energy storage system. Phase change materials (PCM) can absorb as well as release thermal energy throughout the melting and freezing process. In recent times, the phase change materials are the feasible option for lowering down the energy consumption and for enhancing the thermal comfort in houses. In this paper, a comprehensive summary of the phase-change materials (PCM) in state of the art is presented. Primary objective of this review is to overview most of the open scientific studies on regarding fatty acid, and carbon allotropes based fatty acid, fatty alcohol phase change materials. Also, this paper is based on earlier scientific studies to consider the variety of references that have been discussed by the authors as well as viewed as the leading contribution.

Abstract

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on Device-to-Device (D2D) ecosystems (e.g., crowdsensing). Sophisticated applications, like cryptocurrencies, need distributed ledgers (DLs) to function. DLs, such as blockchains and directed acyclic graphs (DAGs), employ consensus protocols to add data in the form of blocks. However, such protocols are designed for resourceful devices that are interconnected via the Internet. Moreover, existing DLs are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce and analyze Mneme, a DAG-based DL that can be maintained solely by mobile devices. Mneme utilizes two novel consensus protocols: 1) Proof of Context (PoC) and 2) Proof of Equivalence (PoE). PoC employs users’ context to add data on Mneme. PoE is executed periodically to summarize data and produce equivalent blocks that require less storage. We analyze Mneme’s security and justify the ability of PoC and PoE to guarantee the characteristics of DLs: persistence and liveness. Furthermore, we analyze potential attacks from malicious users and prove that the probability of a successful attack is inversely

Abstract

A deep learning-based approach called Deep-StRIP is proposed for the detection of two major class of structural repeats, namely, Class III and Class IV repeats in Kajava's classification, which covers over 90% of known structural repeats

Abstract

In this article, we present an efficient algorithm for solving a class of chance-constrained optimization under nonparametric uncertainty. Our algorithm is built on the possibility of representing arbitrary distributions as functions in Reproducing Kernel Hilbert Space (RKHS). We use this foundation to formulate chance-constrained optimization as one of minimizing the distance between a desired distribution and the distribution of the constraint functions in the RKHS. We provide a systematic way of constructing the desired distribution based on the notion of scenario approximation. Furthermore, we use the kernel trick to show that the computational complexity of our reformulated optimization problem is comparable to solving a deterministic variant of the chance-constrained optimization. We validate our formulation on two important robotic applications: 1) reactive collision avoidance of mobile robots in uncertain dynamic environments and 2) inverse-dynamics-based path-tracking of manipulators under perception uncertainty. In both these applications, the underlying chance constraints are defined over nonlinear and nonconvex functions of uncertain parameters and possibly also decision variables. We also benchmark our formulation with the existing approaches in terms of sample complexity and the achieved optimal cost highlighting significant improvements in