Abstract

River valley projects have a lot of promise in the seismically active Himalayan orogenic region. Some hydroelectric projects are now operational, some are in the planning stages, and a few more will be built shortly. Knowing the nature of ground motion at these locations is critical. The present study uses a probabilistic seismic hazard analysis (PSHA) technique to estimate Peak Ground Acceleration (PGA) for the three hydropower projects in Uttarakhand, Himachal Pradesh, and Jammu and Kashmir (India). Given all potential earthquakes, the aim of probabilistic seismic hazard analysis (PSHA) is to quantify the rate of surpassing certain ground motion levels at the project site. Hazard curves may be used to determine the seismic design input for a location, and they can also be used to analyze the tunnel seismic reaction.

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Medical image segmentation is critical for effective computer-aided diagnosis and localization of ailments. Automated segmentation of wound regions from patient images can aid clinicians in measuring and managing chronic wounds and monitoring the wound healing trajectory. While there exists a plethora of work on general medical image segmentation, there is hardly any work on wound image analysis and segmentation. Existing methods are limited to segmenting a smaller subset of ulcers, such as foot ulcers, with no special processing for wound images. In this paper, we build segmentation models for eight different types of wound images. Wound image analysis is a challenging problem due to the lack of availability of extensive data (labeled or unlabeled), and annotation is also challenging due to the shortage of well-trained wound care clinicians. To handle these challenges, we contribute WoundSeg 1 , a large and diverse dataset of segmented wound images. Generic wound image segmentation is complex due to the heterogeneous appearance of wound area across images of similar wound types. We propose a novel image segmentation framework, WSNet, which leverages (a) wound-domain adaptive pretraining on a large unlabeled wound image collection and (b) a global-local architecture that utilizes full image and its patches to learn fine-grained details of heterogeneous wounds. On WoundSeg, we achieve a decent Dice score of 0.847. On existing AZH Woundcare and Medetec datasets, we establish a new state-of-the-art. Further, we show the impact of using segmentation for improving the accuracy of downstream tasks like wound

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In developing economies, residential energy consumption patterns have rapidly transformed with better energy access and service quality. Unlike other building types, residential buildings are more complex due to wide variations in their consumption patterns influenced by various factors. Researchers have characterised residential building stock based on distinct building archetypes. This paper presents a comprehensive review of relevant published research focusing on the classification of residential buildings based on their energy consumption. This review also focuses on residential archetype studies in the context of building science. The methodologies adopted by different researchers to characterise the energy use of residential building stock using an archetypal approach at different spatial scales (building to city scale and local to national scale) have been critically reviewed in this study. The paper will provide the researchers with a holistic understanding of the current directions and magnitude of ongoing research in this domain. KEYWORDS: Housing stockbuilding archetypesclassification methodologybuilding energy consumptionenergy modellingbottom-up studies Previous article View issue table of contents Next article Disclosure statement No potential conflict of interest was reported by the author(s). Additional information Funding This work is supported by a Joint Indo-UK research project called ‘Residential building energy demand reduction in India (RESIDE),’ funded by the Department of Science and Technology (DST/TMD/UK-BEE/2017/07), Government of India, and the Engineering and Physical Sciences Research Council (EP/R008434/1), United Kingdom. Related research People also read Recommended articles Cited by Identifying building archetypes based on energy performance as the major criteria: a case of Jaipur, India Bibhu Kalyan Nayak et al. Advances in Building Energy Research Published online: 27 May 2023 Data-driven approach to prioritize residential buildings’ retrofits in cold climates using smart thermostat data Aya Doma et al. Architectural Science Review Published online: 31 Mar 2023 Review of studies on thermal comfort in Indian residential buildings Sunil Kumar Sansaniwal et al. Science and Technology for the Built Environment Published online: 27 Feb 2020 View more

Abstract

Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes. The vulnerability of a building to earthquake can be assessed through inspection that takes into account the expected damage progression of the associated component and the component’s contribution to structural system performance. Most of these inspections are done manually, leading to high utilization of manpower, time, and cost. This paper proposes a methodology to automate these inspections through UAV-based image data collection and a software library for post-processing that helps in estimating the seismic structural parameters. The key parameters considered here are the distances between adjacent buildings, building plan-shape, building

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Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors’ posts on the Internet. Research on ID becomes important when authors’ written online communication pertains to sensitive domains, e.g., mental health. Over time, researchers have utilized AI-based automated word spinners (e.g., SpinRewriter, WordAI) for paraphrasing content. However, these tools fail to satisfy the purpose of ID as their paraphrased content still leads to the source when queried on search engines. There is limited prior work on judging the effectiveness of paraphrasing methods for ID on search engines or

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One of the primary challenges in Semi-supervised Domain Adaptation (SSDA) is the skewed ratio between the number of labeled source and target samples, causing the model to be biased towards the source domain. Recent works in SSDA show that aligning only the labeled target samples with the source samples potentially leads to incomplete domain alignment of the target domain to the source domain. In our approach, to align the two domains, we leverage contrastive losses to learn a semantically meaningful and a domain agnostic feature space using the supervised samples from both domains. To mitigate challenges caused by the skewed label ratio, we pseudo-label the unlabeled target samples by comparing their feature representation to those of the labeled samples from both the source and target domains

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Reconfigurable robots provide an attractive option for cleaning tasks, thanks to their better area coverage and adaptability to changing environment. However, the ability to change morphology creates drastic changes in the reconfigurable robot dynamics, and existing control design techniques do not take this into account. Neglecting configuration changes can lead to performance degradation and, in the worst scenarios, instability. This paper proposes to embed the changes arising from reconfiguration in the control design, via a switched uncertain Euler-Lagrangian model. Accordingly, a novel switched adaptive design is proposed for trajectory tracking. Closed-loop stability is assured using the multiple Lyapunov function framework, and the design is implemented and validated on a self-reconfigurable pavement cleaning mobile robot (PANTHERA). Note to Practitioners —Self-reconfigurable mobile cleaning robots, which can change their configurations as per the application requirements, are now predominantly used for cleaning and maintenance operations because of their better area coverage, less manpower requirement and consistent performance. However, the state-of-the-art control strategies for conventional robots cannot always ensure stability and performance under the simultaneous effects of configuration changes and uncertainties. The switched Euler-Lagrange model formulated in this work can capture the configuration changes of the robot and the proposed switched adaptive controller can tackle uncertainties of each configurations of the robot. The simulation and experimental results clearly show the potential issues of the state-of-the-art methods and the remarkable benefits of the proposed approach.

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The used sodium from experimental studies is un-economical to purify or reuse from fast breeder reactors and is to be discarded safely. This disposal is done by converting the sodium into sodium hydroxide(NaOH) and hydrogen gas as a by product. The molarity of the sodium hydroxide aqueous solution thus obtained ranges from 12M to 16M, which needs real-time monitoring. This paper describes the feasibility study to develop a simple, innovative sensor to measure such high concentrations of NaOH surrounded by hydrogen gas in the light of no COTS sensor available in the market to meet the requirements

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The ever-increasing problem of forest fire risks has intrigued researchers globally to find a mechanism that can provide an early warning of forest fire incidents as well as assess the extent of spatial-temporal damage caused by them. Further information and data on its impacts would help in mitigating the disaster through advanced planning and management. The effects of forest fire have been known for a long time, but due to limited data, mitigation measures were always hampered. With the advancement of technology in the recent past with respect to high-resolution satellite data, a programming interface, and the development of mobile applications to a certain extent, it is now possible to predict forest fires and to study the impacts at local, regional, and global levels. Several open satellite data sets available from NASA, NOAA, and COPERNICUS have facilitated the quantification of various variables in near real time. Further the easy access to open-source data facilitated in the development of user-friendly web interfaces and APPS to continuously monitor the spatio-temporal changes caused as a result of forest fires on the ecosystem and on climate.