Abstract

Around the world, there are a lot of unreinforced masonry (URM) buildings situated in seismically active areas. Additionally, the majority of these buildings were not built in accordance with seismic code requirements. Buildings made of URM are seismically unsafe and require strengthening, as demonstrated by previous earthquakes. Masonry material exhibits a great deal of variability in its behaviour, and the tensile or flexural capacity of the structural components is one of the key factors in the seismic susceptibility of masonry buildings. The flexural strength and deformability of URM walls can be increased using a variety of retrofitting methods. These can be loosely divided into two categories: “conventional” and “advanced”. The surface treatments (e.g., shotcrete, reinforced plaster, ferrocement overlays, polyurea spray technology), stitching and grout or epoxy injections, re-pointing, reinforced concrete confinement, and post-tensioning fall under the first category. The second category includes the use of seismic wallpaper (i.e., fiber-reinforced polymers—FRP such as epoxy-bonded strips or in situ impregnated fabrics), textile-reinforced mortar composites (mesh/fabric reinforcement in the form of fibre, wire, polypropylene bands, plastic bags, bamboo, etc.), and centre core. The present study reviews the state-of-the-art literature on several methods for reinforcing URM walls subject to lateral earthquake loading. The paper provides a complete overview of retrofitting techniques, including benefits, suitability, applicability, drawbacks, restrictions, sustainability, and consequences on rural communities. In addition, the effectiveness of retrofitting methods is compared using factors including weight, technology (i.e., level of skill required), cost, manpower, and disruption to occupants. The study’s findings will help the policymakers, archaeological survey of India (ASI), various government departments owning low-rise masonry buildings, planners, building owners, architects, structural engineers, consultants, and designers, select an appropriate retrofitting system. Further, the design methodology for retrofitting masonry walls using advanced composites like TRM and FRP is also described in detail, using an explanatory example for seismic in-plane and out-of-plane actions.

Abstract

Moving Target Defense (MTD) has emerged as a proactive and dynamic framework to counteract evolving cyber threats. Traditional MTD approaches often rely on assumptions about the attacker’s knowledge and behavior. However, real-world scenarios are inherently more complex, with adaptive attackers and limited prior knowledge of their payoffs and intentions. This paper introduces a novel approach to MTD using a Markov Decision Process (MDP) model that does not rely on predefined attacker payoffs. Our framework integrates the attacker’s real-time responses into the defender’s MDP using a dynamic Bayesian Network. By employing a factored MDP model, we provide a comprehensive and realistic representation of the system and its dependencies. We also incorporate incremental updates to an attack response predictor as new data emerges, ensuring an adaptive and robust defense mechanism. Additionally, we consider the costs of switching configurations in MTD, integrating them into the reward structure to balance execution and defense costs. We first highlight the challenges of the problem through a theoretical negative result on regret. However, our empirical evaluations, conducted in network and web application settings, demonstrate the framework’s effectiveness in environments characterized by high uncertainty and dynamically changing attack surfaces.

Abstract

Text driven diffusion models have shown remarkable capabilities in editing images. However, when editing 3D scenes, existing works mostly rely on training a NeRF for 3D editing. Recent NeRF editing methods leverages edit operations by deploying 2D diffusion models and project these edits into 3D space. They require strong positional priors alongside text prompt to identify the edit location. These methods are operational on small 3D scenes and are more generalized to particular scene. They require training for each specific edit and cannot be exploited in real-time edits. To address these limitations, we propose a novel method, FreeEdit, to make edits in training free manner using mesh representations as a substitute for NeRF. Training-free methods are now a possibility because of the advances in foundation model’s space. We leverage these models to bring a training-free alternative and introduce solutions for insertion, replacement and deletion. We consider insertion, replacement and deletion as basic blocks for performing intricate edits with certain combinations of these operations. Given a text prompt and a 3D scene, our model is capable of identifying what object should be inserted/replaced or deleted and location where edit should be performed. We also introduce a novel algorithm as part of FreeEdit to find the optimal location on grounding object for placement. We evaluate our model by comparing it with baseline models on a wide range of scenes using quantitative and qualitative metrics and showcase the merits of our method with respect to others.

Abstract

Deep learning assisted diagnosis for assessing the severity of various respiratory infections using chest Computed Tomography (CT) scan images has gained much attention after the COVID-19 pandemic. Major tasks while building such models require an understanding of the characteristic features associated with the disease, patient-to-patient variations and changes associated with disease severity. In this work, an attention-based Convolutional Neural Network (CNN) model with customized bottleneck residual module (Attn-CNN) is proposed for classifying CT images into three classes: COVID-19, Normal, and other Pneumonia. The efficacy of the model is evaluated by carrying out various experiments, such as effect of class imbalance, impact of attention module, generalizability of the model and providing visualization of model’s prediction for the interpretability of results. Comparative performance evaluation with five state-of-the-art deep architectures such as MobileNet, EfficientNet-B7, Inceptionv3, ResNet-50 and VGG-16, and with published models such as COVIDNet-CT, COVNet, COVID-Net CT2, etc. is discussed.

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Abstract

Monuments were built for thousands of years as the most enduring and well-known representations of past civilizations. It is crucial to preserve these because they are national symbols with unique cultural and historical significance. The deterioration of the structures happens as a result of weathering, fire, natural disasters like earthquakes, construction flaws, and numerous other factors. In general, the issue with repairing and reinforcing regular buildings is very different from the issue with restoring monument buildings. The goal of keeping a monumental structure in use may be viewed as secondary in importance and any event as a result of the effort made to fulfil the main task. In the case of monumental buildings, emphasis is placed on the preservation of their aesthetic and historical aspects. Therefore, the restoration strives to protect and highlight the monument’s or building’s aesthetic and historical aspects. Choosing the appropriate material for the restoration depends on how the original structure looks. New materials must be compatible with those already used in the building. The goal of the present study is to provide an overview of the materials used to restore the structural integrity of historic structures and monuments that have been damaged by seismic activity. All of these buildings need extra attention because of their specific historical or architectural value, or because they are significant because they are still standing examples of a former way of life.

Abstract

Masonry structures cover a very wide range of monumental works in space and time, covering almost 6,000 years. The structural assessment of the load-carrying capacity of such old structures depends on several parameters such as: (a) material strength; (b) texture of the structure; (c) geometrical form and dimensions; (d) existing loading and constraints; and (e) expected loadings at failure. Detailed calculations are also required for the structural evaluation using most of the above parameters firstly by considering the gravity loads and secondly by the combination of gravity load and lateral action in areas of high seismicity. However, at the same time, it is equally significant for the structural engineer, who is involved in structural restoration design, to know in depth qualitatively the basic forms and their structural behaviour of monumental buildings. The structural engineer in charge must understand from the beginning, qualitatively, the structural behaviour of a building under consideration to various basic actions so that one can make a preliminary diagnosis of the causes that are responsible for the existing damages (cracks, drifts, settlements, splitting etc.). In this way, one will be in a position to focus on the detailed research, in situ and laboratory, to the critical points of the building, and can conduct the analysis and design using suitable models for a credible safety evaluation. Therefore, an effort is made in the present study to give a qualitative approach to the structural behaviour of the basic structural forms of masonry monumental and historical buildings, where the consequences of the seismic action are traumatic now and then. This article gives an outline and creates background information for further structural evaluation of the monuments.

Abstract

Data visualization is key to representing data effectively, especially in large multidimensional datasets. In this paper, we extend the BEADS i.e. breaking cluster into chunks of points and represent each chunk as bead. We do this to study the underlying pattern i.e. shape, size and location of the clusters and visualize them in a manner in which the users understand the distribution of the data points. BEADS cluster visualization system is implemented to represent the high-dimensional shape of a cluster by using standard two-dimensional structures like squares and circles as metaphors. This is achieved by subdividing clusters into smaller chunks, or "beads," using the K-means algorithm and optimizing the lp-norm value to determine the most suitable shape for each bead. The visualization helps comprehend the shape, size and overall placement of clusters in high dimensions. Future work involves finding the best number of beads for each cluster individually and improving the efficiency to speed up the process for large datasets. Instead of K-mean, any other appropriate partitioning algorithm could be used at the user's discretion.

Abstract

The Śabdabrahman exercise platform (SBE), at sabdabrahman.org, is an on-line interactive Sanskrit instructional platform that offers immediate feedback and focused help to students at every step of analyzing and translating the Sanskrit sentences in a first-year Sanskrit textbook. Steps include transliteration from Devanagari to standard Romanization and vice versa, analysis of sandhi, identification of nominal and verbal inflection, syntax, compound analysis, and translation. Student submission at each step is evaluated, errors highlighted, and links supplied to relevant help. Source documents are prepared in XML in accordance with the Text-Encoding Initiative guidelines. The platform is coded in Flask and ReactJS and hosted at Amazon Web Services (AWS). The platform has been successfully used in first-year Sanskrit courses offered through The Sanskrit Library (sanskritlibrary.org).