Abstract

A numerical study is done to determine the seismic performance of G+1 industrial RC building after retrofitting beams and columns. Deterioration of building has occurred due to excess chemical spillage over structural elements. Distressed beams and columns were retrofitted using jacketing techniques. The building is located in Indian Seismic Zone IV, so there is a need to understand the global seismic behaviour of the building after retrofitting. Nonlinear static pushover analysis is carried out to and the results indicate a decrease in the storey shear values. Keywords: Compressive Strength, Seismic Retrofitting, Push-over Analysis, Base Shear, Displacement, Jacketing

Abstract

The present study describes the interdependency between earthquake ground motion parameters and seismic damage to various buildings. A novel ranking procedure between energy, damage and seismic risk has been proposed for buildings subjected to ground motions. Correlation analysis was done between damage to buildings, energy and total energy to confirm the trend of parameters and their influence on total energy ranking. It was found that high peak ground acceleration (PGA) or high energy alone may not lead to extensive damage to the buildings. However, they were affected by the predominant frequency, duration of ground motion and amplitude. A correlation study was performed using over 300 ground motions datasets. It was found that the seismic damage of low-rise buildings had a moderate correlation with root mean square acceleration, characteristic intensity, sustained maximum acceleration and effective design acceleration. Also, a weak correlation was observed between seismic damage of high-rise, tall buildings and (V/H)PGA, Arms. The damage and seismic risk rankings may help change Government policies for retrofitting buildings.

Abstract

Many tall buildings are being constructed in different Indian cities to cater to the demand generated by the large number of people migrating from rural areas to urban centres. The safety of such tall buildings is ensured by designing them for dynamic loads, viz. wind and earthquake. To withstand these loads, computation of the natural period becomes essential. The current Indian seismic code IS 1893 (2016) has outlined a few empirical expressions based on different structural systems to compute the natural period. These expressions have been developed using data obtained from experiments performed on low to midrise buildings. Thus, verifying their applicability for tall structures before using them is important. To achieve this, in the present study ambient vibration testing was done on 28 reinforced concrete (RC) tall buildings in the Indian cities of Hyderabad and Mumbai, whose heights ranged from 50 to 150 m. These tests’ natural periods were compared with existing Indian and international codes. Based on the comparison, a novel empirical expression of RC tall buildings is proposed here.

Abstract

With rapid technological growth, security attacks are drastically increasing. In many crucial Internet-of-Things (IoT) applications such as healthcare and defense, the early detection of security attacks plays a significant role in protecting huge resources. An intrusion detection system is used to address this problem. The signature-based approaches fail to detect zero-day attacks. So anomaly-based detection particularly AI tools, are becoming popular. In addition, the imbalanced dataset leads to biased results. In Machine Learning (ML) models, F1 score is an important metric to measure the accuracy of class-level correct predictions. The model may fail to detect the target samples if the F1 is considerably low. It will lead to unrecoverable consequences in sensitive applications such as healthcare and defense. So, any improvement in the F1 score has significant impact on the resource protection. In this paper, we present a framework for ML-based intrusion detection system for an imbalanced dataset. In this study, the most recent dataset, namely CICIoT2023 is considered. The random forest (RF) algorithm is used in the proposed framework. The proposed approach improves 3.72%, 3.75% and 4.69% in precision, recall and F1 score, respectively, with the existing method. Additionally, for unsaturated classes (i.e., classes with F1 score < 0.99), F1 score improved significantly by 7.9%. As a result, the proposed approach is more suitable for IoT security applications for efficient detection of intrusion and is useful in further studies.

Abstract

We investigate the possible sizes of all the CP-violating asymmetries offered by the angular distribution of rare decay Λb → Λð→ pπ−Þlþl− in the Standard Model and new physics scenarios motivated by the recent b → slþl− anomalies. We work in a model-independent effective theory framework and discuss the sensitivity of CP asymmetries to new O9;10 operators and their chirality flipped counterparts. We find that the size of many of the CP asymmetries can be at the level of a few percent in new physics scenarios consistent with current b → slþl− data at a level of 1σ. We emphasize that measurements of these CP asymmetries can be used to discriminate different new physics scenarios in b → slþl−.

Abstract

Abstract: We calculate QED corrections to the semileptonic decays H1 → H2ℓ +ℓ − where ℓ = e, µ and H1,2 are hadrons. The soft and/or collinear divergences are regulated in a gauge-invariant manner and demonstrably cancel, leaving behind a finite residue that depends on the (infrared) momentum cutoff below which a photon is considered to be indistinguishable. On resuming, the said sensitivity reduces drastically, i.e., for the NLL result as compared to the NLO one. The overall correction is negative and its magnitude is larger for a lighter lepton. For B → K(∗) decays, the corrections improve the agreement for the differential distributions, while the behavior is more complicated for Λb → Λ (∗) decays. Rather intriguingly, the corrections serve to regenerate the tension for the lepton flavor universality observables RK and RK∗ .

Abstract

Criticality, observed during second-order phase transitions, is an emergent phenomenon. The brain operates near criticality, where complex systems exhibit high correlations. The critical brain hypothesis suggests that the brain becomes an efficient learning system in this state but poor in memory, while sub-criticality enhances memory but inhibits learning. As a system approaches criticality, it develops ”domain”-like regions with competing phases and increased spatiotemporal correlations that diverge. The dynamics of these domains depend on the system’s proximity to criticality. This study investigates the phase ordering properties of a spin-lattice model derived from Alzheimer’s and cognitively normal subjects, expecting significant differences in their proximity to criticality. However, our findings show no conclusive distinction in the distal properties from criticality, as reflected in the phase ordering behavior of the Alzheimer’s and cognitively normal brain.

Abstract

X-rays diagnose numerous thoracic diseases; however, accurate pathology detection requires trained radiologists. The number of available experts may impede population-level patient care, delaying medical action. State-of-the-art (SOTA) machine learning methods categorize chest X-rays across numerous diseases well but do not always account for explainability. Interpretability assessments rarely focus on the conciseness and anatomical correctness of Class Activation Mapping outcomes. These models are not accurate or dependable when tested on different datasets of the same modality. They are not used because their predicted performance is not explainable. This work introduces a self-supervised and weakly supervised pretraining pipeline with an auxiliary loss and supervised fine-tuning that retains performance across datasets. We use the Chest X-ray14 (NIH CXR) dataset for pre-training and CheXpert

Abstract

Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski’s rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.

Abstract

In this paper, we propose MolOpt, the first attempt of its kind to use Multi-Agent Reinforcement Learning (MARL) for autonomous molecular geometry optimization (MGO). Typically MGO algorithms are hand-designed, but MolOpt uses MARL to learn a learned optimizer (policy) that can perform MGO without depending on other hand-designed optimizers. We cast MGO as a MARL problem, where each agent corresponds to a single atom in the molecule. MolOpt performs MGO by minimizing the forces on each atom in the molecule. Our experiments demonstrate the generalizing ability of MolOpt for MGO of Propane, Pentane, Heptane, Hexane, and Octane when trained on Ethane, Butane, and Isobutane. In terms of performance, MolOpt outperforms the MDMin optimizer and demonstrates similar performance to the FIRE optimizer. However, it does not surpass the BFGS optimizer. The results demonstrate that MolOpt has the potential to introduce innovative advancements in MGO by providing a novel approach using reinforcement learning (RL), which may open up new research directions for MGO. Overall, this work serves as a proof-of-concept for the potential of MARL in MGO.