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Reimagining stormwater management: Sustainable drainage pathways for resilient Indian cities
No full textSummary India, as the world's most populous country, and with a substantial urban population, requires strategic development to mitigate the risks of urban pluvial flooding in the context of a changing climate. Rapid urbanization increases the presence of impervious surfaces, and climate change effects bring intense, frequent and long-duration rainfall events in India, which magnify urban flooding. Implementing sustainable urban drainage solutions (SUDSs) would mitigate stormwater flood risks, but India has yet to adopt this approach; instead, it relies on traditional drainage infrastructure, despite increasing population indices and an extended yearly rainfall season. Here, we highlight the existing scenario, the challenges and the way forward towards implementing SUDSs in India. To attain SUDSs, city-specific drainage-related challenges need to be identified through problem tree analysis, co-creation with stakeholders of a shared vision for sustainable urban drainage and the design of actionable pathways and experimental approaches for implementing interventions and refining practical indicators. These actions could collectively provide a roadmap for achieving resilient SUDSs
Comparative Analysis of Machine Learning Models for Characterizing Spatial Variability of Wind Pressure Coefficients
No full textThis study explores the global proliferation of tall buildings, highlighting the critical examination of wind pressure during their design. It systematically addresses the spatial variability of mean pressure coefficients on building surfaces, an aspect often overlooked in prevailing design standards. Utilizing Computational Fluid Dynamics (CFD) simulations with RANS turbulence model, this investigation focuses on rectangular buildings with varying plan dimensions. CFD model validation against wind tunnel experiments yields consistently reliable results. Mean pressure coefficient characterization on a horizontal plane employs three machine learning models: Non-Linear Regression (NLR), Artificial Neural Network (ANN), and Support Vector Regression (SVR). The model's performance is evaluated based on various error metrics and the number of parameters required to describe the model. Results show all models performing well with accuracy exceeding R2?>?0.98. Notably, the NLR emerges as the optimal model, requiring the fewest parameters and incorporating observed physical constraints within the variations. � 2025 Elsevier B.V., All rights reserved
Embodied ritual performance and new writing systems
No full textThe "language" of ritual involves not only oral communication but also the manipulation of visual display. Speech signs are therefore coordinated with visual and material artifacts to ensure the efficacy of ritual performance. This chapter will argue that the creation of new writing systems (scripts) stems in part through the practice of coordinating visual material and oral signs in the context of embodied ritual performance, the traces of which are often explicitly evident in the iconic construction of the graphemes themselves. The chapter will draw material from ethnographic accounts of the development of new scripts primarily from Asia and Native America
Reliability of Tunneling Regime for Silicon on Insulator-Based Neuron
No full textLow-power operations are essential for implementing large spiking neural networks (SNNs) in real-world applications. An area and energy-efficient demonstration of a functional liquid state machine (LSM) for spoken word detection using the band-to-band-tunneling (BTBT)-based neuron was proposed earlier. For a product-level demonstration of the BTBT regime operating chips, the variability and reliability of neurons emerge as noteworthy concerns for neuromorphic processors due to the potential implications for performance degradation over time. In this work, we characterize and compare the reliability of partially depleted (PD) silicon on insulator (SOI) transistors in the BTBT and on-regime (ION ) regimes. The drain current fractional degradation (?ID/ID) increases with an increase in voltage and thermal stress in the BTBT and ION regime. The reaction-diffusion-drift (RDD) framework is used to estimate device degradation under operating bias conditions. At operating bias, a ~17% fractional degradation is observed in the BTBT regime operation for ten years, comparable to the degradation observed in the ION regime. Finally, we analyzed the impact of device degradation on the SNN performance. � 2024 Elsevier B.V., All rights reserved
Targeted Imaging of Estrogen Receptor-Positive Cancer Cells Using Fluorescent Estradiol Probes
No full textBreast cancer remains the second most common cause of cancer-related deaths in women worldwide, with ≈70% of cases linked to the overexpression of Estrogen Receptor (ERα). Existing imaging tools often fail to reliably differentiate between ER-positive and ER-negative cancer cells. To address this limitation, two novel fluorescent probes, E2N and E2R, are synthesized by conjugating estradiol to styryl and rhodamine-based fluorophores using click chemistry. These probes are characterized by their photophysical properties, biocompatibility, and selective targeting of ER-positive cells. Cellular uptake studies demonstrate preferential internalization of E2N and E2R in ER-positive MCF-7, ZR-75-1, and T-47D cells, with minimal uptake in ER-negative MDA-MB-231, MDA-MB-468, and healthy COS-7 and NIH-3T3 cell lines. Kinetic studies reveal efficient and rapid uptake of E2N in ER-positive MCF-7 cells, while mechanistic investigations identified clathrin-mediated endocytosis as the receptor-mediated pathway for both probes. Localization studies further confirm their mitochondrial specificity in ER-positive cells, with E2R displaying higher mitochondrial selectivity. These findings underscore the potential of E2N and E2R as powerful tools for distinguishing ER-positive from ER-negative breast cancer cells. Their receptor-mediated targeting and precise imaging capabilities make them promising candidates for advancing breast cancer diagnostics and enabling more targeted therapeutic strategies
Robust networks of rainfall extremes emerge despite fragile ocean monsoon causality under Internal variability
No full textTensoIS: A Step Towards Feed-Forward Tensorial Inverse Subsurface Scattering for Perlin Distributed Heterogeneous Media
No full textEstimating scattering parameters of heterogeneous media from images is a severely under-constrained and challenging problem. Most of the existing approaches model BSSRDF either through an analysis-by-synthesis approach, approximating complex path integrals, or using differentiable volume rendering techniques to account for heterogeneity. However, only a few studies have applied learning-based methods to estimate subsurface scattering parameters, but they assume homogeneous media. Interestingly, no specific distribution is known to us that can explicitly model the heterogeneous scattering parameters in the real world. Notably, procedural noise models such as Perlin and Fractal Perlin noise have been effective in representing intricate heterogeneities of natural, organic, and inorganic surfaces. Leveraging this, we first create HeteroSynth, a synthetic dataset comprising photorealistic images of heterogeneous media whose scattering parameters are modeled using Fractal Perlin noise. Furthermore, we propose Tensorial Inverse Scattering (TensoIS), a learning-based feed-forward framework to estimate these Perlin-distributed heterogeneous scattering parameters from sparse multi-view image observations. Instead of directly predicting the 3D scattering parameter volume, TensoIS uses learnable low-rank tensor components to represent the scattering volume. We evaluate TensoIS on unseen heterogeneous variations over shapes from the HeteroSynth test set, smoke and cloud geometries obtained from open-source realistic volumetric simulations, and some real-world samples to establish its effectiveness for inverse scattering. Overall, this study is an attempt to explore Perlin noise distribution, given the lack of any such well-defined distribution in literature, to potentially model real-world heterogeneous scattering in a feed-forward manner. Project Page: https://yashbachwana.github.io/TensoIS/
Daily Practice Quizathon for SQL Using Telegram
No full textConsistent practice is crucial for students of online programming courses to master the syntax and semantics of a programming language. However, instructors face challenges with dropouts, less participation, engagement, and motivation. To address these issues, we designed a gamified SQL Quizathon, which leverages social media platform, Telegram, where a lot of students are already active. This study aimed to observe and understand, the behavior of online Bachelor of Science (BS) degree students, during the daily quiz-based practice sessions. Here, we conducted a 5-day Quizathon, on a private Telegram channel, releasing one SQL quiz on a daily basis. To enhance engagement, we shared a daily leaderboard of top performers and also sent reminders. We analyzed the trends in students’ quiz performances and completion times. The data was collected from QuizBot, and the survey feedback from students. Initially, 237 students expressed their interest by registering, but 122 actively participated in the Quizathon. Of these, 72 also completed the post-Quizathon survey wherein 56 students had attempted all the five quizzes. Analysis revealed distinct behavioral patterns: initial excitement, cautious mid-Quizathon attempts, and a drop in the completion time of final quiz. Quiz scores showed consistency in the first four quizzes but dropped in the fifth quiz. Completion times varied, with significant differences being observed. Survey results of (N = 56) students attempting a 5-day streak, i.e. daily participation, indicated positive responses towards the daily practice and leaderboard. Students found Quizathon to be engaging and reported that their SQL understanding had improved along with their self-confidence
Diazotrophs: An Overlooked Sink of N2O
No full textThe ocean is the second-largest source of greenhouse gas nitrous oxide ((Formula presented.) O). However, its role as an (Formula presented.) O sink is severely overlooked. (Formula presented.) O fixation by diazotrophs has lately been proposed as a new pathway of (Formula presented.) O consumption. We investigated diazotrophic (Formula presented.) O consumption and examined the anthropogenic influence on (Formula presented.) O dynamics in the coastal northeastern Arabian Sea, a hotspot of (Formula presented.) O emissions. Our findings reveal that relatively unperturbed waters, unlike anthropogenically perturbed waters, are a modest net (Formula presented.) O sink (98 (Formula presented.) 29 (Formula presented.) saturation), contrary to previous reports. (Formula presented.) O fixation remains active in anthropogenically perturbed waters in contrast to (Formula presented.) fixation. We additionally provide evidence that the absence of control incubations leads to incorrect fixation rate estimates, further implying that oceanic dark carbon fixation rates might be overestimated. We suggest that (Formula presented.) O fixation not only directly sequesters (Formula presented.) O but may correspond to 0.3 Tg C (Formula presented.) of global ocean net primary production
Reactive main group metal complexes of the neutral NNNN macrocycle, Me4TACD
No full textCurrently, there is considerable interest in introducing molecularly defined main group metal compounds as precursors and model complexes of homogeneous catalysts for various bond cleavage and forming transformations. With a focus on the NNNN macrocyclic ligand Me4TACD (N,N′,N′′,N′′′-tetramethyl-1,4,7,10-tetraazacyclododecane), this review summarizes the versatility of the ligand Me4TACD for the stabilization of reactive main group s- and p-block (group 1, 2, 12-14) metals. Metal hydrides, hydrocarbyls and silyls are often monomeric and catalyze alkene hydrofunctionalisations. In contrast to the rich coordination chemistry of d- and f-block transition metals using a plethora of ligands, main group metals still leave room for new structures and reactivities, aligning with the current efforts to develop a systematic understanding in s- and p-block metal-ligand combinations