In a world where analog design is becoming increasingly important, it is becoming increasingly important to provide solutions that can increase designer productivity by increasing the level of available automation. A particularly critical step is in generating the circuit layout, since circuit parasitics can greatly influence circuit performance. This paper presents an overview of recent progress in the area of automated analog layout synthesis, using the ALIGN layout synthesis to showcase a representative design methodology, and listing a set of open problems. An example of how ALIGN is used to synthesize an RF MIMO circuit with enhanced designer productivity is shown, and recent developments on expanding from layout synthesis to automated optimization, with layout in the loop, are overviewed.
Fatigue damage in quasi-brittle materials is an important area of study for structural durability and failure prediction, given its severe consequences, particularly when exposed to aggressive environment. Prediction of cumulative fatigue damage has been receiving growing attention since Palmgren’s idea of damage accumulation and Miner’s linear damage rule. Subsequently, numerous damage models have been proposed. This study outlines the most basic understanding of fatigue damage in quasi-brittle materials available in the literature over the past few decades. It highlights the experimental, analytical, and computational approaches to characterize and model fatigue damage in these materials. The experimental section highlights key techniques and mechanical response, including acoustic emission monitoring and damage indices, that provide valuable insights into stiffness degradation, residual strength reduction, crack evolution and failure mechanisms. Special attention is given to load-sequence effects, statistical variability in fatigue life, and coupled environmental deterioration mechanisms. Analytical models, derived from plasticity, fracture mechanics, continuum damage theories, etc., offer mathematical formulations to characterize fatigue degradation. Computational approaches, such as finite element modeling, cohesive zone modeling, phase field modeling, microplane models, and discrete element modeling enhance predictive capabilities by capturing complex damage evolution. Despite significant progress, challenges remain in standardizing experimental methods, improving analytical model accuracy, reducing computational costs, and developing reliable multi-scale and probabilistic fatigue assessment frameworks for realistic structural applications.
Composite action in concrete filled steel tubular (CFST) columns is by the natural bond between steel tube and concrete. This article reviews experimental studies using push-out test to predict the bond strength and bond -slip behaviour of CFST columns. The influence of cross-sectional dimensions, type of steel, concrete shrinkage, age of concrete, type of concrete, type of steel, section geometry, interface length, thickness of steel tube, compressive strength of concrete, type of loading, interface type and temperature variation are presented in this paper. The bond strength is significantly reduced with the use of large CFST sections, stainless-steel tubes and combination of recycled aggregate concrete (RAC) and stainless-steel tubes in CFST columns. In such situations additional measures are required to enhance the bond performance. Recent developments in CFST systems include the use of expansive concrete to induce prestressing effects, use of ultra-high-performance concrete (UHPC), use of ultra-high-strength steel, and the provision of internal shear connectors such as shear studs, internal rings, vertical ribs, and welded reinforcing bars for the improvement of bond strength.
Groundwater nitrate contamination in India shows strong spatial asymmetry, with severe pollution concentrated in northwestern, central, and southern states, driven by high nitrogen loading and limited hydrological flushing. Multivariate analysis from Karnataka indicates that anthropogenic inputs, groundwater extraction, and shallow water tables strongly co-vary with nitrate levels, while rainfall provides the dominant natural dilution control. Isotopic evidence across major river basins reveals mixed nitrate sources—fertilizers, soil organic nitrogen, sewage, and manure—varying with climate and hydrogeology. Comparative assessment of four districts demonstrates that lithology, vadose-zone thickness, and monsoon-driven anoxia jointly regulate natural denitrification potential and nitrate transport velocity. Health risk analysis shows that exposure is shaped not only by contamination levels but also by water-use patterns, demographics, and correct reference dose selection, with standard parameter assumptions often inflating risk estimates. Mitigation requires integrated strategies: precision nutrient management, climate-smart fertilization, improved irrigation control, decentralized sanitation with nutrient recovery, and context-appropriate nitrate treatment technologies ranging from biosorbents to biological denitrification systems. Together, these findings highlight the need for region-specific interventions that align hydrogeological constraints with demographic exposure patterns to effectively reduce nitrate risks.
Geocells have recently gained worldwide popularity for being integral to various geotechnical applications. The three-dimensional (3D) cellular network of the geocell mattress provides all-round confinement to the infill soils, providing greater benefits in terms of strength improvement and reduction in deformations. This leads to their widespread applications in various geotechnical structures like embankments, foundations, pavements, slopes, retaining walls, railways, erosion control and slope protection, and waste containment systems. Though geocells were initially used to reinforce subgrades, currently, they provide economical and innovative solutions to many complex geotechnical problems, like shore protection and military defense. This paper presents a state-of-the-art review on various applications of geocells, along with current research trends and prospects. The underlying mechanisms of soil-geocell interactions for each of these applications are explained in detail. The review compiles insights gained from a lot of experimental, analytical, numerical, and field studies on geocell applications and provides future directions based on current research trends.
Ever since the dawn of human civilization, agriculture has driven scientific, engineering, and technological developments. Today, it is technology’s turn to drive agriculture because of the increasing population of the world and accompanying challenges in food production that include reducing cultivable land, compromised soil health, ever-increasing stressors on crops, detrimental climatic changes, scarcity of water and energy, etc. Technologies developed for human health, wellbeing, and comfort are now being utilized for improving agriculture, and justifiably so. Just as precision medicine is being developed for humans, it is being adapted for precision agriculture. Central to this critical endeavour are the soil sensors. To complement many insightful reviews that are available on this topic, this review article takes a comprehensive view by focusing on only three important soil sensors, namely, pH, nutrients, and moisture. In order to provide a complete view of the topic, this paper begins with a glimpse of the ever-growing list of research publications and proceeds to give an overview of transduction principles, multiple ways of measuring the aforesaid three soil parameters, capabilities and limitations of existing sensor techniques, status of commercially available sensors, integration of sensors into state-of-the-art precision and digital agricultural practices, and finally future perspectives.
Secure Multi-Party Computation (MPC) protocols allow mutually distrusting parties to compute a function of their inputs without revealing their inputs. Consensus protocols allow mutually distrusting parties to agree on a common output. This article surveys how consensus can help to build MPC protocols that tolerate more corruptions and provide improved security. We describe what MPC results can be achieved with and without consensus, and give intuitions behind these results.
Multi-Party Computation (MPC) enables a set of parties to jointly compute a function while preserving the privacy of their inputs. Although the problem has been studied for several decades, most prior work considers the classical setting in which the set of parties is fixed throughout the protocol execution. This assumption is poorly suited for modern applications that are long-lived and in which parties may join or leave the computation dynamically. Motivated by this limitation, a growing body of recent work has introduced models of MPC with dynamic committees, including YOSO MPC, Fluid MPC, Layered MPC, and SCALES, among others. The proliferation of such models raises natural questions: how do these approaches relate to each other, and can they be unified under a common framework? We show that MPC with dynamic committees can be reduced to the design of a classical MPC protocol with a static set of parties and a general adversary structure, but where the interaction pattern is constrained to follow a fixed layered acyclic graph. Each party corresponds to a node in the graph and can send secret messages along its outgoing edges. We further demonstrate that existing dynamic-committee MPC models can be recovered as specific instantiations of this layered-graph framework.
Artificial Intelligence (AI) has transformed industries worldwide, yet its adoption in agriculture remains limited, particularly in low-resource settings. Farmers in developing countries face two chronic challenges: (1) the assumption of reliable Internet access is often an incorrect one to make in low-resource settings in developing countries; and (2) the complexity of AI algorithms often makes it challenging to be used by low-resource farmers, who often lack the required significant technical expertise needed to use these AI algorithms effectively. As a result, many low-resource farmers are excluded from the benefits of AI technologies, thereby exacerbating the global digital divide. This paper explores whether Generative AI, particularly large language models (LLMs) like ChatGPT, Gemini, and LLaMA, can bridge this ever-increasing divide, and make AI tools and algorithms more accessible to farmers in developing countries. LLMs represent a breakthrough in interactivity, allowing users to engage conversationally with AI algorithms. Historically, AI has lacked this interactivity, making it difficult for non-technical users to understand or act on algorithmic outputs. Generative AI models, however, offer a solution by serving as intuitive interfaces that translate complex AI algorithms into actionable insights. These models enable farmers to ask questions and receive answers in natural language, along with enabling them to provide feedback so that the underlying AI algorithms could be adapted to their specific needs. Furthermore, by leveraging non-Internet modalities like voice-based interfaces and SMS, these models can extend AI’s reach to remote areas lacking reliable Internet access. This paper takes the position that Generative AI can indeed serve as a bridge between low-resource farmers and heavy-duty AI algorithms. This paper discusses how LLMs bring a unique level of interactivity to AI. We then describe a vision for how these models can enable farmers to access algorithmic agricultural advice in an accessible and user-friendly manner. Subsequently, we outline the challenges that must be overcome to realize this vision, including technical, infrastructural, and ethical considerations. Finally, we conclude with a discussion on the broader implications of using Generative AI to address the needs of low-resource farming communities. By exploring this intersection of Generative AI and agriculture, this paper aims to contribute to the ongoing dialogue on making AI technologies inclusive and impactful for underserved populations, ensuring that the transformative potential of AI is enjoyed equitably by all sections of society.
The water uptake status of a plant is directly linked to water stress levels in the leaves. It is important to identify this stress strategically, and if possible in advance. This study reviews current practices and highlights canopy/leaf temperature as a reliable indicator of plant water status and stress. Several indices were developed to quantify this stress out of which the crop water stress index (CWSI), based on temperature (of leaf and reference surfaces) measurement, is the most widely accepted technique despite challenges posed by the environmental variability. Further, experimental results are also presented, carried out using IR imaging to correlate the leaf temperature to different stages such as ‘healthy’, ‘dying/wilted’, and completely ‘dead/dry’. Healthy leaf temperatures are closely aligned with white dry and black wet reference surfaces. Dying leaves exhibited temperatures ranging between yellow and red dry surfaces, while dead leaves aligned between dry green and blue surfaces, with most data clustering near green, indicating its suitability for representing dead leaf conditions. Dying leaves were observed to be 8–10 °C warmer than healthy leaves under similar ambient conditions. These findings helped to formulate a refined and user-friendly CWSI scale using optimal combinations of dry and wet reference surfaces, enabling accurate detection of water stress stages. This work underscores the potential of temperature-based assessments for early stress detection and precision irrigation.
Large Language Models (LLMs) and agentic workflows are increasingly being applied and evaluated across multiple domains, including agriculture, where they support intelligent, context-aware advisory services. Such systems can assist farmers across the agricultural lifecycle, including soil preparation, sowing decisions, crop health monitoring, pest identification, irrigation planning, harvesting, and post-harvest management. In the Indian context, where agricultural practices vary significantly across regions and are deeply rooted in local knowledge, Generative AI and Natural Language Processing can be used to deliver timely, localized, and language-accessible agricultural advisories. Adapting LLMs with region-specific domain knowledge enhances their relevance and practical utility, particularly when delivered through chatbot-based interfaces that support local languages. However, training and deploying such systems typically require substantial computational resources, often relying on modern GPU architectures. This paper examines the practical considerations involved in leveraging existing computational infrastructure for Generative AI workloads and proposes an approach for their effective use in resource-constrained settings. The paper further presents a proposed system architecture incorporating agentic AI workflows for agricultural applications, illustrating how task-oriented agents powered by LLMs can support agricultural chatbot functionality. It also discusses key design considerations in integrating model outputs with agent-based workflows and constructing scalable pipeline architectures tailored to agricultural use cases.
Zeolites are functional nanoporous materials that are central to catalysis, separations, and several other energy-relevant technologies. Yet, despite hundreds of predicted frameworks, only a small fraction of these materials are reproducibly synthesized and used industrially. In part, this gap arises due to the complex interplay of many different phenomena that occur during nucleation, oligomerization, phase change, and crystal growth. To contextualize the progress made over the last few decades, this review explores seminal accomplishments spanning early quantum chemical and molecular dynamics studies to more recent examples using Monte Carlo methods, coarse-grained simulations, and enhanced sampling algorithms. These retrospective insights allow us to identify unresolved scientific questions, long-standing computational bottlenecks, and potential opportunities within this domain. In particular, we highlight five pressing challenges for the zeolite modeling community. These are: (1) shifting from method-driven to application-focused mindset; (2) building cost-efficient reactive force fields and/or machine-learning potentials for describing dynamic multicomponent systems; (3) expanding the use of rare-event and enhanced sampling methods; (4) integrating simulations with experiments for quantitative predictions; and (5) fostering interdisciplinary collaboration through shared data and open benchmarks. We anticipate that meeting these challenges will enable the rational, scalable synthesis of “designer” zeolites, which, ultimately, will transform our shared energy and chemicals infrastructure.
India generates substantial volumes of public agricultural data, yet artificial intelligence (AI) adoption in farming remains limited and largely confined to pilot initiatives. This paper examines this gap by assessing India’s agricultural data infrastructure against the requirements of AI systems deployed at scale. Drawing on a systematic review of major national datasets and digital initiatives—including Soil Health Cards, crop insurance, AgriStack, and selected state platforms—we identify persistent structural constraints, including temporal misalignment between data collection and agricultural decision cycles, spatial fragmentation arising from the absence of common geocodes linking soil, weather, and yield information, limited machine readability due to reliance on static data formats, and unclear governance frameworks that restrict data access and reuse. These deficiencies impede cross-dataset integration and automated decision support, with disproportionate consequences for smallholders, who constitute 86
Carbon farming is the collection of agricultural best practices specifically designed to maximize the capture and long-term storage of atmospheric carbon dioxide in soils and plant biomass, while simultaneously reducing greenhouse gas emissions from cultivation practices. Carbon farming can be viewed as a promising pathway to simultaneously address climate change mitigation, soil degradation, and farmer welfare. For example, if the entire agricultural cropland in India practices carbon farming, this will spectacularly offset about 50
