Abstract

Long-term transplant success is limited by allograft rejection, a complex process traditionally studied on an organ-specific basis. To establish a unified framework beyond organ-specific studies, we performed a network-based systems biology analysis of transcriptomic data from 672 liver, kidney, and heart transplant biopsies to identify a conserved, pan-organ molecular framework of rejection. By constructing and comparing organ-specific gene co-expression networks, we identified a consensus, six-module immune cascade that captures the hierarchical nature of the alloimmune response. In addition, we also uncovered a highly conserved 24-gene cell cycle signature consistently upregulated in rejecting allografts, implicating cellular proliferation as a core feature of rejection pathology. From this framework, we derived a 172-gene immune signature and applied machine learning models to assess its predictive performance, achieving accuracy comparable to established benchmarks. We further refined this to a minimal, high-performance 20-gene immune signature (AUC > 0.96). Both the immune and cell cycle signatures demonstrated robust, pan-organ utility when independently validated in a lung transplant cohort (n=243). Collectively, these findings define a pan-organ molecular framework for rejection and highlight cell cycle dysregulation as a conserved hallmark, offering a foundation for standardized, cross-organ diagnostic platforms to improve allograft surveillance and patient outcomes.

Abstract

A conventional push-push frequency doubler (CPFD) is simple to implement but suffers from limited total
efficiency (ηtotal) at millimeter-wave (mm-wave) frequencies due to negative feedback introduced by the gate-drain capacitance (Cgd). To overcome this limitation, a 41-62.4 GHz mm-wave FD is
proposed in which the NMOS CPFD employs cross-coupled gatesource feedback that generates an in-phase gate second-harmonic voltage with the drain's second-harmonic current. This feedback effectively mitigates the inherent Cgd feedback and doubles
the fundamental gate-source voltage, Vgs[f0]. As a result, the drain 2f0 current generated by the nonlinear second-harmonic transconductance gm2,nonlinear of the active core's NMOS is increased, leading to enhanced drain efficiency (ηD) and ηtotal. The proposed FD is designed in a 65 nm CMOS process. Post layout simulations demonstrate a peak ηD of 30.79%, a peak ηtotal of 13.7%, and a peak 2f0 output power of 5.8 dBm at 46 GHz, with a 12 dBm local oscillator (LO) input and 12.66 mW DC power consumption. The FD achieves a 3 dB bandwidth of 41-
62.4 GHz (41.4%) and an efficiency bandwidth of 42.3-56 GHz (27.7%), where ηtotal ≥ 10%.

Abstract

How can states and institutions govern crises that no longer occur in isolation but amplify one another across social, economic, and ecological systems? This paper addresses the problem of governing the polycrisis, understood as the convergence of multiple, interacting risks that defy linear policy responses. We propose a framework for modelling such complexity through a structured, two-level system of nodes and subnodes that maps interdependencies across domains of climate change, agricultural productivity, disaster preparedness and governance, and migration. This approach translates qualitative evidence from regional data, policy documents, and existing research into a reproducible network of 120 directional relationships, capturing both direct and cross-domain feedbacks. The model enables the identification of leverage points and cascading vulnerabilities, providing a diagnostic tool for anticipatory and adaptive governance. Applying this framework to the Western Himalayas, the study demonstrates how fragile mountain systems reveal the institutional and spatial limits of crisis management. By integrating computational modelling with political analysis, the paper advances a relational understanding of how states confront systemic risks in complex environments.

Abstract

We address the problem of reactive motion planning for quadrotors operating in unknown environments with dynamic obstacles. Our approach leverages a 4-dimensional spatio-temporal planner, integrated with vision-based Safe Flight Corridor (SFC) generation and trajectory optimization. Unlike prior methods that rely on map fusion, our framework is mapless, enabling collision avoidance directly from perception while reducing computational overhead. Dynamic obstacles are detected and tracked using a vision-based object segmentation and tracking pipeline, allowing robust classification of static versus dynamic elements in the scene. To further enhance robustness, we introduce a backup planning module that reactively avoids dynamic obstacles when no direct path to the goal is available, mitigating the risk of collisions during deadlock situations. We validate our method extensively in both simulation and real-world hardware experiments, and benchmark it against state-of-the-art approaches, showing significant advantages for reactive UAV navigation in dynamic, unknown environments.

Abstract

Large Language Models (LLMs) are increasingly deployed in highstakes settings where errors are costly and decisions must be made
under time pressure, ambiguity, and strict constraints. However,
most existing benchmarks evaluate model performance under relaxed conditions and fail to capture how LLM behavior degrades
under stress. This paper presents a structured framework for stresstesting LLM robustness across five real-world scenarios with increasing difficulty. We evaluate three widely used models--ChatGPT
(GPT-5.1), Gemini 3, and Claude 4.5 Sonnet--using a 45-item rubric
assessing accuracy, safety, logical consistency, and constraint adherence. To quantify robustness, we introduce a Stress Sensitivity
Index (SSI) that measures performance degradation as task difficulty
increases. Our results show that ChatGPT remains largely stable
under stress, Gemini exhibits moderate degradation, and Claude
displays substantial sensitivity, particularly in constraint-following
tasks. These findings highlight the importance of stress-oriented
evaluation for understanding LLM reliability prior to their deployment in real-world, high-stress human environments.

Abstract

India's Digital Personal Data Protection (DPDP) Rules were released in November 2025, following the public comments on the draft rules released in January 2025, and new provisions have been added on data localisation and data transfers. Constant shifts can be noticed in India's approach to crossborder data transfers in recent years, as evident in the previous versions of the Digital Personal Data Protection Act as well as India's international trade negotiations. In this context, it is important to understand India's evolving position on the debate between data localisation and data free flow, given its unique position in the global data economy. We analyse the emerging regional differences on this debate by examining key global developments, India's domestic legislation and strategic negotiations, and updates in the recently released DPDP Rules. By placing India within the larger global context, this paper argues that India's position can be characterised as a 'quasi-localisation' approach. This offers flexibility to participate in the global data economy while retaining some control over citizens' data. However, the lack of clarity raises a number of concerns both at the domestic and international levels.

Abstract

Contactless fingerprint recognition offers a hygienic and convenient alternative to contact-based systems, enabling rapid acquisition without latent prints, pressure artifacts, or hygiene risks. However, contactless images often show degraded ridge clarity due to illumination variation, subcutaneous skin discoloration, and specular reflections. Flash captures preserve ridge detail but introduce noise, whereas non-flash captures reduce noise but lower ridge contrast. We propose Fusion2Print (F2P), the first framework to systematically capture and fuse paired flash-non-flash contactless fingerprints. We construct a custom paired dataset, FNF Database, and perform manual flash-non-flash subtraction to isolate ridge-preserving signals. A lightweight attention-based fusion network also integrates both modalities, emphasizing informative channels and suppressing noise, and then a U-Net enhancement module produces an optimally weighted grayscale image. Finally, a deep embedding model with cross-domain compatibility, generates discriminative and robust representations in a unified embedding space compatible with both contactless and contact-based fingerprints for verification. F2P enhances ridge clarity and achieves superior recognition performance (AUC=0.999, EER=1.12%) over single-capture baselines (Verifinger, DeepPrint).

Abstract

Land dispossession due to industrial mining has formed an influential part of global land grabbing literature across the Global South. Mine closure, on the other hand, is at present typically left to environmental experts and mining companies in spite of its possibilities to regenerate the land for new environmental and social purposes once a mine inevitably closes. While Indian coal mines are yet to close to contain fossil emissions, some of the first large, open pit mines from the 1990s have closed in recent years for economic reasons. In this article we explore Indian coal mine closure policy and its implementation in the coal town Korba. To do this we draw on national policy, international best practice in mine closure, mine closure planning documents, and ethnographic fieldwork in Korba. Our analysis shows how large funds and significant technical expertise are put to practice governed by voluntary mine closure guidelines. The results of this under-regulated approach are highly mixed with a focus on forest plantations and, in places, aquaculture or tourism parks. Rural, previously mining-dependent communities may receive short-term jobs working on closure activities but are otherwise excluded from closure plans intent on continued privatisation and industrialisation. We conclude with a set of policy recommendations based on the international principles and standards of the Society for Ecological Restoration to firstly make regulations mandatory for all sectors and types of mining, and secondly ensure community participation in the regeneration of mined out landscapes.

Abstract

Sustainable management of River Water Quality (RWQ) in tropical river systems requires an approach that addresses both spatial and temporal risks associated with hydrological dynamics, anthropogenic influences, and ecological health. This study develops an uncertainty-aware spatiotemporal risk assessment approach integrating upstream hydrological dynamics (SWAT), operational variability with Environmental Flow (Eflow), water quality simulations (QUAL2K), and probabilistic Water Quality Index (WQI) calculations. Block-Bootstrap ensembles and Monte Carlo techniques are used to quantify uncertainty propagation and construct confidence intervals within the model chain. The monthly WQI estimates under the different Eflow and pollution control scenarios are used to assess the spatial risk variability of the river system. Temporal risk was assessed with WQI using various probabilistic measures, including mean, variance, loss probability, entropy, mean excess loss, and value at risk. A unified risk ranking was developed by using Borda and Copeland aggregation techniques. Categorised spatial risk maps were created using GIS by integrating Eflows and ecological index. The results revealed significant seasonal variations in water quality, with April, March, and May identified as high-risk months due to increased pollution levels. The monthly WQI-mean in the Monte Carlo analysis (1000 iterations), as per the severity of the risk, April, March, and May months are identified as the riskiest months. Spatial risk mapping revealed distinct high-risk zones and highlighted the necessity of pollution treatment level of 25-50 % to reduce ecological risk under optimal Eflow regimes. This comprehensive framework underscores the need for both flow regulation and pollution management to protect river ecosystems, providing actionable insights for effective river health protection.

Abstract

Accurate runway extraction from very high-resolution satellite imagery is challenging: runways are extremely elongated and key
surface markings (e.g., threshold bars) are tiny relative to the scene.
Most existing methods yield coarse masks, limiting downstream use. We
present AERO-DETR, a dual-perspective pipeline that couples a topdown runway locator with a bottom-up sparse-marking detector, and
introduces orientation-normalized training (per-runway chips and
labels rotated to horizontal) to stabilize pattern learning under severe domain variability (geographic diversity, acquisition conditions, extreme aspect ratios, and near-parallel look-alikes). On sub-meter imagery, AERODETR achieves mAP@50:95=0.572, outperforming the best baseline
(DETR, 0.369) by +0.203 absolute (+55% relative) under identical
tiling, augmentation, and epochs, and attains a mean polygon IoU of
0.8927 against ground truth across single, parallel, intersection, mixed,
and complex airport layouts. These results indicate that fusing marking
localization with extent refinement is effective for precise runway extraction.