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This study explores the novel application of Generative Artificial Intelligence (GenAI) in analyzing unstructured visual data generated through Participatory Rural Appraisal (PRA), specifically focusing on women's empowerment research in rural communities. Using the "Ideal Village" PRA activity as a case study, we evaluate three state-of-the-art Large Language Models (LLMs) - GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro - in their ability to interpret hand-drawn artifacts containing multilingual content from various Indian states. Through comparative analysis, we assess the models' performance across critical dimensions including visual interpretation, language translation, and data classification. Our findings reveal significant challenges in AI's current capabilities to process such unstructured data, particularly in handling multilingual content, maintaining contextual accuracy, and avoiding hallucinations. While the models showed promise in basic visual interpretation, they struggled with nuanced cultural contexts and consistent classification of empowerment-related elements. This study contributes to both AI and gender research by highlighting the potential and limitations of AI in analyzing participatory research data, while emphasizing the need for human oversight and improved contextual understanding. Our findings suggest future directions for developing more inclusive AI models that can better serve community-based participatory research, particularly in gender studies and rural development contexts., Comment: 6 pages, 2 figures, International Conference on Gender and Technology 2025

In this article, we improve the deep learning solution for coastline extraction from Synthetic Aperture Radar (SAR) images by proposing a two-stage model involving image classification followed by segmentation. We hypothesize that a single segmentation model usually used for coastline detection is insufficient to characterize different coastline types. We demonstrate that the need for a two-stage workflow prevails through different compression levels of these images. Our results from experiments using a combination of CNN and U-Net models on Sentinel-1 images show that the two-stage workflow, coastline classification-extraction from SAR images (CCESAR) outperforms a single U-Net segmentation model.

In geometrically frustrated magnetic systems, weak interactions or slight changes to the structure can tip the delicate balance of exchange interactions, sending the system into a different ground state. Brochantite, Cu$_4$SO$_4$(OH)$_6$, has a copper sublattice composed of distorted triangles, making it a likely host for frustrated magnetism, but exhibits stacking disorder. The lack of synthetic single crystals has limited research on the magnetism in brochantite to powders and natural mineral crystals. We grew crystals which we find to be a new polytype with a tendency toward ABAC stacking and some anion disorder, alongside the expected stacking disorder. Comparison to previous results on natural mineral specimens suggests that cation disorder is more deleterious to the magnetism than anion and stacking disorder. Our specific heat data suggest a double transition on cooling into the magnetically ordered state., Comment: 9.5 pages, CIF files available as arXiv ancillary files

Glioblastoma, a highly aggressive brain tumor, poses major challenges due to its poor prognosis and high morbidity rates. Partial differential equation-based models offer promising potential to enhance therapeutic outcomes by simulating patient-specific tumor behavior for improved radiotherapy planning. However, model calibration remains a bottleneck due to the high computational demands of optimization methods like Monte Carlo sampling and evolutionary algorithms. To address this, we recently introduced an approach leveraging a neural forward solver with gradient-based optimization to significantly reduce calibration time. This approach requires a highly accurate and fully differentiable forward model. We investigate multiple architectures, including (i) an enhanced TumorSurrogate, (ii) a modified nnU-Net, and (iii) a 3D Vision Transformer (ViT). The optimized TumorSurrogate achieved the best overall results, excelling in both tumor outline matching and voxel-level prediction of tumor cell concentration. It halved the MSE relative to the baseline model and achieved the highest Dice score across all tumor cell concentration thresholds. Our study demonstrates significant enhancement in forward solver performance and outlines important future research directions.

This paper addresses the challenges faced by algorithms, such as the Firefly Algorithm (FA) and the Genetic Algorithm (GA), in constrained optimization problems. While both algorithms perform well for unconstrained problems, their effectiveness diminishes when constraints are introduced due to limitations in exploration, exploitation, and constraint handling. To overcome these challenges, a hybrid FAGA algorithm is proposed, combining the strengths of both algorithms. The hybrid algorithm is validated by solving unconstrained benchmark functions and constrained optimization problems, including design engineering problems and combinatorial problems such as the 0-1 Knapsack Problem. The proposed algorithm delivers improved solution accuracy and computational efficiency compared to conventional optimization algorithm. This paper outlines the development and structure of the hybrid algorithm and demonstrates its effectiveness in handling complex optimization problems.

CuSn(OH)$_6$ is a quantum spin system from the family of magnetic double perovskite hydroxides, having a frustrated magnetic sublattice. It is also known as the natural mineral mushistonite, whose crystal structure has remained elusive for decades. Here we employ x-ray and neutron powder diffraction to solve the crystal structure of CuSn(OH)$_6$ and propose a structure model in the orthorhombic space group $Pnnn$ with correlated proton disorder. The occupation of the hydrogen sites in the structure is constrained by ``ice rules'' similar to those known for water ice. The resulting frustration of the hydrogen bonding network is likely to have a complex and interesting interplay with the strong magnetic frustration expected in the face-centred magnetic sublattice. Structural distortions, which are quite pronounced in Cu$^{2+}$ compounds due to the Jahn-Teller effect, partially alleviate both types of frustration. We also show that hydrostatic pressure tends to suppress proton disorder through a sequence of proton-ordering transitions, as some of the split hydrogen sites merge already at 1.75 GPa while others show a tendency toward possible merging at higher pressures.

Spinon-magnon mixing was recently reported in botallackite Cu$_2$(OH)$_3$Br with a uniaxially compressed triangular lattice of Cu$^{2+}$ quantum spins [Zhang et al., Phys. Rev. Lett. 125, 037204 (2020)]. Its nitrate counterpart rouaite, Cu$_2$(OH)$_3$NO$_3$, has a highly analogous structure and might be expected to exhibit similar physics. To lay a foundation for research on this material, we clarify rouaite's magnetic phase diagram and identify both low-field phases. The low-temperature magnetic state consists of alternating ferro- and antiferromagnetic chains, as in botallackite, but with additional canting, leading to net moments on all chains which rotate from one chain to another to form a 90$^\circ$ cycloidal pattern. The higher-temperature phase is a helical modulation of this order, wherein the spins rotate from one Cu plane to the next. This extends to zero temperature for fields perpendicular to the chains, leading to a set of low-temperature field-induced phase transitions. Rouaite may offer another platform for spinon-magnon mixing, while our results suggest a delicate balance of interactions and high tunability of the magnetism., Comment: 21 pages, 21 figures; CIF files describing the refinements provided as ancillary files

We study the effect of temperature on the global properties of static and slowly rotating self-gravitating Bose-Einstein condensate (BEC) stars within general relativity. We employ a recently developed temperature dependent BEC equation of state (EoS) to describe the stellar matter by assuming that the condensate can be described by a non-relativistic EoS. Stellar profiles are obtained using general relativistic Hartle-Thorne slow rotation approximation equations. We find that with increasing temperatures mass-radius values are found to be decreasing for the static and rotating cases; though presence of temperature supports high mass values at lower central densities. Countering effects of rotation and temperature on the BEC stellar structure have been analysed and quantified. We report that inclusion of temperature has significant effect on the rotating stellar profiles but negligible effect on the maximum mass, as in the case of static system. We have also studied the effect of EoS parameters -- boson mass and strength of the self-interaction -- on global properties of static and rotating BEC stars, in presence of temperature., Comment: 12 pages, 10 figures; accepted for publication in Phys. Rev. D

This paper explores the role that 5G, WiFi-7, and Time-Sensitive Networking (TSN) can play in driving smart manufacturing as a fundamental part of the Industry 4.0 vision. The paper provides an in-depth analysis of each technology's application in industrial communications, with a focus on TSN and its key elements that enable reliable and secure communication in industrial networks. In addition, the paper includes a comparative study of these technologies, analyzing them based on a number of industrial use-cases, supported secondary applications, industry adoption, and current market trends. The paper concludes by highlighting the challenges and future directions for the adoption of these technologies in industrial networks and emphasizes their importance in realizing the Industry 4.0 vision within the context of smart manufacturing.

The possibility to tune the functional properties of nanomaterials is key to their technological applications. Superlattices, i.e., periodic repetitions of two or more materials in different dimensions are being explored for their potential as materials with tailor-made properties. Meanwhile, nanowires offer a myriad of possibilities to engineer systems at the nanoscale, as well as to combine materials which cannot be put together in conventional heterostructures due to the lattice mismatch. In this work, we investigate GaAs/GaP superlattices embedded in GaP nanowires and demonstrate the tunability of their phononic and optoelectronic properties by inelastic light scattering experiments corroborated by ab initio calculations. We observe clear modifications in the dispersion relation for both acoustic and optical phonons in the superlattices nanowires. We find that by controlling the superlattice periodicity we can achieve tunability of the phonon frequencies. We also performed wavelength-dependent Raman microscopy on GaAs/GaP superlattice nanowires and our results indicate a reduction in the electronic bandgap in the superlattice compared to the bulk counterpart. All our experimental results are rationalized with the help of ab initio density functional perturbation theory (DFPT) calculations. This work sheds fresh insights into how material engineering at the nanoscale can tailor phonon dispersion and open pathways for thermal engineering., Comment: 41 pages including supporting information. Accepted for publication in ACS Applied Nano Materials

Searches for neutrinos from gravitational wave events have been performed utilizing the wide energy range of the IceCube Neutrino Observatory. We discuss results from these searches during the third observing run (O3) of the advanced LIGO and Virgo detectors, including a low-latency follow-up of public candidate alert events in O3, an archival search on high-energy track data, and a low-energy search employing IceCube-DeepCore. The dataset of high-energy tracks is mainly sensitive to muon neutrinos, while the low energy dataset is sensitive to neutrinos of all flavors. In all of these searches, we present upper limits on the neutrino flux and isotropic equivalent energy emitted in neutrinos. We also discuss future plans for additional searches, including extending the low-latency follow-up to the next observing run of the LIGO-Virgo-KAGRA detectors (O4) and analysis of gravitational wave (GW) events using a high-energy cascade dataset, which are produced by electron neutrino charged-current interactions and neutral-current interactions from neutrinos of all flavors., Comment: Presented at the 38th International Cosmic Ray Conference (ICRC2023). See arXiv:2307.13047 for all IceCube contributions

The IceCube Neutrino Observatory is a cubic-kilometer Cherenkov detector at the South Pole, designed to study neutrinos of astrophysical origin. We present an analysis of the Medium Energy Starting Events (MESE) sample, a veto-based event selection that selects neutrinos and efficiently rejects a background of cosmic ray-induced muons This is an extension of the High Energy Starting Event (HESE) analysis, which established the existence of high-energy neutrinos of astrophysical origin. The HESE sample is consistent with a single power law spectrum with best-fit index $2.87^{+0.20}_{-0.19}$, which is softer than complementary IceCube measurements of the astrophysical neutrino spectrum. While HESE is sensitive to neutrinos above 60 TeV, MESE improves the sensitivity to lower energies, down to 1 TeV. In this analysis we use an improved understanding of atmospheric backgrounds in the astrophysical neutrino sample via more accurate modeling of the detector self-veto. A previous measurement with a 2-year MESE dataset had indicated the presence of a possible 30 TeV excess. With 10 years of data, we have a larger sample size to investigate this excess. We will use this event selection to measure the cosmic neutrino energy spectrum over a wide energy range. The flavor ratio of astrophysical neutrinos will also be discussed., Comment: Presented at the 38th International Cosmic Ray Conference (ICRC2023). See arXiv:2307.13047 for all IceCube contributions

Zinc oxynitride (ZnO$_x$N$_y$) has recently emerged as a highly promising band gap-tunable semiconductor material for optoelectronic applications. In this study, a novel DC reactive sputtering protocol was developed to fabricate ZnO$_x$N$_y$ films with varying elemental concentrations, by precisely controlling the working pressure. The band gap was rigorously analyzed using UV-Visible spectroscopy, which was complemented by EDAX spectroscopy to determine the variations in the elemental composition. The correlation between the microstructure and band gap was investigated through the application of AFM, XRD, and Raman spectroscopy, while the Urbach theorem was used to evaluate the defect states. This study revealed the existence of intermediate structures formed during the tuning of the band gap, which can have important implications for future research aimed at developing heterostructures and 2D superlattices for photonics applications.