Maharaja Sriram Chandra Bhanja Deo University

The powder samples of rare earth, Gd doped ZnO were synthesized by a wet chemical solution method. Structural analysis showed that all the samples crystallize in the hexagonal wurtzite structure of ZnO along with the impurity phases associated with Gd. The crystallite sizes, estimated using the Debye–Scherrer and Williamson–Hall methods, were found to be in the ranges of 29.63–43.02 nm and 31.64–56.81 nm, respectively. The morphological features showed nearly hexagonal rectangular particles with few rod shaped particles. The 3

Here, we report a Fe-TiO2@rGO composite that exhibits good photocatalytic activity, reusability, non-hazardous properties, and enhanced stability. The wide band gap range of TiO2 (3.2 eV) has been reduced by doping with iron metal oxide nanocomposites and by using a non-metal supporting material, reduced graphene oxide. The photocatalytic and optoelectronic properties of the samples have been analyzed using ultraviolet-differential reflectance spectroscopy (UV-DRS) and photoluminescence (PL) techniques. It has been found that there is a decrease in the band gap (red shift) when we go from bare TiO2 (3.10 eV) to Fe-doped TiO2 (2.65 eV) and the composite (2.15 eV), which indicates the superior absorption capability of the composite over a wider range of the visible spectrum. Furthermore, the photocatalytic degradation study of methylene blue (MB) yields an intriguing result, where the composite shows approximately 85% dye degradation activity within the first 90 minutes, compared to bare TiO2 (25%) and Fe-doped TiO2 (50%). Methylene blue in the proximity of the catalyst surface increases the electron lifetime by reducing the recombination of hole scavengers, allowing the electron to participate in photocatalytic reactions. The formation of radicals, such as hydroxide (OH-) and superoxide (O2-), triggers the decolourization of the methylene blue pollutant.

This study presents a comprehensive spatio-temporal analysis of land use and land cover (LULC) transformation in Bhubaneswar, India, over a 25-year period (2000-2025), with a particular focus on the dynamics of built-up expansion. Although a number of LULCbased urban studies have been conducted in the area, with respect to medium-sized Indian cities, there are a few opportunities to obtain integrated assessments combining directional growth, landscape fragmentation, and measures of sprawl with predictive modelling. Using multi-temporal Landsat data, supervised classification was conducted in ArcGIS, followed by post-classification change detection and accuracy assessment, and the resulting historical trends were modelled using a comparative Machine Learning (ML) framework incorporating Random Forest, Support Vector Regression, Gradient Boosting, and Linear Regression models. Directional and proximity-based spatial diagnostics, landscape fragmentation analysis, and urban sprawl metrics were employed to quantify and characterise the patterns of urban growth. Results reveal a substantial increase in built-up area from 35.89 km2 in 2000 to 122.31 km2 in 2025, primarily through the conversion of agricultural land (30.79 km2), barren land (33.54 km2), and vegetation (24.18 km2). Directional analysis identified W-SW, S-SW, and N-NE sectors as primary expansion corridors, while buffer-based assessment highlighted high-density sprawl within the 2-4 km zones surrounding urban cores. Fragmentation metrics indicated a temporal shift from edge-and patch-dominated built-up forms to consolidated large core areas, with regression analysis confirming statistically significant structural evolution. This evolution is quantified by an 81% reduction in the Number of Patches (from 2,109 to 400), confirming a statistically significant restructuring of the urban landscape. Sprawl indices such as the Urban Expansion Intensity Index (UEII = 1.1136) and Shannon's entropy (increasing from 2.526 to 2.877) further validated the rapid and decentralised nature of urbanisation. Comparative ML models (Random Forest R2 = 0.928) project future built-up area scenarios ranging from a conservative stabilisation around 99.44 km2 to an aggressive expansion up to 154.07 km2 by 2035. These findings provide viable urban planning insights through the identification of high-growth areas, consolidation areas, as well as sprawl-prone areas, which are useful in guiding evidence-based land-use regulation policies, infrastructure planning, and sustainable urban development policies. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

A long-lived LLM agent, such as OpenClaw, earns its value by acting on a user's preferences and constraints across sessions, not just the current request. Yet today's agents keep what a user volunteers but rarely ask for what stays unspoken, leaving a proactivity gap in long-lived LLM agents: an agent cannot act on a preference it never obtained. As users delegate more of their affairs to agents, the impact of this gap grows. We isolate one concrete, controllable slice of this gap as Ask-to-Remember (ATR): the agent decides whether to ask now for a reusable user preference that the current task does not need but a later session with the same user will. ATR is hard even to evaluate: the right question is underdetermined and its payoff deferred to tasks that may never arise. ATRBench, to the best of our knowledge the first ATR benchmark, makes it measurable by fixing each user's preferences as hidden ground truth, so success demands asking, not recall. Across eight frontier LLM agents, defaults fall at least 62 points below an oracle handed the relevant preference, and prompting closes little of it. Diagnostics identify acquisition as the bottleneck. ATRBench surfaces this proactivity gap in current agents and offers a diagnostic testbed for closing it.

Software evolution is a continuous process that transforms changing user requirements into improved software systems. Establishing a clear and well-structured development process is widely recognized as an effective means to enhance software maintainability, quality, and productivity. Tailoring software processes from existing process patterns and standards is essential for improving process performance, ensuring product quality, reducing development risks, and minimizing rework. Despite its importance, current research lacks a systematic and formally grounded method for tailoring software evolution processes. In this paper, we propose a structured approach based on Petri Net (PN) theory to address this limitation. There are four fundamental process constructs: sequence, concurrency, selection, and iteration are identified as basic building blocks for modeling software evolution processes. Using these constructs, four tailoring operations, namely adding, deleting, splitting, and merging, are formally defined. We study on the scalable process composition, matrix-based representations of Petri Nets (PNs) are employed. Incidence and related matrices provide a concise and mathematically tractable representation of both place/transition nets and restricted PNs, enabling the identification of essential structural properties of software processes. Also, we prove the reachability analysis and firing rules are utilized to derive a mathematical behavioral notation that captures binary relationships between input and output variables. This notation facilitates precise analysis of dynamic behavior for systematic software process tailoring.