In this paper, we investigate the cardinality of the distance set Delta(A, B) for sets A and B subset of F-q(d) where A or B is contained in a k-dimensional affine subspace over a finite field. Assuming that B lies in a k-co ordinate plane up to translations and rotations, we prove that if |A| |B| > 2(q)(d), then |Delta(A, B)| > q/2, where |Delta(A, B)| denotes the number of distinct distances between elements of A and B. In particular, we show that our result recovers the sharp (d + 1)/2 threshold for the Erd & odblac;s-Falconer distance problem in odd dimensions, where distances are determined by a single set. As a further application, we also obtain an improved result on the Box distance problem posed by Borges, Iosevich, and Ou, in the case where 2 is a square in F-q. (c) 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Ammonia (NH3) co-firing is a practical option for reducing direct CO(2 )emissions from coal-fired power plants while utilizing existing infrastructure. However, its overall environmental and economic feasibility depends strongly on the NH(3)supply pathway and policy support. This study evaluated the process performance, cradle-to-gate environmental impacts, and economic feasibility of NH(3 )co-firing in a 1,100 MW ultra-supercritical circulating fluidized bed (USC-CFB) coal-fired power plant. The NH(3)co-firing ratio was varied from 0 % to 50 % (HHV basis), and NH(3)was supplied from fossil-based (grey), fossil-based with carbon capture (blue), and renewable (green) production routes. Life cycle assessment, techno-economic analysis, policy scenario analysis, sensitivity analysis, and Monte Carlo-based uncertainty analysis were conducted. Increasing the NH(3)co-firing ratio reduced direct CO(2 )emissions at the power generation stage but decreased gross power output and net plant efficiency. At 50 % NH(3)co-firing, green NH(3)reduced global warming potential by 33.1 % relative to baseline case, whereas grey and blue NH(3)increased by 18.1 % and 2.0 %, respectively. The levelized cost of electricity (LCOE) increased with NH(3)co-firing ratio for all pathways in the order of grey < blue < green, mainly because of NH(3 )fuel cost. With policy support, the LCOE of green NH(3)could be reduced to below 100 $/MWh in the range of 20-40 % NH(3)co-firing ratios. Overall, the combination of green NH3, intermediate co-firing ratios, and policy support provided the most favorable balance between GWP reduction and economic performance among the evaluated cases.
Large Language Models (LLMs) are increasingly applied to cybersecurity, but their reliability in Critical Infrastructure (CI) threat modeling remains underexplored. This study proposes CITADEL, a CTI-grounded framework for generating CI-specific cyber threat scenarios with open-source LLMs. CITADEL combines MITRE ATT&CK, CAPEC, and CISA KEV in a three-stage Planner-Enricher-Writer pipeline to produce structured, evidence-linked multi-stage scenarios. We evaluated four open-source LLMs across Communication, Energy, and Healthcare domains through a blind assessment by ten cybersecurity professionals. The results indicate generally positive expert ratings for realism, accuracy, coherence, and utility, while revealing domain-specific differences in model performance. Compared with unconstrained LLM generation, CITADEL improved ATT&CK technique identifier validity by 44.9 percentage points and XML parse success by 7.1 percentage points. CAPEC semantic accuracy also increased by 33.8%, though this improvement did not reach statistical significance and should be interpreted as a directional finding. These results suggest that CTI grounding substantially improves the factual grounding of attack technique identifiers and the structural reliability of generated threat scenarios. Overall, CITADEL provides a practical blueprint for evidence-grounded LLM-based threat scenario generation in CI security.
Context and objective Rice (Oryza sativa L.) grain chalkiness is a serious problem under high temperature (HT) and/or low solar radiation. The degree of chalkiness varies among grains within a panicle. However, the spatial and temporal patterns of chalkiness within a panicle and the underlying mechanisms causing this variation remain unclear. Methods In this study, the temperature-sensitive mutant flo11–2 and wild-type cultivars were used to elucidate the distribution of chalkiness ratio and grain area within a panicle under four short-daylength treatments and three nitrogen levels over three years. A two-day HT treatment was applied to examine spatial and temporal changes in susceptibility to chalkiness during grain filling. Results and conclusions Grain area, a possible indicator of available carbohydrates per spikelet, was greater at the upper and middle positions of each primary rachis branch (PB), regardless of the PB position within a panicle. In contrast, the distribution of chalkiness ratio differed markedly from that of grain area and changed progressively from the upper grains of the top PB to the lower grains of the bottom PB under short-daylength treatments that covered a wider range of grain-filling periods. Nitrogen fertilizer treatments conducted over three years further showed that the upper grain position (G1) had a significantly larger grain area than the lower grain position (G7), whereas the upper branch position (B1) exhibited a significantly higher chalkiness ratio than the lower branch position (B10). Because grain sensitivity to HT varied, being higher at 13–14 days after flowering (DAF) than at 21–22 DAF, the distribution of chalkiness ratio could be influenced by how this sensitive period coincided with HT. Significance Rice grain chalkiness is more strongly influenced by flowering order than by carbohydrate supply, which was demonstrated for the first time through the use of the temperature-sensitive mutant flo11–2.
In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS), Federated Learning (FL) offers a promising decentralized intelligence paradigm for Video Anomaly Recognition (VAR). This task is vital for maintaining high-fidelity Digital Twins and ensuring safety in mission-critical environments. However, the inherent data heterogeneity across distributed edge clients leads to a fundamental challenge known as semantic misalignment, where clients learn divergent feature representations of “normal” and “abnormal” events. The problem becomes particularly pronounced in VAR, where the presence of diverse and fine-grained anomaly categories leads each client to develop distinct semantic interpretations of abnormality. Existing federated methods primarily focus on binary anomaly detection and fail to address this misalignment, preventing effective fine-grained recognition. In this paper, we introduce FedVAR, a weakly-supervised FL framework explicitly designed for VAR. Leveraging the rich representations of Vision-Language Models (VLMs), FedVAR employs a prototype-based alignment mechanism that creates a shared semantic anchor for all clients to re-center and align their visual and textual feature spaces. This process enforces a consistent representation of “normality” across the decentralized network, directly mitigating semantic misalignment and enabling robust prompt-learning of anomaly direction vectors with minimal communication overhead. We conduct extensive experiments on challenging benchmarks under various non-IID data partitioning schemes, unseen domains, and novel anomaly classes. The results demonstrate that FedVAR consistently outperforms state-of-the-art federated baselines, establishing a robust framework for distributed intelligence in video-based CPS.
