Dakota State University

In 2009, W.D. Banks and I.E. Shparlinski studied the average densities of primes p <= x for which the reductions of elliptic curves of small height modulo p satisfy certain algebraic properties, namely cyclicity and divisibility of the number of points by a fixed integer m. In this paper, we refine their results by restricting the primes p under consideration to lie in an arithmetic progression k mod n. Furthermore, for a fixed modulus n, we investigate statistical biases among the different congruence classes k mod n of primes satisfying the aforementioned properties. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Land cover information is essential for understanding Earth's surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD's temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)-a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD. LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consist ing of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency-critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics. Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 +/- 1.60% overall agreement, compared to 71.1 +/- 1.7% agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 +/- 1.22% agreement, compared to 84.6 +/- 1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.

We investigate the feasibility of extracting infinite volume scattering phase shift on quantum computers in a simple one-dimensional quantum mechanical model, using the formalism established in the work by Guo and Gasparian [Phys. Rev. D 108, 074504 (2023)] that relates the integrated correlation functions for a trapped system to the infinite volume scattering phase shifts through a weighted integral. The system is first discretized in a finite box with periodic boundary conditions, and the formalism in real time is verified by employing a contact interaction potential with exact solutions. Quantum circuits are then designed and constructed to implement the formalism on current quantum computing architectures. To overcome the fast oscillatory behavior of the integrated correlation functions in real-time simulation, different methods of postdata analysis are proposed and discussed. Test results on IBM hardware show that good agreement can be achieved with two qubits, but complete failure ensues with three qubits due to two-qubit gate operation errors and thermal relaxation errors.

This paper addresses the data poisoning attack in smart agricultural systems using federated unlearning, where the contribution of compromised field devices must be removed from the global model under communication constraints. We propose FedSCAN, a three-phase framework that identifies critical layers based on parameter sensitivity, classifies active neurons within these layers through relative weight change analysis, and performs unlearning via sparse low-rank adaptation to active neurons while freezing base model parameters. FedSCAN limits computational overhead to a compact parameter subset, enabling devices to participate in a communication-efficient manner. Experimental results on four datasets demonstrate that FedSCAN achieves up to $426.3 \times$ communication cost reduction compared to retraining while maintaining remaining accuracy within 3% of the retraining baseline. Compared with state-of-the-art methods, FedAU, FedEraser, and FedOSD, on the agricultural datasets, FedSCAN reduces communication overhead up to $196.3 \times$ while achieving up to 16.4% higher remaining accuracy than FedAU and 13.1% than FedEraser. On the Fruit-360 dataset with 131 fine-grained categories, FedSCAN achieves up to $181.8 \times$ communication reduction while maintaining remaining accuracy within 0.07% of the retraining baseline.

This study examines the integration of artificial intelligence (AI) and knowledge management (KM) processes and their role in fostering proactive and reactive green innovation (GI) in the Iraqi oil industry. It also explores the moderating effects of trust in technologies and sustainability orientation. Using a cross-sectional design, data were collected from 612 middle-level managers in Iraqi oil companies through a structured questionnaire. The data were analysed using SmartPLS v3.9 and SPSS v26 to assess measurement validity, reliability and the hypothesised relationships. The findings indicate that AI has a significant positive effect on both KM processes and GI. KM processes play a crucial mediating role by transforming AI capabilities into proactive and reactive GI outcomes. While trust in technologies and sustainability orientation moderate these relationships, their effects are relatively modest. Theoretically, the study underscores the importance of integrating AI and KM to enhance environmental performance. It contributes original empirical evidence from a challenging and underexplored context, offering insights into the conditions enabling GI in traditional industries.