Land use is a process that turns a piece of land’s natural ecosystem into an artificial one. The mix of plant and man-made covers on the Earth’s surface is known as land cover. Land use is the primary external force behind change in land cover, and land cover has an impact on how land use is carried out, resulting in a synergistic interaction between the two at the Earth’s surface. In China’s Shandong Peninsula city cluster, Dongying is a significant coastal port city. It serves as the administrative hub for the Yellow River Delta and is situated in Shandong Province, China’s northeast. The changes in its urban land use and land cover in the future are crucial to understanding. This research suggests a prediction approach that combines a patch-generation land use simulation (PLUS) model and long-term short-term memory (LSTM) deep learning algorithm to increase the accuracy of predictions of future land use and land cover. The effectiveness of the new method is demonstrated by the fact that the average inaccuracy of simulating any sort of land use in 2020 is around 5.34%. From 2020 to 2030, 361.41 km² of construction land is converted to cropland, and 424.11 km² of cropland is converted to water. The conversion areas between water and unused land and cropland are 211.47 km² and 148.42 km², respectively. The area of construction land and cropland will decrease by 8.38% and 3.64%, respectively, while the area of unused land, water, and grassland will increase by 5.53%, 2.44%, and 0.78%, respectively.

We aimed to improve the performance of ship detection methods in synthetic aperture radar (SAR) images by utilizing machine learning (ML) and artificial intelligence (AI) techniques. The maritime industry faces challenges in collecting precise data due to constantly changing sea conditions and weather, which can affect various maritime operations, such as maritime security, rescue missions, and real-time monitoring of water boundaries. To overcome these challenges, we present a survey of AI- and ML-based techniques for ship detection in SAR images that provide a more effective and reliable way to detect and classify ships in a variety of weather conditions, both onshore and offshore. We identified key features frequently used in the existing literature and applied the graph theory matrix approach (GTMA) to rank the available methods. This study’s findings can help users select a quick and efficient ship detection and classification method, improving the accuracy and efficiency of maritime operations. Moreover, the results of this study will contribute to advancing AI- and ML-based techniques for ship detection in SAR images, providing a valuable resource for the maritime industry.

Novel second order fractal geometry is presented in this paper for exhibiting radiation characteristics feasible for biomedical applications. The Flamenco fractal is iterated upto second order for attaining resonance covering Industrial, Scientific and Medical (ISM) band for off-body Wireless Body Area Networks (WBAN) communications. The antenna is designed in monopole configuration with the fractal geometry placed on the top layer of the printed circuit board and ground plane embedded on the bottom layer. The frequency response plot is presented for the verification of the radiation characteristics of proposed antenna for ISM band covering frequencies 2.4-2.48 GHz. The gain patterns presented verifies the omni-directional radiations for the Flamenco fractal providing suitability for wireless communications.

Purpose The purpose of this study is to investigate how three factors—stable traits (the severity of ADHD symptoms), momentary states (mental fatigue), and classroom context (class size and area)—influence students’ attention in actual classroom settings. The study compares the distinct and combined contributions of trait, state, and context using the time-pressured d2 Test of Attention, which is given in situ and guided by the dual-pathway and cognitive-energetic models of ADHD. Methodology A cross-sectional survey design was employed to examine the relationships among ADHD symptom severity, mental fatigue, and student attention. Data were collected in April 2025 from 134 Iranian university students during regular classroom sessions. ADHD symptom severity was measured using the Adult ADHD Self-Report Scale (ASRS-v1.1) 6-Item Screener, mental fatigue with the Mental Fatigue Scale (MFS), and attention through the D2 Test of Attention, which assessed accuracy and processing speed under time constraints. Classroom size (square meters) and class size were included as control variables. Data analysis was performed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 to evaluate both direct and moderating effects. Results The findings revealed a significant negative relationship between ADHD symptom severity and student attention, indicating that higher ADHD symptoms were associated with lower attention performance. However, the hypothesized moderating effect of mental fatigue on this relationship was not supported. Additionally, classroom size and the class size showed significant effects, with smaller class sizes and more spacious classrooms linked to increased attention. Conclusion This study underscores the significant impact of ADHD symptom severity on student attention, independent of mental fatigue. It highlights the importance of classroom factors in shaping attentional performance and calls for future research to further explore these dynamics through longitudinal and experimental designs. Originality/value We integrate classroom ecology—class size and usable area per student—into an in-situ objective attention task (d2), isolating trait (ADHD symptoms) from state (mental fatigue) influences. Despite theory-driven expectations, mental fatigue did not moderate ADHD’s impact, indicating a state-insensitive, trait-dominant pathway under time-pressured selective-attention demands. These findings refine dual-process and cognitive-energetic accounts by highlighting environmental context (space and group size) as the more consequential amplifier of ADHD-linked attentional deficits.