Truman State University

This study confirmed the factor structure of the Instructor Strategic Ambiguity Measure (ISAM) and established discriminant and convergent construct validity of ISAM by exploring relationships between ISAM and teacher communication behaviors. Confirmatory factor analyses supported the four-factor structure (application, assignments, challenge, and leadership). Results demonstrated a positive relationship between ISAM and constructive teacher behaviors (i.e., student intellectual stimulation and deep approaches to learning). Also, ISAM had an inverse relationship with teacher misbehaviors. Student engagement partially mediated the relationship between instructor strategic ambiguity and intrinsic motivation. The application and assignments factors contributed significantly to student affect.

Your understanding of what you see now surely influences what you will look at next. Yet this simple concept has only recently begun to be systematically studied and elaborated within theoretical frameworks. The Scene Perception & Event Comprehension Theory (SPECT) distinguishes between front-end and back-end processes that occur while viewers perceive and comprehend dynamic real-world events. Front-end processes occur during each eye fixation (information extraction, attentional selection) and back-end processes occur in memory (the current event model, prior knowledge, and executive processes). We begin with a selective review of the scene perception literature on bottom-up and top-down effects on attentional selection in scenes, and highlight unanswered questions regarding the impact of the viewer's event model–their understanding of what is happening now. Then, we outline the SPECT theoretical framework, and review empirical evidence about how the viewer's current event model influences attentional selection. This influence is contrasted with those of visual saliency (e.g., color, brightness, motion, etc.) and task-driven control (i.e., goal setting, attentional control, inhibition). From this review, we specify a hierarchy of factors affecting attentional selection, in the order of task-driven control, visual saliency, and event models. We then propose several mechanisms by which the viewer’s event model influences attentional selection, and propose a systematic approach to investigating how that happens while watching dynamic scenes. (217 words)

Fertigating with phosphorus (P) through drip irrigation has recently emerged as a promising strategy to enhance phosphorus use efficiency (PUE) in potato production. However, due to P’s limited mobility in soil—particularly in the lateral direction—and the relatively sparse root system of potatoes, its uptake remains suboptimal under conventional practices. This study, conducted from 2021 to 2022 in Inner Mongolia, China, investigated whether modifying drip tape depth and emitter spacing could improve the spatial alignment between soil-applied P and potato roots, thereby boosting PUE. Two variables were tested: the depth of drip tape placement and the distance between emitters. Key parameters measured included plant dry weight, leaf area index (LAI), soil Olsen P concentration, root distribution, tuber yield, and partial factor productivity of fertilizer P (PFPP). Results showed that subsurface drip tape placement at a 10 cm depth or reducing emitter spacing to 15 cm significantly improved root-P alignment. These adjustments led to increased P uptake, higher LAI, greater biomass accumulation, and improved tuber yield and PFPP compared to the conventional setup (surface drip tape with 30-cm emitter spacing). The findings underscore the importance of optimizing drip tape configuration to synchronize P availability with crop demand. Deep placement and closer emitter spacing can significantly enhance PUE and yield in drip-fertigated potato systems, offering valuable guidance for more efficient P management in potato production.

Drylands, the most climate-sensitive terrestrial regions, currently support more than 38% of the world's population. Understanding long-term population responses to climate change in and around these areas provides critical insights for anticipating future societal challenges. We integrate radiocarbon-dated archaeological records, simulated climatic data, the Minimum Terrestrial Resource Model (MTRM), and pollen-based vegetation reconstructions to examine spatiotemporal variations in dryland boundary, population distribution, potential hunter-gatherer density, and plant and animal resource densities in East Asia at four key periods since the Last Glacial Maximum (LGM) (20-19 ka, 15-14 ka, 10-9 ka, and 6-5 ka). From 20-19 ka to 15-14 ka, continued warming and humidification drove a northwestward retreat of the dryland boundary, sustained steppe ecosystems, and increased wild animal food resource richness, fostering population growth and expansion. From 15-14 ka to 6-5 ka, continued climatic amelioration promoted a vegetation transition from steppe to forest in marginal areas of the dryland, decreasing wild animal resource availability and shifting population centers toward more humid regions in the southeast. This ecological transformation coincided with the emergence and intensification of agriculture. These results demonstrate a cascading mechanism in which climate change reshaped vegetation cover and food resource distribution, driving human adaptation and long-term shifts in population geography.

With the wide application of diffusion models in the field of image generation, how to improve the controllability of results while ensuring the quality of generation has gradually become one of the research hotspots. Compared with the traditional generation model, the diffusion model has obvious advantages in generation stability and detailed performance, but there are still certain challenges in control accuracy and result consistency. This article reviews the development status of controllable image generation technology, and first analyzes the evolutionary background of image generation technology under the framework of diffusion model. On this basis, this paper systematically sorts out two representative controllable generation methods: the explicit structural constraint method based on additional conditions and the implicit semantic guidance method based on visual examples. The former realizes precise control of image spatial layout and geometric structure by introducing edge diagrams and attitude diagrams; the latter uses a small number of reference images to extract semantic features to achieve personalized generation. Finally, this paper summarizes the advantages and limitations of different methods in terms of control accuracy, generation flexibility and calculation cost, and briefly looks forward to the future research direction from the perspective of multi-conditional integration, control stability and evaluation standards.