Age-related contextual cueing features are more evident in reaction variability than in reaction time.

Visual-spatial contextual cueing learning underpins the daily lives of older adults, enabling them to navigate their surroundings, perform daily activities, and maintain cognitive function. While the contextual cueing effect has received increasing attention from researchers, the relationship between this cognitive ability and healthy ageing remains controversial. To investigate whether visual-spatial contextual cueing learning declines with age, we examined the contextual learning patterns of older (60-71 years old) and younger adults (18-26 years old) using a contextual-guided visual search paradigm and response variability measurements. We observed significant contextual learning effects in both age groups, impacting response speed and variability, with these effects persisting for at least 24 days. However, older adults required more repetitions and memorised fewer repeated stimuli during initial learning. Interestingly, their long-term memory maintenance appeared stronger, as their contextual facilitation persisted in both response speed and variability, while younger adults only persisted in response speed but not variability. Overall, our results suggest an age-related complex and diverse contextual cueing pattern, with older adults showing weaker learning but stronger long-term memory maintenance compared with younger adults.

Bird community structure is driven by urbanization level, blue-green infrastructure configuration and precision farming in Taizhou, China.

Land use/land cover (LULC) changes and high urbanization rates are the main drivers of avian habitat loss in developing countries. However, few studies have examined the effects of urbanization intensity on avian diversity distribution and its importance in guiding eco-friendly urban planning. We surveyed bird distribution (n = 67 species) in different seasons using local ecological knowledge (LEK) and transect line methods in Jiangyan District from July 2018 to May 2019. One-way analysis of variance (ANOVA) was used to assess the effects of urbanization levels on birds relative density and richness during spring-summer (breeding season) and autumn-winter seasons (non-breeding season). Generalized linear models (GLM) were identified for the landscape composition and configuration that drive relative density and richness in native bird communities. Using redundancy analysis (RDA), we identified the landscape composition and configuration factors affecting bird foraging and roosting at urbanization levels. The results showed high dependency of waders and granivores on paddy fields and dry arable land respectively during the breeding season. During non-breeding season, wetland abundance, land cover, connectivity and total area of BGI were important habitat factors in attracting birds. Moreover, the landscape composition and configuration factors of BGI: wetlands as well as farmland habitats, are the main environmental cues that influence bird foraging. Therefore, to increase habitat suitability over landscape matrix, we propose creation of multiple waterbodies and green corridors of variable types and sizes on natural patches to improve the connectivity of ecological network. We also recommend land management interventions in farmland ecosystems, which could contribute to natural habitat restoration and improve bird biodiversity in urban areas.

Biomechanical Analysis and Training Method Research on Head Shot Strength of Football Players.

With the development of intelligent sports in China and the rapid improvement of the strength of colleges and universities, the reform of traditional football players' header shooting training methods is becoming more and more urgent in order to solve some problems in the development of sports and speed up the intelligent training of Chinese football players. Based on this, this paper studies the biomechanical analysis and training method based on the integration of header strength data of football players. A dynamic header tracking model of football players based on a local search algorithm is designed. The data collection is realized from the aspects of athletes' header shooting training, skill improvement, physical consumption, and trajectory. The biological data of header shooting power is comprehensively analyzed and evaluated by using a local search algorithm. The results show that the training system based on a local search algorithm has the advantages of high feasibility, high data accuracy, and fast response speed. It can effectively conduct accurate guidance and improve the shooting accuracy according to the biological characteristics of header shooting intensity. This paper studies the biological analysis and training method of header strength of football players based on a local search algorithm. This has certain reference significance for accelerating the construction of intelligent training of Chinese football players.