The accelerating pace of digital life has intensified psychological strain, increasing the demand for accessible and systematized emotional support tools. Relaxing video games—defined as low-pressure, non-competitive games designed to promote calm and emotional relief—offer immersive environments that facilitate affective engagement and sustained user involvement. This study proposes a computational framework that integrates sentiment analysis and topic modeling to investigate the affective mechanisms and behavioral dynamics associated with relaxing gameplay. We analyzed nearly 60,000 user reviews from the Steam platform in both English and Chinese, employing a hybrid methodology that combines sentiment classification, dual-stage Latent Dirichlet Allocation (LDA), and multi-label mechanism tagging. Emotional relief emerged as the dominant sentiment (62.8%), whereas anxiety was less prevalent (10.4%). Topic modeling revealed key affective dimensions such as pastoral immersion and cozy routine. Regression analysis demonstrated that mechanisms like emotional relief (β = 0.0461, p = 0.001) and escapism (β = 0.1820, p < 0.001) were significant predictors of longer playtime, while Anxiety Expression lost statistical significance (p = 0.124) when contextual controls were added. The findings highlight the potential of relaxing video games as scalable emotional regulation tools and demonstrate how sentiment- and topic-driven modeling can support system-level understanding of affective user behavior. This research contributes to affective computing, digital mental health, and the design of emotionally aware interactive systems.

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can seamlessly integrate into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual–semantic gap, steering the model towards enhanced performance in Zero-Shot Instance Retrieval. Our models achieve state-of-the-art performance on both the SUN (92.8% and 82.2%) and CUB datasets (59.92% and 48.82%) for bi-directional image-attribute retrieval accuracy. This work not only benchmarks the cognition alignment of AI but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.

Understanding the evolution of land use change and its drivers is vital in keeping the fragile balance between human activities and nature. The present study employs remote sensing data from between 1990 and 2020 during the Grain-for-Green Project (GGP) in Luochuan county, Shaanxi Province, which offers 30 years of continuous data on precipitation, temperature, population, and GDP. Here, the combined method of supervised classification with manual visual interpretation was adopted for building a high-precision spatial distribution information database, in order to explore the links existing between the change features of land use, distribution, and spatial pattern, and the interference of local socio-economic development and natural factors before and after the GGP. According to the results, during the past three decades, Luochuan county has undergone large changes in land use types, displaying distinct features and regional disparities. Geographically, the north of Luochuan county is predominantly forest and grassland, while farmland is mostly in the south of Luochuan county. In 1990, farmland dominated this county; however, after 2000, forest and grassland areas expanded. Notably, the implementation of the GGP significantly influenced changes in grassland and forest areas. With the development of modernization, Luochuan county’s land use structure has gradually equilibrated, with increased uniformity in the distribution of various land use types. Obviously, the shift in land use from 1990 to 2020 primarily correlates with mean annual temperature, annual precipitation, total population, and GDP. Furthermore, regression analysis revealed that during the initial decade of the GGP, natural factors primarily drove land use changes. However, after the GGP, the conversion rate from farmland to forest and shrubland/grassland escalated, and population growth was the continual external force driving the expansion of forest and grassland. Despite substantial economic benefits from land development and utilization in Luochuan county during the past 30 years, certain human economic activities have posed significant pressure on regional agricultural development and sustainable land resource use. Overall, this study helps our government to enhance national land management and planning through a targeted method, also providing a reference for analyzing land use change processes within same areas.

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can integrate seamlessly into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms, using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual-semantic gap, steering the model towards enhanced performance in zero-shot instance retrieval. This work not only benchmarks the cognition alignment of AI, but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.}

As the degree of intelligence in social development increases, the future society will present new features. For example, the necessary labor time for humans will be greatly shortened. The direct result of this is the rapid growth of unemployed people. Many jobs in the future society will be replaced by intelligent machines, and a differentiation of new social stratification will emerge, bringing new social problems and new institutional appeals. The innovations of this article are: first, “the rapid growth of the unemployed people is a great progress of society” is the creative idea of this article, which is different from the views of many economists; second, this article envisions a differentiation of a new social stratification of the future society—the base stratum, the middle stratum, and the top stratum—and analyzes the social problems brought about by the differentiation of new social stratification; third, this article puts forward the institutional appeal of a shared society based on the principle of economic distribution of “social security + distribution according to labor”, and proposes reasonable philosophical suggestions for the future development of humans.