Q. J. Yao

Short video addiction (SVA) has emerged as a growing behavioral and social issue, driven by the widespread use of digital platforms that provide highly engaging, personalized, and brief video content. We investigated the neuroanatomical and functional substrates of SVA symptoms, alongside brain transcriptomic and cellular characteristics, using Inter-Subject Representational Similarity Analysis (IS-RSA) and transcriptomic approaches. Behaviorally, we found that dispositional envy was associated with SVA. Structurally, SVA was positively correlated with increased morphological volumes in the orbitofrontal cortex (OFC) and bilateral cerebellum. Functionally, the dorsolateral prefrontal cortex (DLPFC), posterior cingulate cortex (PCC), cerebellum, and temporal pole (TP) exhibited heightened spontaneous activity, which was positively correlated with SVA severity. Transcriptomic and cellular analyses also showed specific genes linked to gray matter volume (GMV) associated with SVA, with predominant expression in excitatory and inhibitory neurons. These genes showed distinct spatiotemporal expression patterns in the cerebellum during adolescence. This study offers a comprehensive framework integrating structural, functional, and neurochemical evidence to highlight the neural-transcriptomic underpinnings of SVA symptoms in a non-clinical population.

Generative artificial intelligence (GenAI) tools like ChatGPT are reshaping higher education, raising concerns about academic integrity alongside potential benefits. The psychological tension accompanying the decision whether to use GenAI or not for a particular task may lead to “AI guilt”—students’ moral discomfort when using GenAI for traditionally human tasks. This study examines the impact of AI guilt on students’ use of ChatGPT for academic tasks, focusing on disciplinary differences between pure and applied fields. We conducted a survey at a Singaporean university, measuring AI guilt and ChatGPT usage. Using logistic regression analysis, we found that AI guilt significantly reduces ChatGPT use for creativity-based tasks but not for routine-based tasks. The relationship between AI guilt and ChatGPT use is linear in pure fields, while applied fields show a nonlinear pattern. Specifically, the perceived risk of being detected and potential academic penalties decreases ChatGPT use in creativity-based tasks for all samples, while rationalization tendencies increase it. Interestingly, the interaction between rationalization tendencies and perceived social norms reduces usage, reflecting the tension between internal justification and external pressures. Heterogeneous analysis shows that rationalization tendencies and perceived risk influence pure fields more, whereas social norms drive applied fields more. These results show how AI guilt varies across disciplines and affects task-level GenAI tool use, offering insights for developing tailored ethical guidelines and integration strategies in higher education.

Generative artificial intelligence (GenAI) tools like ChatGPT are reshaping higher education, raising concerns about academic integrity alongside potential benefits. The psychological tension accompanying the decision whether to use GenAI or not for a particular task may lead to “AI guilt”—students’ moral discomfort when using GenAI for traditionally human tasks. This study examines the impact of AI guilt on students’ use of ChatGPT for academic tasks, focusing on disciplinary differences between pure and applied fields. We conducted a survey at a Singaporean university, measuring AI guilt and ChatGPT usage. Using logistic regression analysis, we found that AI guilt significantly reduces ChatGPT use for creativity-based tasks but not for routine-based tasks. The relationship between AI guilt and ChatGPT use is linear in pure fields, while applied fields show a nonlinear pattern. Specifically, the perceived risk of being detected and potential academic penalties decreases ChatGPT use in creativity-based tasks for all samples, while rationalization tendencies increase it. Interestingly, the interaction between rationalization tendencies and perceived social norms reduces usage, reflecting the tension between internal justification and external pressures. Heterogeneous analysis shows that rationalization tendencies and perceived risk influence pure fields more, whereas social norms drive applied fields more. These results show how AI guilt varies across disciplines and affects task-level GenAI tool use, offering insights for developing tailored ethical guidelines and integration strategies in higher education.

Globalization and the digital revolution have forced a rethink of many philosophical works of the 20th century. Among them, Martin Heidegger’s ideas attract special attention. The main purpose of the article is to study the dichotomy of opposition between the image of technology and the pre-technological era in Martin Heidegger’s philosophy of art. Research methods include the possibility of recombination of heterogeneous data to obtain previously unknown knowledge and semantic analysis of Martin Heidegger’s works. The study proposes a model of the dichotomy of civilization development within the framework of Ge-stell. The essence of this model is the concept of Ge-stell, which now covers modern civilization with industrial and digital technologies. At the same time, humanity and nature are a “permanent reserve” in relation to Ge-stell, that is, according to Heidegger’s ideas, Bestand. Humanity has two paths of development, which involve creation and destruction. Technological development over the past two centuries has caused a global environment. In his philosophy, Heidegger proposed art as the only way to overcome the crisis of modernity. The findings expand the understanding of the global problems associated with the modern development of human civilization through a new rethinking of Heidegger’s ideas. The study offers an alternative to the path of destruction through the mechanism of creative perception of technology through art.

Young children typically demonstrate a transfer deficit, learning less from video than live presentations. Semantically meaningful context has been demonstrated to enhance learning in young children. We examined the effect of a semantically meaningful context on toddlers’ imitation performance. Two- and 2.5-year-olds participated in a puzzle imitation task to examine learning from either a live or televised model. The model demonstrated how to assemble a three-piece puzzle to make a fish or a boat, with the puzzle demonstration occurring against a semantically meaningful background context (ocean) or a yellow background (no context). Participants in the video condition performed significantly worse than participants in the live condition, demonstrating the typical transfer deficit effect. While the context helped improve overall levels of imitation, especially for the boat puzzle, only individual differences in the ability to self-generate a stimulus label were associated with a reduction in the transfer deficit.

With the ultrahigh-speed, large-scale development of tourism and the increasing frequency, intensity, and scope of extreme natural hazards in the context of climate warming, tourism has entered a high-risk era. Based on the central urban area within the outer ring of Shanghai as the research area and the tourism attraction as the research object, this paper takes the flood scenario simulation combined with GIS network analysis to evaluate the spatial accessibility of the emergency response of urban key public service departments under current and future river flood scenarios in different return periods. The results of the study show that, under the current and future flood scenarios, the submergence range is mainly distributed within 2 ∼ 3 km along the banks of the Huangpu River, and it tends to increase from north to south; there are 6, 9, and 21 tourism attractions in the emergency blind area under the once-in-a-century floods in 2010, 2030, and 2050 and 98, 105, and 112 tourism attractions in the emergency blind area under the once-in-a-millennium floods in 2010, 2030, and 2050, respectively; in the flood scene, local road traffic in the inundation area is interrupted by water, and 120 first aid cannot get or be delayed to some tourist attraction ; and in 2030, under the normal and flooding scenarios, 120 first aid in the downtown area of Shanghai has the fastest route to tourism attractions according to the speed of S1, S2, S3, and S4. The flooding intensity, road traffic conditions, and the number and location of key public service departments jointly determine the service scope and response time of medical emergency in urban floods. Since the flood control area of the central city in Shanghai is mainly distributed in the 2 ∼ 3 km area on both banks of the Huangpu River, the impact of flood on the emergency medical service in the entire central city is limited, mainly in some hospitals in the riverside area, where 120 emergency vehicles are unable or delayed to reach some tourism attractions. The research indicates that the quantitative assessment method of spatial accessibility of the emergency response under flood scenario simulation has important scientific value and practical significance, which can provide decision-making basis for emergency management of tourism in China’s urban flood disaster.

This paper firstly introduces the basic knowledge of music, proposes the detailed design of a music retrieval system based on the knowledge of music, and analyzes the feature extraction algorithm and matching algorithm by using the features of music. Feature extraction of audio data is the important research of this paper. In this paper, the main melody features, MFCC features, GFCC features, and rhythm features, are extracted from audio data and a feature fusion algorithm is proposed to achieve the fusion of GFCC features and rhythm features to form new features under the processing of principal component analysis dimensionality reduction. After learning the main melody features, MFCC features, GFCC features, and rhythm features, based on the property that PCA dimensionality reduction can effectively reduce noise and improve retrieval efficiency, this paper proposes vector fusion by dimensionality reduction of GFCC features and rhythm features. The matching retrieval of audio features is an important task in music retrieval. In this paper, the DTW algorithm is chosen as the main algorithm for retrieving music. The classification retrieval of music is also achieved by the K-nearest neighbor algorithm. In this paper, after implementing the research and improvement of algorithms, these algorithms are integrated into the system to achieve audio preprocessing, feature extraction, feature postprocessing, and matching retrieval. This article uses 100 different kinds of MP3 format music as the music library and randomly selects 4 pieces each time, and it tests the system under different system parameters, recording duration, and environmental noise. Through the research of this paper, the efficiency of music retrieval is improved and theoretical support is provided for the design of music retrieval software integration system.

With the development of communication and computer technology, the application of big data technology has become increasingly widespread. Reasonable, effective, and fast retrieval methods for querying information from massive data have become an important content of current research. This article provides an image retrieval method based on the weighted nearest neighbor label prediction for the problem of automatic image annotation and keyword image retrieval. In order to improve the performance of the test method, scientific experimental verification was implemented. The nearest neighbor weights are determined by maximizing the training image annotation, and experiments are carried out from multiple angles based on the Mahalanobis metric learning integration model. The experimental results show that the proposed tag correlation prediction propagation model has obvious improvements in accuracy, recall rate, break-even point, and overall average accuracy performance compared with other widely used algorithm models.

Photovoltaic power forecasting can provide strong support for the safe operation of the power system. Existing forecasting methods are ineffective for grid scheduling decisions or risk analysis. The novel multicluster interval prediction method is proposed to consider the volatility and randomness of PV power output. First, this method utilizes the sparse autoencoder and Bayesian regularized NARX network for point forecasting of PV power. Second, density peak clustering improved by kernel Mahalanobis distance is applied to classify the dataset into multiple clusters, including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation to accomplish the PV power interval prediction. The proposed hybrid method is applied for the interval prediction of PV power at Yulara, Australia. Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. Results shows that the average values of nRMSE, MRE, nMAE, and R2 for the four periods are 4.45%, 0.90%, −0.15%, 3.39%, and 95.93%, respectively. Moreover, the results of interval prediction obtained by the other interval prediction approaches are compared with the proposed KMDDPC-MKDE. It shows that the average values of PICP, PINAW, ACE, and nMPICD for four periods are 93.93%, 9.50%, 3.93%, and 7.10% at 90% confidence level, respectively. Outcomes demonstrate that the proposed method can obtain more accuracy, a higher coverage rate, narrower average bandwidth, and a closer distance between the middle of interval and actual value than other methods.

With the technological evolutionary advent, a vision-based approach presents the remote measuring approach for the analysis of vibration. The structure vibration test and model parameter identification in the detection of the structure of the bridge evaluation occupies the important position. The bridge structure to operate safely and reliably is ensured, according to the geological data of qixiashan lead-zinc mine and engineering actual situation, with the aid of international mining software Surpac. To build the 3D visualization model of the application of visualization in mine production are discussed. The results show that the final solid model of -425 stope can accurately display the spatial form of each layer of stope through rotation, amplification and movement. The proposed system is effectually able to perform cutting, volume calculation and roaming in any direction, which has certain guiding significance for mine production management. An accuracy value of 98.75%, the sensitivity of 99%, specificity of 99.64% and PPV of 99.89% are achieved using the proposed 3D modelling and visualization algorithm for vibration signal processing and management.

© 2016 Optical Society of America.We propose an all-solid-state tunable Bragg filter with a phase transition material as the defect layer. Bragg filters based on a vanadium dioxide defect layer sandwiched between silicon dioxide/titanium dioxide Bragg gratings are experimentally demonstrated. Temperature dependent reflection spectroscopy shows the dynamic tunability and hysteresis properties of the Bragg filter. Temperature dependent Raman spectroscopy reveals the connection between the tunability and the phase transition of the vanadium dioxide defect layer. This work paves a new avenue in tunable Bragg filter designs and promises more applications by combining phase transition materials and optical cavities.

TikTok has one of the most advanced algorithm systems and is the most addictive as compared to other social media platforms. While research on social media addiction is abundant, we know much less about how the TikTok information system environment affects users’ internal states of enjoyment, concentration, and time distortion, which in turn influences their addiction behavior. To fill this gap, this study collects responses from 659 adolescents in China aged between 10 and 19 years old, and the data is then analyzed using Partial Least Square. We find that the system quality has a stronger influence than information quality in determining adolescents’ experience with TikTok and that the flow experience has significant direct and indirect effects on TikTok addiction behavior. Notably, this study finds that TikTok addiction is determined by users’ mental concentration on the medium and its content. Several theoretical insights from the stimulus–organism–response model and the flow theory are used to explain the findings.