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This study investigates the transformative impact of digital technologies on the presentation and dissemination of traditional Chinese culture through the lens of popular cultural TV shows. By analyzing programs such as Chinese Poetry Conference and National Treasure, we explore how digital technologies reshape cultural content, alter transmission mechanisms, and influence audience engagement with traditional culture. Employing a mixed-method approach, including case studies, content analysis, and audience surveys, this research reveals that digital technologies significantly enhance the accessibility and appeal of traditional culture, particularly among younger demographics. The study finds that augmented reality (AR), virtual reality (VR), and interactive platforms create immersive cultural experiences, fostering deeper cultural understanding and identification. However, it also highlights challenges in balancing technological innovation with cultural authenticity. This research contributes to the understanding of cultural heritage preservation in the digital age and offers practical insights for the cultural creative industry.

Local governments are playing an increasingly prominent role in the global investment area by holding large publicly-held pools of assets. The incentives for bureaucrats’ engagement in financial investment and their expertise are presumably distinctive from professional investors in the private markets. Therefore, it’s intriguing to study these government-held funds’ investment patterns and performance. To investigate these problems, this paper uses a hand-collected and novel data set that covers the transaction details of the California government pooled money investment account (PMIA) from 2014 to 2020. This paper presents a statistical analysis of the investments in the last 7 years and study the differences and similarities in behavior and performance between government investments and mutual funds. This paper also presents a model to measure the similarity of funds. I construct two relevant funds that require different levels of sophistication in selecting securities and activeness but are similar to the PMIA in other dimensions. Then I compare their performances with PMIA’s performance in the next period. This paper concluded that the government does perform worse and it is not because of their passive strategy but because of a lack of sophisticated skill. This paper provides new evidence of government investment behavior by rigorous statistical methods.

Tuberculosis (TB) is a global health problem of major concern. Identification of immune biomarkers may facilitate the early diagnosis and targeted treatment of TB. We used public RNA-sequencing datasets of patients with TB and healthy controls to identify differentially expressed genes and their associated functional networks. GBP1 expression was consistently significantly upregulated in TB, and 4492 differentially expressed genes were simultaneously associated with TB and high GBP1 expression. Weighted gene correlation analysis identified 12 functional modules. Modules positively correlated with TB and high GBP1 expression were associated with the innate immune response, neutrophil activation, neutrophil-mediated immunity, and NOD receptor signaling pathway. Eleven hub genes ( GBP1, HLA-B, ELF4, HLA-E, IFITM2, TNFRSF14, CD274, AIM2, CFB, RHOG , and HORMAD1 ) were identified. The least absolute shrinkage and selection operator model based on hub genes accurately predicted the occurrence of TB (area under the receiver operating characteristic curve = 0.97). The GBP1-module-pathway network based on the STRING database showed that GBP1 expression correlated with the expression of interferon-stimulated genes ( GBP5, BATF2, EPSTI1, RSAD2, IFI44L, IFIT3, and OAS3 ). Our study suggests GBP1 as an optimal diagnostic biomarker for TB, further indicating an association of the AIM2 inflammasome signaling pathway in TB pathology.

Due to the inherent disorder and fluidity of water, precise machining of water through laser cutting are challenging. Herein we report a strategy that realizes the laser cutting machining of water through constructing hydrophobic silica nanoparticle-encased water pancakes with sub-millimeter depth. Through theoretical analysis, numerical simulation, and experimental studies, the developed process of nanoparticle-encased water pancake laser cutting and the parameters that affect cutting accuracy are verified and elucidated. We demonstrate that laser-fabricated water patterns can form diverse self-supporting chips (SSCs) with openness, transparency, breathability, liquid morphology, and liquid flow control properties. Applications of laser-fabricated SSCs to various fields, including chemical synthesis, biochemical sensing, liquid metal manipulation, patterned hydrogel synthesis, and drug screening, are also conceptually demonstrated. This work provides a strategy for precisely machining water using laser cutting, addressing existing laser machining challenges and holding significance for widespread fields involving fluid patterning and flow control in biological, chemical, materials and biomedical research. “Due to the inherent disorder and fluidity of water, machining of water through laser cutting is challenging. Here, authors report a strategy through laser cutting to realize the machining of nanoparticle encased water pancakes with the depth of water at sub-millimeter level.”

Runoff fluctuations under the influence of climate change and human activities present a significant challenge and valuable application in constructing high-accuracy runoff prediction models. This study aims to address this challenge by taking the Wanzhou station in the Three Gorges Reservoir area as a case study to optimize various prediction models. The study first selects artificial neural network (ANN) and support vector machine (SVM) as the base models. Then, it evaluates and selects from three time-series decomposition methods. Time-Varying Filter-based Empirical Mode Decomposition (TVF-EMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Variational Mode Decomposition (VMD). Subsequently, these decomposition methods are coupled with optimization algorithms, including Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), and Sparrow Search Algorithm (SSA), to construct various hybrid prediction models. The results indicate that: (1) The single prediction model LSTM demonstrated higher prediction accuracy compared to BP and SVM; (2) The VMD-LSTM model outperformed the CEEMDAN-LSTM and TVF-EMD-LSTM models. Compared to the single LSTM model, the Nash-Sutcliffe Efficiency (NSE) and Pearson’s correlation coefficient (R) of the VMD-LSTM model were improved by 15.06% and 6.82%, respectively; (3) Among the machine learning prediction models coupled with various methods, the VMD-SSA-LSTM model achieved the highest accuracy. Compared to the VMD-LSTM model, the NSE and R values of the VMD-SSA-LSTM model were further increased by 13.09% and 4.26%, respectively. Employing a “decomposition-reconstruction” strategy combined with robust optimization algorithms enhances the performance of machine learning prediction models, thereby significantly improving the runoff prediction capabilities in watershed hydrological models.

In response to the challenge of dynamic adaptability in operational safety assessment for UAVs operating in complex operational environments, this study proposes a novel operational safety assessment method based on an Improved Support Vector Machine. An operational safety assessment index system encompassing four dimensions—operator, UAV platform, flight environment, flight mission—is constructed to provide a comprehensive foundation for evaluation. The method introduces a dynamic weighted information entropy mechanism based on a sliding window, overcoming the static features and delayed response of traditional SVM methods. Additionally, it integrates Gaussian and polynomial kernel functions to significantly enhance the generalization capability and classification accuracy of the SVM model in complex operational environments. Experimental results show that the proposed model demonstrates superior performance on test samples, effectively improving the accuracy of operational safety assessment for the Reconnaissance–Strike UAV in complex operational environments, and offering a novel methodology for UAV safety assessment.

Recent years have witnessed the remarkable progress in wireless communication systems due to the escalating demand for higher data rates, improved reliability, and increased energy efficiency. In this regard, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technology, enhancing spectral efficiency and accommodating multiple users concurrently within the same time and frequency resources. Simultaneously, the energy harvesting has surfaced as a sustainable solution, converting ambient environmental energy into usable electrical power for operating communication nodes. This paper proposes a cooperative NOMA transmission scheme integrating energy harvesting and utilizing Least Squares (LS) channel estimation for precise Channel State Information (CSI) acquisition. The objective is to establish an optimal communication path from source to destination. Relay selection methods: Optimal Relay Selection (ORS) and Max-Min Relay Selection (MMRS), are compared, focusing on their impact on the system performance. The analysis considers the influence of the number of relays and power allocation factor on the system, with a specific emphasis on the outage probability expressions. Comparative analysis between the cooperative-NOMA and the traditional cooperative relaying without NOMA reveals the superior performance of the cooperative-NOMA. Additionally, the ORS scheme outperforms MMRS in terms of the outage performance.

Currently, the Chinese government leads urban renewal via a top-down management style with the government playing the role of decision-maker. The decision-making opinions held by groups of stakeholders are divided, which creates many social problems, project technical issues and even civil disorder. This paper uses factor analysis to extract the key variables for decision-making on urban renewal and the entropy weight method to sort these key variables by importance. Based on this order, the differing opinions of stakeholders regarding urban renewal decision-making are explored. First, contradictory opinions exist concerning the importance of the ecological environment, housing and facilities, social welfare and commercial activities, which are the main driving forces behind urban renewal, due to the groups of stakeholders having different interest demands. Second, these varying interest demands of the stakeholders affect the urban renewal decision-making results. Finally, compensation to people for the demolition of their homes, infrastructure supplements and the investment behaviour of developers display the greatest lack of consensus of all the variables tested in urban renewal decision-making between different stakeholders.

Music generation by AI algorithms like Transformer is currently a research hotspot. Existing methods often suffer from issues related to coherence and high computational costs. To address these problems, we propose a novel Transformer-based model that incorporates a gate recurrent unit with root mean square norm restriction (TARREAN). This model improves the temporal coherence of music by utilizing the gate recurrent unit (GRU), which enhances the model’s ability to capture the dependencies between sequential elements. Additionally, we apply masked multi-head attention to prevent the model from accessing future information during training, preserving the causal structure of music sequences. To reduce computational overhead, we introduce root mean square layer normalization (RMS Norm), which smooths gradients and simplifies the calculations, thereby improving training efficiency. The music sequences are encoded using a compound word method, converting them into discrete symbol-event combinations for input into the TARREAN model. The proposed method effectively mitigates discontinuity issues in generated music and enhances generation quality. We evaluated the model using the Essen Associative Code and Folk Song Database, which contains 20,000 folk melodies from Germany, Poland, and China. The results show that our model produces music that is more aligned with human preferences, as indicated by subjective evaluation scores. The TARREAN model achieved a satisfaction score of 4.34, significantly higher than the 3.79 score of the Transformer-XL + REMI model. Objective evaluation also demonstrated a 15% improvement in temporal coherence compared to traditional methods. Both objective and subjective experimental results demonstrate that TARREAN can significantly improve generation coherence and reduce computational costs.

In general multiple medical devices orthogonal frequency-division multiplexing (OFDM) communication systems, all the interfering medical users are legitimate but will cause disturbance to the desired user. In this work, we evaluate three deep learning (DL) algorithms: fully connected deep neural networks, convolutional neural networks, and long short-term memory neural networks for signal processing and detection in uncoded multiple medical devices OFDM communications systems. The bit error rates (BER) of these DL methods are compared with the conventional linear minimum mean squared error (LMMSE) detector. Additionally, the relationships between the BER and signal-to-interference ratio, signal-to-noise ratio, the number of interferences, and modulation type are investigated. Numerical results show that DL methods outperform LMMSE under different multiple medical device interference situations and are robust when the wireless channel has high variability. Also, DL methods are proven to have strong anti-interference ability and are useful in multiple medical devices OFDM systems.