Identifying key factors influencing immersive experiences in virtual reality enhanced museums.

This study investigates how virtual reality (VR) technology shapes immersive experiences in museum environments. It aims to identify the critical factors that determine the quality and effectiveness of immersion and to explore whether overlooked peripheral elements-beyond core constructs such as the Technology Acceptance Model (TAM)-significantly influence user experience. To address these questions, a multi-dimensional, hierarchical indicator framework was developed, encompassing technical, behavioral, and cultural dimensions.

Leveraging data analytics to revolutionize cybersecurity with machine learning and deep learning.

The exponential growth of digital technologies has brought about a surge in the complexity and frequency of cyber-attacks, necessitating robust cyber security measures. This study introduces an innovative approach to cyber security data analysis by leveraging Convolutional Neural Network (CNN) technology. The primary objective is to explore the potential of CNNs in accurately and efficiently detecting and classifying cyber security threats.

Trust the Machine or Trust Yourself: How AI Usage Reshapes Employee Self-Efficacy and Willingness to Take Risks.

As artificial intelligence (AI) technology becomes increasingly widespread in organizations, its impact on individual employees' psychology and behavior has garnered growing attention. Existing research primarily focuses on AI's effects on organizational performance and job design, with limited exploration of its mechanisms influencing individual employees, particularly in the critical area of risk-taking behavior, which is essential to organizational innovation. This research develops a moderated mediation model grounded in social cognitive theory (SCT) to explore how AI usage affects the willingness to take risks.

Research on Rapid and Non-Destructive Detection of Prune Brix Based on Visible/Near-Infrared Spectroscopy Technology.

Prunes, known for their sweet aroma and smooth texture, are rich in vitamins and minerals, making them both a delicious and nutritious food. Brix is a key indicator of prune quality; however, rapid and nondestructive measurement remains a challenge. About this research, visible/near-infrared (Vis/NIR) spectral data from various prune types were collected and preprocessed using Standard Normal Variate (SNV) to reduce noise and scattering effects.

Improving CNN predictive accuracy in COVID-19 health analytics.

The COVID-19 pandemic has underscored the critical necessity for robust and accurate predictive frameworks to bolster global healthcare infrastructures. This study presents a comprehensive examination of convolutional neural networks (CNNs) applied to the prediction of COVID-19-related health outcomes, with an emphasis on core challenges, methodological constraints, and potential remediation strategies. Our investigation targets two principal aims: the identification of COVID-19 infections through chest radiographic imaging, specifically X-rays, and the prognostication of disease severity by integrating clinical parameters and electronic health records.

Enhancing employees' workplace well-being through workplace digitization: exploring the mediating roles of job demands and digital anxiety.

Introduction: With the arrival of the digital era, workplaces are undergoing radical changes, and digitalization not only enhances work efficiency but also significantly influences employees' workplace well-being. In this context, it is crucial for enterprises to actively promote digital transformation to gain a competitive edge. A significant challenge lies in liberating employees from monotonous tasks, thereby facilitating their engagement in more creative and analytical endeavors, which in turn enhances their workplace well-being.

Work from home and employee well-being: a double-edged sword.

Background: With the advancement of globalization and technology, remote work has gradually become a feasible and popular work model, particularly with the rapid adoption of working from home driven by the COVID-19 pandemic. This shift not only provides employees with greater flexibility and convenience, potentially enhancing their well-being, but also brings challenges such as blurred boundaries between work and family, and increased social isolation, which can lead to a decline in well-being. Therefore, exploring the dual-edged impact of working from home on employee well-being can help organizations design and implement better policies, enabling employees to balance work and family while improving overall well-being.

Empowering carbon neutrality: Impact of the technology factor market on China's carbon emission intensity.

Achieving carbon neutrality cannot do without necessary technological achievements, and technology factor market (TFM) is an important tool for the transformation of technological achievements. This study empirically examines the impact and mechanisms of TFM on carbon emission intensity by applying provincial panel data from 2010 to 2022. The result shows that TFM significantly reduces carbon emission intensity.

Made in China 2025: Artificial intelligence intervention and urban green economy development.

Against the backdrop of the profound adjustment of the global industrial structure and the rise of the Fourth Industrial Revolution, the contradiction between rapid economic growth and ecological environmental protection has become increasingly prominent. Promoting green development has become a key path to achieving sustainable urban development. In 2015, China launched the "Made in China (2025)″ policy, aiming to promote industrial transformation and upgrading through intelligent manufacturing and green production.

A robust and efficient change point detection method for high-dimensional linear models.

In the context of linear models, a key problem of interest is to estimate the regression coefficient. Nevertheless, in certain instances, the vector of unknown coefficient parameters in a linear regression model differs from one segment to another. In this paper, when the dimension of covariates is high, a new method is proposed to examine a linear model in which the regression coefficient of two subpopulations may be different.

Research on the impact of artificial intelligence development on urban low-carbon transformation.

Scientific analysis of the impact of artificial intelligence (AI) development on urban low-carbon transformation has substantial practical implications for better fostering the development of AI and high-quality urbanization. Based on the panel data of 226 cities in China from 2012 to 2022, this study first designed a comprehensive evaluation index system for the development of AI and urban low-carbon transformation. Secondly, the entropy weight-TOPSIS method and the super-efficiency SBM model were used to measure the development level of AI and the efficiency of urban low-carbon transformation respectively.

Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening.

In light of global climate change and the objective of carbon neutrality, the carbon market has become an important tool for the international community to combat climate change. Nonetheless, due to the complexity and non-linear nature of the carbon price, its accurate prediction has always been a research difficulty. This work presents a hybrid model incorporating comprehensive feature screening, optimized quadratic decomposition, and the Optuna-Attention-LSTM prediction method, aiming to improve the accuracy and stability of carbon price prediction.

Retraction notice to "Design optimization of office building envelope by developed farmland fertility algorithm for energy saving" [Heliyon 10 (2024) e23387].

[This retracts the article DOI: 10.1016/j.heliyon.

Market tools for achieving carbon unlocking: Is China's energy-consumption trading policy effective?

In the context of the current shift from "dual control" of energy consumption to "dual control" of carbon emissions, formulating reasonable energy policies is crucial for breaking free from a high-carbon development path and promoting a transition to low-carbon economy. However, despite the energy trading system being an important institutional arrangement in China's energy market reform, research remains scarce regarding its effectiveness in reducing dependence on high-carbon energy and industries formed in the economic development, or in mitigating urban carbon lock-in. By applying the difference-in-differences method to the panel data of 283 cities in China, this paper empirically examines the impact characteristics and mechanisms of energy rights trading on urban carbon lock-in.

Identify the co-expressed genes of hypertensive nephropathy and diabetic nephropathy.

This study focused on the intricate connections between hypertension nephropathy (HN) and diabetic nephropathy (DN) in terms of molecular and pathological mechanisms. The samples were from the Gene Expression Omnibus (GEO) database. GSE37460 and GSE142153 are training sets, and GSE37455 and GSE30529 are validation sets.

Empirical Analysis of the Impact of Employment on the Elderly Mental Health in China.

Background: As China undergoes rapid modernization concurrent with accelerated aging, older adults are exposed to multifaceted cultural dynamics. Consequently, employment may carry multidimensional significance as a status passage. This study aimed to investigate the impact of employment on the mental health of elderly persons in China, leveraging middle-range theory to understand the nuanced social significance of employment as a status passage.

Multi-Omics Pan-Cancer Profiling of Unveils Its Prognostic Potential, Metabolic Regulation, and Immune Microenvironment Interactions.

This study systematically analyzed the expression and clinical significance of Hydroxysteroid 17-beta dehydrogenase type 10 () in 33 cancers by integrating TCGA, GTEx, and other multi-omics databases. was highly expressed in 14 cancers, like GBM and LGG, but low in 5, such as KIRC. Its expression correlated closely with overall survival (OS) and disease-free survival (DFS).

Bacterial Species in Engineered Living Materials: Strategies and Future Directions.

In recent years, there has been a notable increase interest in engineered living materials (ELMs) owing to their considerable potential. One key area of research within this field is the utilisation of various species of bacteria to create innovative living materials. In order to accelerate the advancement of bacterial-based living materials, a systematic summary of bacterial species and their design strategies is essential.

A study on the diffusion model of new energy passenger vehicles with consideration of product value.

Accurately forecasting new energy passenger vehicle sales is essential for developing effective marketing strategies and supporting government policies. Consumers purchase decisions for new energy passenger vehicles are primarily driven by product value, which is shaped by various product attributes that evolve through technological advancements. In this study, we develop a product value function for new energy vehicles based on the theory of value engineering.

Critical mineral constraints pressure energy transition and trade toward the Paris Agreement climate goals.

The clean energy transition centered on photovoltaic solar and wind power is widely regarded as the fundamental way to achieve the Paris Agreement's pledges. The development of clean energies, however, relies much more on critical minerals than that of conventional ones. It is therefore vital to incorporate mineral constraints into integrated assessment modeling and designing of energy transition pathways.

Influencing factors of patients' behavior of healthcare seeking: a meta-analysis in China.

Objectives: Understanding patient's healthcare seeking behavior (BHS) and identifying the determinants influencing BHS can optimize the allocation of medical resources and enhance the efficacy of healthcare systems. The purpose of this study is to identify the influencing factors of patients' BHS and to assess the extent and variation in their impact on BHS.

Methods: Drawing upon the Anderson Behavioral Model of Health Service Utilization, we summarized the factors influencing patients' BHS into three categories, and examined empirical journal articles published from 2010 to 2023 using meta-analysis.

Dark-based optical sectioning assists background removal in fluorescence microscopy.

In fluorescence microscopy, a persistent challenge is the defocused background that obscures cellular details and introduces artifacts. Here, we introduce Dark sectioning, a method inspired by natural image dehazing for removing backgrounds that leverages dark channel prior and dual frequency separation to provide single-frame optical sectioning. Unlike denoising or deconvolution, Dark sectioning specifically targets and removes out-of-focus backgrounds, stably improving the signal-to-background ratio by nearly 10 dB and structural similarity index measure of images by approximately tenfold.

Harnessing Research Pressure: A Dual Chain Mediation Effects of Achievement Motivation and Research Anxiety on Innovative Behavior Under Challenge-Hindrance Research Stressors.

Purpose: As high-caliber innovative talents in the era of China's new quality productivity, researchers often face substantial pressures in their academic work. However, a critical question arises: does research pressure influence their innovative behavior, and if so, through what mechanisms? Despite its significance, few scholars have addressed these issues in depth. Grounded in the cognitive transactional theory of stress and adopting a "perception → psychology → behavior" research framework, this study develops a dual chain mediation model to explain the association between challenge-hindrance research stressors and researchers' innovative behavior.

Building new model of intelligent mutual support for the elderly based on the decision support platform of the health management data center.

At this stage, China is in the stage of population aging. With the continuous growth of the elderly population, the pension problem is also increasingly serious. The existing pension model has such problems as small service scope, low service level, and few types of services, which is difficult to provide good pension services for the elderly.