Innovation networks in the advanced medical equipment industry: supporting regional digital health systems from a local-national perspective.

This study investigates the spatial structure and influencing factors of the innovation network in the advanced medical equipment and device manufacturing industry in the Yangtze River Delta (YRD). This industry is crucial to public health system resilience and regional industrial upgrading. Drawing on joint patent data from the IncoPat Global Patent Database covering the period 2005-2024, the study constructs innovation networks and conducts comparative analyses from both national and regional perspectives using social network analysis and geodetector methods.

Can industry-education integration enhance enterprise employee safety and health levels?

Introduction: Harmonious relationships between enterprises and employees are key to sustainable enterprise development. Therefore, the study explores whether industry-education integration can enhance enterprise employee safety and health levels, in order to alleviate enterprise labor-management issues.

Methods: Based on panel data from Chinese A-share listed enterprises between 2012 and 2021, this study employs a quasi-natural experiment using China's pilot program for industry-education integrated enterprises.

Time-dependent changes in corneal biomechanics after small incision lenticule extraction: an in vivo study.

Background: This study aimed to investigate the in vivo temporal changes in corneal biomechanical properties following small-incision lenticule extraction (SMILE).

Methods: This retrospective study included 30 myopic patients who underwent SMILE with preoperative and postoperative ocular examinations (at 1 week, 1 month, 3 months, and 6 months) including Pentacam and corneal visualization Scheimpflug technology (Corvis ST) at 5 time points over a 6 months period. The corneal elastic modulus was calculated according to the air-puff force and corneal apical displacement from Corvis ST.

The impact of digital literacy on university students' innovation capability: evidence from Ningbo, China.

Introduction: Digital literacy has become a necessary basic ability for university students and has profoundly influenced their learning attitudes and behavior. This study aims to explore the direct effects of digital literacy on innovation capability among university students and whether it varies with students' personal characteristics.

Methods: This study developed new scales to measure the digital literacy and innovation capability of university students.

Exploring the role of generative AI in international students' sociocultural adaptation: a cognitive-affective model.

Against the backdrop of increasing global educational exchanges, the sociocultural adaptation of international students has attracted significant attention. The rise of Generative Artificial Intelligence has brought new perspectives to research in this field, yet existing studies have insufficiently explored the mechanisms through which GenAI influences the sociocultural adaptation of international students. Drawing on the cognitive-affective personality system theory and conservation of resources theory, this study employed a three-stage time-lagged questionnaire survey to collect 329 valid responses from international students at three universities in North, South, and East China.

Optimizing on-demand food delivery with BDI-based multi-agent systems and Monte Carlo tree search scheduling.

On-demand food delivery services are a rapidly expanding sector within the logistics industry, yet optimizing delivery routes in real-time remains a significant challenge, particularly in high-demand and complex environments. This gap hinders operational efficiency and customer satisfaction, highlighting the need for advanced decision-making frameworks. In response, we propose a multi-agent system (MAS) using the Belief-Desire-Intention (BDI) framework to enhance delivery efficiency.

Advancing named entity recognition in interprofessional collaboration and education.

Introduction: Named Entity Recognition (NER) plays a critical role in interprofessional collaboration (IPC) and education, providing a means to identify and classify domain-specific entities essential for efficient interdisciplinary communication and knowledge sharing. While traditional methods, such as rule-based systems and machine learning models, have achieved moderate success in various domains, they often struggle with the dynamic, context-sensitive nature of IPC scenarios. Existing approaches lack adaptability to evolving terminologies and insufficiently address the complex interaction dynamics inherent in multi-disciplinary frameworks.

Factors influencing innovative work behavior among teachers in the higher education sectors in China: The role of work engagement as a mediator and artificial intelligence as a moderator.

The modernization of Chinese higher education relies heavily on fostering innovative work behavior (IWB) among university teachers. However, the crucial role of non-intellectual and external factors has often been overlooked, contributing to insufficient innovative outcomes. Drawing on self-determination theory and social exchange theory, this study develops a research model where career calling (CC) and talent policy (TP) serve as independent variables, IWB as the dependent variable, work engagement (WE) as a mediating variable, and artificial intelligence (AI) as a moderating variable to explore effective ways to stimulate IWB.

Growth-induced percolation on complex networks.

Empirical studies have increasingly highlighted the crucial role of indirect social interactions in shaping human behaviors, yet theoretical models have largely focused on direct influences. By analyzing scientific collaboration networks, we demonstrate that direct and indirect collaborators are key in triggering high-impact research periods. Inspired by these findings, we propose a novel model, growth-induced percolation, which captures how individuals are activated through indirect interactions.

Medical treatment cost for Chinese inpatients with colorectal cancer by sites.

Background: The increasing prevalence of colorectal cancer (CRC) is a challenge for China's healthcare system. Using hospitalization data from Ningbo, China, this study aims to estimate the medical treatment cost and cost structure of CRC based on tumor sites to gain insights with respect to the cost efficiency of early diagnosis.

Methods: A retrospective observational study was performed in a real-life clinical setting of a tertiary hospital in Ningbo, China.

Artificial intelligence-driven internet of things-based green supply chain for carbon reduction in sustainable manufacturing.

Recent advancements in sustainable business practices and information technology trends have led to the rise of green smart supply chains. These green sustainable supply chains are a novel approach that involves information technology to enhance the operational standards throughout various industries. The integration of artificial intelligence and the Internet of Things has transformed these sectors by utilizing intelligent supply chain practices, improving efficiency and significantly reducing environmental impacts.

Digital infrastructure and intra-provincial balanced development: Evidence from China's provincial panel data.

Unbalanced regional development poses a significant challenge to global economic growth. While existing studies have primarily concentrated on disparities between provinces, there is a notable lack of research addressing regional imbalances within a single province. In this paper, we use urban-level indicators to assess the level of intra-provincial regional balanced development in China.

Exploring new lengths for q-ary quantum MDS codes with larger distance.

In the past decade, the construction of quantum maximum distance separable codes (MDS for short) has been extensively studied. For the length [Formula: see text], where m is an integer that divides either q  +  1 or q - 1, a complete set of results has been available. In this paper, we dedicate to a previously unexplored cases where the length [Formula: see text], subject to the conditions that m is neither a divisor of q - 1 nor q  +  1.

Deep learning-based anomaly detection for precision field crop protection.

Introduction: Precision agriculture relies on advanced technologies to optimize crop protection and resource utilization, ensuring sustainable and efficient farming practices. Anomaly detection plays a critical role in identifying and addressing irregularities, such as pest outbreaks, disease spread, or nutrient deficiencies, that can negatively impact yield. Traditional methods struggle with the complexity and variability of agricultural data collected from diverse sources.

Analysis of the intrinsic value of life in the context of synthetic biology.

The ongoing advancements in synthetic biology, employing either "bottom-up" or "top-down" approaches to construct synthetic life, are generating significant interest. However, the broad application of these scientific practices remains fraught with ethical controversies. Thus, investigating the intrinsic value associated with synthetic life is crucial for determining whether and how synthetic life should be constructed and utilized.

Temporary consolidation and excavation of artifacts at waterlogged archaeological sites.

Ensuring the preservation of fragile artifacts from waterlogged archaeological sites demands the advancement of extraction techniques that uphold both safety and the integrity of historical data. Conventionally employed extraction materials are predominantly applicable to sites exhibiting high soil stability and low moisture content, whereas the recovery of artifacts from waterlogged contexts remains technically challenging. This study explores low-acyl gellan gum as a temporary solidification material for waterlogged archaeological relics, enhancing its efficacy through ionic cross-linking methodologies.

Multi-limited-angle spectral CT image reconstruction based on average image induced relative total variation model.

In recent years, spectral computed tomography (CT) has attracted extensive attention. The purpose of this study is to achieve a low-cost and fast energy spectral CT reconstruction algorithm by implementing multi-limited-angle scans. General spectral CT projection data are collected over a full-angular range of 360 degrees.

Toward a modeling and analysis method of cyber-physical systems architecture evolution based on bigraph.

The evolution of cyber-physical systems (CPS) is inevitable. Traditional graph and hypergraph modeling and analysis methods can only describe one-dimensional evolutionary information, making it difficult to directly apply them to the modeling and analysis of CPS evolution processes that involve two-dimensional space. To address this issue, this paper proposes a Bigraph model for CPS that incorporates positional constraints.

Exploring the influence of "keeping consumers in suspense" in live streaming on consumer impulse buying behavior: A test of the mediating effects of consumer inner states.

Background: Live-streaming e-commerce has emerged as a dynamic shopping medium, engaging consumers through interactive and immersive experiences. "Keeping Consumers in Suspense" (KCIS), encompassing preview fuzziness, suspense setting, and delight creation, is widely used to drive engagement and impulse buying. Still, its impact on consumer inner states and behaviors remains underexplored.

An optimized approach for container deployment driven by a two-stage load balancing mechanism.

Lightweight container technology has emerged as a fundamental component of cloud-native computing, with the deployment of containers and the balancing of loads on virtual machines representing significant challenges. This paper presents an optimization strategy for container deployment that consists of two stages: coarse-grained and fine-grained load balancing. In the initial stage, a greedy algorithm is employed for coarse-grained deployment, facilitating the distribution of container services across virtual machines in a balanced manner based on resource requests.

Measurement and spatio-temporal evolution of high-quality industrial development level in China.

The scientific measurement of high-quality industries development (Abbreviated as HQID) level is crucial for promoting their advancement in China. This measurement holds significant importance in reducing regional differences in HQID among cities and fostering coordinated urban industry development. This study utilizes panel data encompassing 286 cities in China from 2003 to 2022.

Multilingual SEIR public opinion propagation model with social enhancement mechanism and cross transmission mechanisms.

The propagation of public opinion in multilingual environments presents unique challenges due to the diversity of languages, cultures, and values. This study develops an SEIR-based model tailored for multilingual contexts, incorporating mechanisms such as social enhancement, forgetting, and cross-transmission. The model's purpose is to improve transparency, inclusivity, and effectiveness in public opinion management, particularly in diverse linguistic settings.

Environmental policy uncertainty and green transformation dilemma of Chinese enterprises.

With the intensification of environmental issues, environmental policies have played an increasingly important role in the Chinese economic system. The previous literature focuses on the impact of environmental policies on the green transformation of enterprises but pays little attention to policy-related environmental risks. In this way the impact of environmental policy uncertainty on enterprise green transformation remains a black box, which forms the initial motivation of this essay.