Search Results (2,943)

Design coordination is a critical process for avoiding spatial conflicts and ensuring design alignment in large-scale construction projects. While Building Information Modelling (BIM) tools have improved coordination through 3D model integration and clash detection, inefficiencies persist due to fragmented workflows, frequent tool switching, and challenges with issue documentation. Cloud-based BIM collaboration tools offer a promising alternative by enabling real-time model sharing, centralized issue tracking, and enhanced stakeholder communication. However, empirical evidence on their practical implementation and effects on coordination processes remains limited. Unlike prior cloud-BIM reviews that focus on technical capabilities or adoption barriers in isolation, this study provides an empirically grounded framework that links specific tool features to observable workflow changes and their downstream impacts on coordination outcomes. This study investigates the impact of cloud-based BIM collaboration tools on the design coordination process, with a focus on issue identification, resolution, and documentation. A framework was developed using a mixed-methods approach comprising action research, an ethnographic case study, and comparative analysis of three large infrastructure projects to categorize workflow changes resulting from tool adoption. The findings indicate that cloud-based BIM tools streamline coordination by reducing manual transitions, automating documentation, and improving information accessibility during meetings. Nevertheless, their effectiveness is constrained by organizational structures and contract limitations. This study provides a validated process-change framework and practical insights for engineering managers seeking to align digital collaboration tools with project delivery strategies, contributing to both theory and practice in BIM-based coordination and digital transformation in the AEC industry. Full article

In the digital economy era, digital marketing has become a key strategy for firms seeking competitive advantage. However, its reliance on data has heightened exposure to cybersecurity risks. While existing research highlights the importance of digital transformation, less is known about how cybersecurity governance influences firms’ digital marketing activities. Drawing on signalling theory and the resource-based view, this study uses panel data from Chinese A-share listed firms during 2012–2023 to examine the impact of cybersecurity governance on digital marketing and its underlying mechanisms. The results show that effective cybersecurity governance significantly enhances firms’ digital marketing engagement. Mechanism analyses identify three channels. First, by preventing data breaches and negative incidents, firms enhance corporate reputation. Second, by creating a secure operating environment, cybersecurity governance strengthens risk-taking capacity and encourages marketing innovation. Third, by improving information disclosure and stakeholder communication, it alleviates information asymmetry. Heterogeneity analyses indicate that the positive effect is more pronounced for non-state-owned enterprises, firms in eastern regions, and high-tech firms. This study fills a gap in the literature by linking cybersecurity governance path to digital marketing and contributes to research on its economic consequences. The findings also offer practical implications for strengthening internal governance to support external market activities. Full article

This paper presents the H-CoRE (Heterogeneous Cooperative Multi-Robot Execution) framework designed to enable autonomous multi-robot operations in GNSS-denied environments. Built on an ROS 2-based architecture, H-CoRE enables collaborative, structured task execution through standardized software stacks. Each robot’s stack combines a high-level executive system with an agent-specific motion layer and leverages multi-sensor fusion for localization and mapping. The framework is inherently reconfigurable, allowing individual agents to operate autonomously or as part of a multi-robot team for collaborative missions. In the considered scenario, the system integrates aerial and ground vehicles, a fixed pan–tilt–zoom camera, and a human supervisory interface within a unified, modular infrastructure. The proposed system has been deployed in indoor, GNSS-denied environments, demonstrating autonomous navigation, cooperative area coverage, and real-time information sharing across multiple agents. Experimental results confirm the effectiveness of H-CoRE in maintaining general awareness and mission continuity, paving the way for future applications in search-and-rescue, inspection, and exploration tasks. Full article

Armed conflicts are at the epicentre of an information war, amplified by false claims about the motivations of the conflicts and refugees. The spread of narratives, especially in digital media, challenges the European Union to implement effective strategies to combat misinformation and to adopt measures to scrutinise and hold the main communication channels accountable, in order to prevent hostile narratives from influencing public opinion and political decision-makers. In this context, this article seeks to analyse the implications of media discourses and misinformation in the development of social representations about the Russian–Ukrainian war and refugees, as well as the use of social networks by individuals to share this type of content. The research is based on an exploratory study as part of the R&D Project MeInWar—Study on the media and social representations of the Russian-Ukrainian conflict, funded by Europeia University. The study employed a survey method and an online questionnaire applied to a non-probabilistic convenience sample of 222 individuals aged between 18 and 38. The results revealed that media narratives influence attitudes towards refugees and migration policies, and it is clear that factors such as age and gender have an impact on content-sharing practices and the motivations behind them. Full article

The rapid spread of misinformation on social media has emerged as a major societal issue. Over 40% of British social media news-sharers admitted they had shared inaccurate or fake news. The extensive distribution of false information causes public trust deterioration while modifying public opinions and potentially destabilizing social and political systems. There are profound challenges due to this hard-to-detect, hard-to-stop reality and the financials and societal implications are remarkable. As an attempt to limit the challenges created from misinformation this paper introduces some preliminary work on detection of fake news and verification of their reliability based on online content. Large language models (LLMs) are being used along with natural language processing (NLP) techniques to evaluate news articles through their linguistic and contextual characteristics. Several models are compared on how they can typically identify typical indicators of misinformation through the analysis of extensive verified datasets to develop an ability to classify content as authentic or fabricated. This work has been through thorough testing to determine its operational effectiveness and dependability after completion. We present a relatively easy-to-use tool which enables a wide range of people also for those without a background in computer science to easily verify news accuracy before sharing or trusting it. This work could help to stop false information from spreading while promoting fact-based discussions and improving digital literacy skills. The research demonstrates how technology fights the fake news crisis to create an informed digital environment which supports public conversation protection and information integrity in the modern digital age. Full article

Existing research on green transformation predominantly emphasizes “hard constraints” such as carbon taxes and environmental regulations, while neglecting “soft constraints” shaped by environmental sentiment expressions from key actors such as the public, financial institutions, media, and government. In particular, the collective influence of these multi-actor environmental sentiments remains insufficiently explored. This study fills that gap by constructing a collaborative governance framework using multi-source heterogeneous data from China spanning 2013–2023, including 330 provincial government work reports, 1862 bank annual reports, 2472 newspaper articles, and 68,519 Weibo posts, matched to 4708 firm-year observations of Chinese A-share energy companies. We quantify environmental sentiment tendencies through natural language processing, calculating the index as (negative word frequency − positive word frequency)/total word frequency at the province-year level, thus higher index value indicates more negative sentiment tendency, while green transformation is proxied by ln(green patent applications + 1). The results reveal the following: (1) More negative environmental sentiment tendencies from financial institutions, media, public, and government significantly promote green transformation in energy enterprises, with stronger effects observed from financial institutions and government. (2) Economic and climate policy uncertainty selectively weaken the impact of financial institutions’ sentiment, while the moderating effects for other actors are statistically insignificant. (3) The effect of multi-actor environmental sentiment is more pronounced for firms located in eastern China, operating under high competition or stricter environmental regulations. This study provides a novel, quantified approach to assessing multi-actor environmental sentiment tendencies, affirms the effectiveness of informal governance, and highlights the importance of stable policy in guiding corporate green transformation in emerging economies. Full article

This study utilizes digital transformation and patent data from A-share listed companies on the Shanghai and Shenzhen stock exchanges in China between 2011 and 2021 to examine the influence of digital transformation on the quality of cross-regional collaborative innovation. The findings reveal that the cooperative innovation network exhibits pronounced small-world characteristics. In terms of spatio-temporal evolution, China’s urban collaborative innovation network demonstrates a notable quadrilateral spatial structure and has evolved toward a multicenter pattern. Moreover, the advancement of digital transformation positively contributes to both the quality and quantity of cross-regional cooperative innovation. By enhancing the relational embeddedness among cities, digital transformation facilitates improved outcomes in collaborative innovation. Furthermore, when the volume of digital patent applications surpasses a certain threshold, its positive effect on the quality of cross-regional collaborative innovation accelerates. These results provide empirical evidence from a major emerging economy, offering insights that can inform policies and strategies in other regions undergoing digital transition. The mechanisms identified, such as network structure evolution and relational embeddedness, contribute to a broader understanding of how digital transformation shapes innovation dynamics across geographical boundaries in a globalized knowledge economy. Full article

Cross-institutional collaborative fraud detection is essential for combating increasingly sophisticated financial fraud, yet privacy regulations and data silos severely constrain knowledge sharing among institutions. This study aims to develop a privacy-preserving framework that enables effective collaborative fraud detection while protecting raw data, with particular emphasis on exploiting symmetry properties in federated architectures and graph topology analysis. We propose an Adaptive Federated Graph Attention Network (FedGAT), which employs spatio-temporal graph attention mechanisms to capture topological structures and dynamic fraud patterns within institutional transaction networks. The framework introduces a symmetric similarity matrix derived from graph topological features, where the symmetry property ($s_{ij}=s_{ji}$) ensures consistent and unbiased measurement of structural relationships between any pair of institutions. Based on this symmetric similarity metric, an adaptive weighted aggregation mechanism is designed for cross-institutional parameter fusion, enabling balanced knowledge transfer that respects the symmetric collaborative relationship among participating institutions. The symmetric information exchange protocol between local institutions and the central server further guarantees equitable contribution and benefit distribution throughout the federated learning process. The framework is evaluated on the Elliptic Bitcoin transaction dataset and the IEEE-CIS fraud detection dataset, with recall rate and false positive rate as primary performance metrics. Results show that FedGAT achieves a recall of 0.85 and a false-positive rate of 0.038 in single-institution detection, representing approximately 40% and 70% improvements over existing methods, respectively. In collaborative detection across five virtual institutions, the symmetry-aware adaptive aggregation mechanism enables all participants to achieve performance gains exceeding 15% while completely eliminating negative transfer effects observed in simple averaging approaches. This work contributes a novel symmetry-based federated learning framework that balances privacy protection with detection performance, advancing the literature on cross-institutional financial risk management. Full article

Background/Objectives: The medical mushroom Ophiocordyceps sinensis (Caterpillar Fungus), known for its ability to enhance “vitality,” is one of the most popular medicines in Asian traditional medical systems. According to the Chinese Pharmacopeia, O. sinensis is standardized for its adenosine content, the precursor of ATP, which mediates numerous physiological and pathological processes in many diseases. The related fungus of order Hypocreales, Cordyceps militaris, and its major bioactive constituents, 3′-deoxyadenosine (cordycepin), also exhibit pleiotropic biological activities. This review aims to provide a rationale for the adaptogenic and resilience-supporting effects of these medicinal fungi and to align food and drug regulation in Western countries. Methods: In this narrative review, we integrated results from chemical, pharmacokinetic, network pharmacology, preclinical, and clinical studies of O. sinensis, C. militaris, and cordycepin using network pharmacology and bioinformatics tools. Results: Across studies, recurrent mechanistic hubs included PI3K–Akt, AMPK–mTOR, MAPK, NF-κB, apoptosis, and adaptive stress-response signaling pathways, linking immune regulation and metabolic homeostasis. Experimental studies confirmed modulation of cytokine production, kinase signaling, and mitochondrial regulators. Clinical meta-analyses demonstrate consistent adjunctive benefits in renal and pulmonary disorders, although heterogeneity in preparation and methodological limitations remains significant. The review reveals controversy regarding the bioavailability of cordycepin in vivo and its concentration in vitro studies, raising the hypothesis that cordycepin may act as a driver, triggering the organism’s adaptive stress response in stress-induced and aging-related diseases. Pharmacokinetic data indicate that systemic cordycepin concentrations after oral administration remain in the nanomolar range, suggesting that some predicted molecular interactions may occur indirectly or through systems-level mechanisms. The review, for the first time, suggests establishing a regulatory category for resilience-supporting physiological modulators to align food and drug regulation in the EU with contemporary systems biology, thereby complementing the work of EFSA, EMA, FDA, and Asian authorities. Conclusions:O. sinensis, C. militaris, and 3-deoxyadenosine share a common adaptogenic mechanism for maintaining homeostasis of cellular and integrated biological system functions. The systems-level network analysis and reductionistic molecular ligand preceptor pharmacology provide complementary approaches for understanding the multi-target bioactivity of these fungi. This review clarifies conceptual and regulatory barriers to recognizing resilience-supporting interventions and informs future regulatory innovation. Full article

This study investigates the ESG rating effect on firm financing by evaluating rating divergence data from five rating agencies, focusing on China’s A-share listed firms spanning 2018–2023. Empirical findings reveal: (1) ESG rating divergence has negatively exacerbated the financing constraints of enterprises. (2) Economic policy uncertainty in China moderates this relationship, significantly amplifying the financing constraint effect of ESG rating divergence. (3) Parallel intermediation tests the negative impact of information asymmetry and debt capital costs jointly transmitting discrepancies. (4) Deeper analysis shows non-state-owned enterprises, small-scale businesses, firms in less financially marketized regions, and entities with high rating divergence face more notable effects. This study explores the internal operation logic of ESG rating discrepancies on corporate financing constraints through two parallel channels of information asymmetry and debt capital cost. The research conclusions provide empirical support for regulators to promote the standardization of ESG information disclosure, assist investors in improving the risk pricing system, and improve the efficiency of market resource allocation. Full article

In underwater target recognition scenarios, challenges arise as a result of the limited representational capability of acoustic images with single time-frequency features and poor recognition performance due to class imbalances in sample numbers. To tackle these issues, this paper proposes an underwater target recognition method based on feature fusion and balanced ensemble transfer learning. A LiT-INN dual-branch auto-encoder network architecture is employed for time-frequency image feature fusion to solve the weak feature representation capability of single time–frequency features. The Restormer network serves as a shared feature encoder to extract fundamental features, enabling feature fusion of underwater target echo time–frequency image data and generating a fusion image dataset with richer feature information. In order to address class imbalance in sample sizes, a balanced ensemble transfer learning method is constructed using a two-stage decoupled fine-tuning learning method. The first stage employs a uniform sampler strategy to fine-tune the feature extraction module of a pre-trained transfer learning model. The second stage uses multiple balanced sampling optimization methods to fine-tune the classifier. Then, a weight averaging ensemble learning method performs decision-level fusion of multiple weak classifiers. Field test data from three target classes validated the performance of the algorithm, demonstrating a 3% improvement in average recognition accuracy compared to deep transfer learning methods under different imbalance ratios. This method effectively enhances recognition performance for classes with limited samples while significantly boosting overall recognition accuracy, offering a novel solution for underwater target recognition. Full article

Agricultural biomass recycling efficiency is central to advancing the green and sustainable transition of agriculture. Drawing on panel data for 30 Chinese provinces from 2019 to 2023, this study measures recycling efficiency using a three-stage super-efficiency SBM model with undesirable output and examines its determinants with a panel Tobit model. The second-stage SFA indicates that the effects of external conditions on input slacks are input-specific. In particular, GDP is statistically significant only in the biomass-generation slack equation, whereas topographic relief and rural road network density do not show robust associations with any slack measure once controls are included. After removing the influence of environmental factors and random shocks, the overall national level of agricultural biomass recycling efficiency remains moderate. The national mean Stage 3 efficiency decreased from 0.586 in 2019 to 0.427 in 2022 and recovered to 0.543 in 2023. The five-year average was 0.510, which is close to the Stage 1 average of 0.503. Spatial analysis indicates weak global spatial autocorrelation, with only occasional local clustering. The efficiency centroid oscillated during the study period rather than following a one-way migration path, with a total displacement of 70.05 km. The determinant analysis indicates that the number of specialised agricultural machinery has the most stable positive association with recycling efficiency, while other policy, market, and human capital variables do not show robust significance in the short panel. These findings underline the need to align equipment deployment and collection systems with local terrain and transport conditions, expand machinery leasing and service provision, and strengthen capacity building in low-efficiency regions. Establishing a national information sharing and dispatch platform would facilitate cross-regional resource flows and more efficient allocation, while improving local service outlets would make participation more convenient for farmers and reduce transaction costs. Full article

In Social Internet of Vehicles (SIoV) environments, fog computing plays a crucial role in supporting real-time services by reducing the latency inherent in cloud-based architectures. However, fog nodes are typically deployed in physically exposed roadside environments and can be operated by several system operators, making them vulnerable to physical compromise and unauthorized access. Despite these threats, many existing authentication schemes assume fog nodes to be fully trusted or honest-but-curious, allowing them to decrypt transmitted data using a session key shared among vehicles, fog nodes, and cloud servers. To overcome these limitations, this paper proposes a quantum-secure pairwise key agreement scheme that establishes distinct session keys for vehicle–fog, fog–cloud, and vehicle–cloud communications. This design effectively prevents the disclosure of sensitive information even in the event of fog node compromise. Furthermore, Physical Unclonable Functions (PUFs) are employed to mitigate physical capture attacks, while lattice-based cryptography based on the Module Learning with Errors (MLWE) problem is integrated to ensure resistance against quantum computing attacks. The security of the proposed protocol is rigorously validated through formal analysis using AVISPA, BAN logic, and the Real-or-Random (RoR) model, in addition to informal security analysis. Comparative performance evaluations against related schemes demonstrate that the proposed approach achieves a balance between efficiency and security, making it well suited for practical deployment in SIoV environments. Full article

Rigid internal fixation has long been the standard for fracture management; however, excessive construct stiffness can suppress interfragmentary strain, reduce callus formation, and impair secondary fracture healing. Low-elastic-modulus TiNbSn alloys have emerged as a promising alternative, offering mechanical behavior closer to that of cortical bone. This review synthesizes representative preclinical and computational evidence to clarify the mechanobiological rationale for TiNbSn alloy plates in fracture fixation. We summarize key biological requirements for secondary fracture healing, including controlled interfragmentary strain, preservation of vascularity, and effective load sharing, and contrast these with the limitations of conventional high-stiffness fixation plates, such as stress shielding and reduced callus formation. Finite element analyses from previously reported models illustrate qualitative trends toward increased axial displacement, favorable stress distribution, and within a biologically relevant range for endochondral ossification. Consistent findings from animal fracture models further indicate enhanced periosteal and intramedullary callus formation and more physiological healing patterns with TiNbSn plates compared with rigid fixation. Emerging clinical experience with TiNbSn femoral stems provides indirect support for the long-term potential of low-elastic-modulus titanium alloys to mitigate stress shielding; however, such findings should be interpreted only as indirect supportive evidence, as stem implantation and fracture plate fixation involve substantially different mechanical and biological contexts. Collectively, these observations provide preliminary support for the mechanobiological rationale of low-modulus TiNbSn plates and suggest their potential role as biologically informed fixation devices, while highlighting the need for further clinical validation. Full article

Industrial metaverse systems enable shared, immersive environments for coordinating complex, data-intensive industrial workflows; however, ensuring effective and usable interaction remains a key barrier to professional adoption. This study examines immersive point cloud- and CAD-based inspection tasks in an industrial metaverse context using a mixed-methods evaluation that combines perceived usability ratings, cognitive workload assessment (NASA-TLX), validated presence and flow instruments, qualitative interviews, and structured observation. The results indicate that users generally experienced smooth navigation, manageable cognitive workload, and a meaningful sense of spatial presence, supporting focused and task-oriented engagement. At the same time, execution-level challenges—particularly related to tool discoverability, annotation flexibility, system feedback clarity, and interaction ergonomics—introduced workflow friction for some users. By triangulating quantitative, qualitative, and observational evidence, the study derives actionable design recommendations, including adaptive onboarding, improved feedback mechanisms, and refinements to interaction design. Overall, the findings provide empirical insight into how usability, cognitive workload, presence, and flow jointly shape user experience in industrial metaverse inspection environments and inform the development of more robust, user-centered industrial systems. Full article