Search Results (1,555)

Against the backdrop of China’s fast-growing digital economy and its financial inclusion agenda, there is still little city-level evidence on whether the e-CNY pilot accelerates financial deepening at the grassroots. Using a balanced panel of 271 prefecture-and-above cities for 2016–2022, this study employs a staggered difference-in-differences (SDID) design augmented by the synthetic control method (SCM) to rigorously identify the policy effect of the e-CNY pilot. The results show that the pilot program significantly improves urban financial inclusion, contributing to more equitable access to financial services and supporting inclusive socio-economic development. Mechanism analysis suggests that the effect operates mainly through two channels, a merchant-coverage channel and a transaction-scale channel, with the former contributing the majority of the overall effect. Incorporating a migration-based mobility index shows that most studies’ focus on the merchant-coverage effect is amplified in cities under tight mobility restrictions but wanes where commercial networks are already saturated, whereas the transaction-scale channel is largely insensitive to mobility shocks. Heterogeneity tests further indicate stronger gains in non-provincial capital cities and in the eastern and central regions. Overall, the study uncovers a “penetration-inclusion” network logic and provides policy insights for advancing sustainable financial inclusion through optimized terminal deployment, merchant incentives, and diversified scenario design. Full article

The use of low-resolution analog-to-digital converters (ADCs) in receivers has emerged as an effective solution for reducing power consumption in millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) systems. However, low-resolution ADCs also pose significant challenges for channel estimation. To address this issue, we propose a joint uplink/downlink (UL/DL) channel estimation algorithm that utilizes the spatial reciprocity of frequency division duplex (FDD) to improve the estimation of quantized UL channels. Quantified UL/DL channels are concentrated at the BS for joint estimation. This estimation problem is regarded as a compressed sensing problem with finite bits, which has led to the development of expectation-maximization-based quantitative generalized approximate messaging (EM-QGAMP) algorithms. In the expected step, QGAMP is used for posterior estimation of sparse channel coefficients, and the block maximization minimization (MM) algorithm is introduced in the maximization step to improve the estimation accuracy. Finally, simulation results verified the robustness of the proposed EM-QGAMP algorithm, and the proposed algorithm’s NMSE (normalized mean squared error) outperforms traditional methods by over 90% and recent state-of-the-art techniques by 30%. Full article

This paper investigates point-to-point multimodal digital semantic communications in a task-oriented setup, where messages are classified at the receiver. We employ a pre-trained transformer model to extract semantic information and propose three methods for generating semantic codewords. First, we propose semantic quantization that uses quantized embeddings of source realizations as a codebook. We investigate the fixed-length coding, considering the source semantic structure and end-to-end semantic distortion. We propose a neural network-based codeword assignment mechanism incorporating codeword transition probabilities to minimize the expected semantic distortion. Second, we present semantic compression that clusters embeddings, exploiting the inherent semantic redundancies to reduce the codebook size, i.e., further compression. Third, we introduce a semantic vector-quantized autoencoder (VQ-AE) that learns a codebook through training. In all cases, we follow this semantic source code with a standard channel code to transmit over the wireless channel. In addition to classification accuracy, we assess pre-communication overhead via a novel metric we term system time efficiency. Extensive experiments demonstrate that our proposed semantic source-coding approaches provide comparable accuracy and better system time efficiency compared to their learning-based counterparts. Full article

The rapid development of the digital economy and the pursuit of green transformation are reshaping the innovation landscape of Chinese firms. However, limited attention has been paid to how digital industry agglomeration (DIA) influences corporate green innovation (CGI) at the firm level. Drawing on panel data from China’s A-share listed firms between 2017 and 2021, this study examines the differential effects of specialized agglomeration and diversified agglomeration of digital industry on CGI. The results indicate that DIA can promote CGI, with a 1% increase in DIA associated with a 1.503% increase in green innovation output. Further analysis reveals that specialized agglomeration exerts a significant positive effect, while diversified agglomeration has no evident impact. Our mechanism analysis indicates that knowledge spillovers serve as the key channel through which DIA fosters CGI. Moreover, heterogeneous effects analysis indicates that DIA exerts a stronger influence on non-high-tech enterprises and in regions where environmental regulation is less stringent. Drawing on these insights, fostering specialized digital clusters and strengthening knowledge-sharing mechanisms can help alleviate existing constraints on innovation diffusion, accelerating green innovation and supporting long-term sustainability. Full article

This paper investigates the error performance of cooperative decode-and-forward (DF) multiple-input multiple-output (MIMO) systems employing one-bit analog-to-digital converters (ADCs) over Rayleigh fading channels. In cooperative DF MIMO systems, detection errors at the relay may propagate to the destination, thereby degrading overall detection performance. Although joint maximum likelihood detection can efficiently mitigate error propagation by leveraging probabilistic information from a source-to-relay link, its computational complexity is impractical. To address this issue, an approximate maximum likelihood (AML) detection scheme is introduced, which significantly reduces complexity while maintaining reliable performance. However, its analysis under one-bit ADCs is challenging because of its nonlinearity. The main contributions of this paper are summarized as follows: (1) a tractable upper bound on the pairwise error probability (PEP) of the AML detector is derived using Jensen’s inequality and the Chernoff bound, (2) the asymptotic behavior of the PEP is analyzed to reveal the achievable diversity gain, (3) the analysis shows that full diversity is attained only when symbol pairs in the PEP satisfy a sign-inverted condition and the relay correctly decodes the source symbol, and (4) the simulation results verify the accuracy of the theoretical analysis and demonstrate the effectiveness of the proposed analysis. Full article

This review investigates recent developments in cardiac mechano-electrical-fluid interaction (MEFI) modeling, with a focus on multiphysics simulation platforms and digital twin frameworks developed between 2015 and 2025. The purpose of the study is to assess how computational modeling methods—particularly finite element and immersed boundary techniques, monolithic and partitioned coupling schemes, and artificial intelligence (AI)-enhanced surrogate modeling—capture the integrated dynamics of cardiac electrophysiology, tissue mechanics, and hemodynamics. The goal is to evaluate the translational potential of MEFI models in clinical applications such as cardiac resynchronization therapy (CRT), arrhythmia classification, atrial fibrillation ablation, and surgical planning. Quantitative results from the literature demonstrate <5% error in pressure–volume loop predictions, >0.90 F1 scores in machine-learning-based arrhythmia detection, and <10% deviation in myocardial strain relative to MRI-based ground truth. These findings highlight both the promise and limitations of current MEFI approaches. While recent advances improve physiological fidelity and predictive accuracy, key challenges remain in achieving multiscale integration, model validation across diverse populations, and real-time clinical applicability. The review concludes by identifying future milestones for clinical translation, including regulatory model certification, standardization of validation protocols, and integration of patient-specific digital twins into electronic health record (EHR) systems. Full article

This study investigates the effect of the Green Finance Reform Pilot Zone Policy (GFRPZP) on corporate sustainable development performance (SDP) using a multi-period difference-in-differences (DIDs) regression model. This model incorporates control variables, reflecting firm-level characteristics and regional economic conditions. The results show that GFRPZP significantly enhances corporate SDP, with stronger effects observed among non-state-owned enterprises (Non-SOEs), companies situated in eastern regions, those in non-heavily polluting industries, and high-tech companies. Mediation analysis indicates that the policy enhances sustainable development through four main channels: improving the quality and quantity of green innovation, easing financing constraints, and increasing analyst attention. Moderation analysis further demonstrates that digital transformation and internal control strengthen the policy’s effect. Full article

This study investigates the cybersecurity landscape of Aotearoa-New Zealand through a culturally grounded lens, focusing on the integration of Indigenous Māori values into cybersecurity frameworks. In response to escalating cyber threats, the research adopts a mixed-methods and interdisciplinary approach—combining surveys, focus groups, and case studies—to explore how cultural principles such as whanaungatanga (collective responsibility) and manaakitanga (care and respect) influence digital safety practices. The findings demonstrate that culturally informed strategies enhance trust, resilience, and community engagement, particularly in rural and underserved Māori communities. Quantitative analysis revealed that 63% of urban participants correctly identified phishing attempts compared to 38% of rural participants, highlighting a significant urban–rural awareness gap. Additionally, over 72% of Māori respondents indicated that cybersecurity messaging was more effective when delivered through familiar cultural channels, such as marae networks or iwi-led training programmes. Focus groups reinforced this, with participants noting stronger retention and behavioural change when cyber risks were communicated using Māori metaphors, language, or values-based analogies. The study also confirms that culturally grounded interventions—such as incorporating Māori motifs (e.g., koru, poutama) into secure interface design and using iwi structures to disseminate best practices—can align with international standards like NIST CSF and ISO 27001. This compatibility enhances stakeholder buy-in and demonstrates universal applicability in multicultural contexts. Key challenges identified include a cybersecurity talent shortage in remote areas, difficulties integrating Indigenous perspectives into mainstream policy, and persistent barriers from the digital divide. The research advocates for cross-sector collaboration among government, private industry, and Indigenous communities to co-develop inclusive, resilient cybersecurity ecosystems. Based on the UTAUT and New Zealand’s cybersecurity vision “Secure Together—Tō Tātou Korowai Manaaki 2023–2028,” this study provides a model for small nations and multicultural societies to create robust, inclusive cybersecurity frameworks. Full article

To respond to China’s sustainable development goals, this study uses a dynamic panel data set (2009–2023) and the PSM-DID model to examine how blockchain adoption impacts corporate sustainable development performance (CSDP). The results show that blockchain significantly enhances CSDP by 9.8–12.3%, primarily through two channels (reducing financing constraints by improving transparency and decreasing chairman-CEO duality) to optimize governance. Regional environmental regulation strengthens this relationship. Heterogeneity analysis reveals stronger impacts in unregulated industries, private firms, and central–western regions, while state-owned firms show policy-driven governance improvements. The study enriches the understanding of blockchain’s dual role in balancing efficiency and sustainability, offering insights for integrating digital technology into green policy frameworks. Full article

Currently, quantum steganography schemes utilizing the least significant bit (LSB) approach are primarily optimized for fixed-point data processing, yet they encounter precision limitations when handling extended floating-point data structures owing to quantization error accumulation. To overcome precision constraints in quantum data hiding, the EPlsb-MFQS and MVlsb-MFQS quantum steganography algorithms are constructed based on the LSB approach in this study. The multichannel floating-point quantum representation of digital signals (MFQS) model enhances information hiding by augmenting the number of available channels, thereby increasing the embedding capacity of the LSB approach. Firstly, we analyze the limitations of fixed-point signals steganography schemes and propose the conventional quantum steganography scheme based on the LSB approach for the MFQS model, achieving enhanced embedding capacity. Moreover, the enhanced embedding efficiency of the EPlsb-MFQS algorithm primarily stems from the superposition probability adjustment of the LSB approach. Then, to prevent an unauthorized person easily extracting secret messages, we utilize channel qubits and position qubits as novel carriers during quantum message encoding. The secret message is encoded into the signal’s qubits of the transmission using a particular modulo value rather than through sequential embedding, thereby enhancing the security and reducing the time complexity in the MVlsb-MFQS algorithm. However, this algorithm in the spatial domain has low robustness and security. Therefore, an improved method of transferring the steganographic process to the quantum Fourier transformed domain to further enhance security is also proposed. This scheme establishes the essential building blocks for quantum signal processing, paving the way for advanced quantum algorithms. Compared with available quantum steganography schemes, the proposed steganography schemes achieve significant improvements in embedding efficiency and security. Finally, we theoretically delineate, in detail, the quantum circuit design and operation process. Full article

As inevitable outcomes of the digital economy’s low-carbon development, green data centers play a crucial role in environmental impact and underlying mechanisms. This study focuses on green data center establishment as a representative practice, utilizing Chinese A-share listed companies and urban data from 2009 to 2023 to construct a multi-period difference-in-differences model. From a supply chain perspective, we investigate the impact of green data centers on corporate carbon emissions and their mechanisms. The results demonstrate that regional establishment of green data centers significantly promotes corporate carbon emission reduction, with conclusions remaining robust after a series of comprehensive robustness and endogeneity tests. This process primarily operates through two channels: green total factor energy efficiency and green attention. Green data center establishment significantly enhances green total factor energy efficiency and corporate green attention. The more developed the regional digital infrastructure and the higher the computing power development levels, the stronger the incentive effect on corporate carbon reduction. Heterogeneity analysis reveals that green data centers have more significant promoting effects on carbon emission reduction in state-owned enterprises and high-tech enterprises. This research contributes to a deeper understanding of the effects, mechanisms, and regional variations related to green data centers in facilitating corporate carbon emission reduction. Full article

Smart cities leverage digital marketing to promote sustainability and build a distinctive global branding. Despite its growing significance, the role digital marketing in smart city development remains underexplored. This study aims to fill this gap by employing bibliometric analysis of 1908 articles indexed in the Scopus database (2000–2024), using the Bibliometrix R-Studio (version 1.4.1743) and VOSviewer (version 1.6.20). The analysis reveals two thematic clusters: (1) “Digital Innovation and Sustainability”, which emphasizes technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data for energy efficiency and green urban development; and (2) “Governance and Policy”, which highlights digital marketing’s role in enabling participatory governance, citizen engagement, and inclusive urban policies. Findings underscore that digital marketing is not only a strategic communication channel but also a driver of sustainable urban transformation. By synthesizing insights from urban planning, technology, and sustainability, this paper provides a novel perspective on the intersection of digital marketing and smart cities. The results provide valuable guidance for policymakers, city planners, and researchers to harness digital marketing in promoting sustainability and further develop the smart city concept. Full article

The dissemination and practice of green consumption behavior is an important issue in promoting sustainable development. With the advent of the digital age, social media platforms have become an important channel for promoting green consumption. The expression of language style has become an increasingly important factor influencing consumer attitudes. From the perspective of consumer perception, this study used three situational simulation experiments (n total = 304) to explore the mechanism by which the “Versailles Literature” language style impacts the feelings and behaviors of audiences of the green consumption behavior of the poster, and to examine the mediating roles of ostentation perception and hypocrisy perception. Data analysis was conducted using SPSS. The research findings showed that, compared with “non-Versailles Literature”, this style significantly reduces positive attitudes toward green consumption while increasing perceptions of bragging and hypocrisy. Furthermore, the strength of social ties between the consumer and the poster plays a moderating role in the effect of language style; specifically, when posts come from strangers, consumers perceive a stronger sense of bragging and hypocrisy. The research results will provide practical guidance for individuals and enterprises to effectively promote the concept of green consumption on social media, helping enterprises avoid the negative reactions brought about by conspicuous green consumption behaviors and exaggerated or false promotion of environmental behaviors, such as “greenwashing”. Full article

This study examines how students perceive factors contributing to their sense of belonging (SoB) in an online project-based master’s course in social innovation, incorporating collaboration with external stakeholders and structured around real-world challenges from external partners. SoB has been shown to impact academic outcomes positively, but online teamwork and collaboration outside the institution walls present unique challenges. The study adopts a qualitative approach and an interpretivist standpoint to find meaningful aspects and generate an understanding of positions among the students. Data was gathered through interviews, allowing students to express their unique experiences and perspectives on SoB in the context of a master’s course, with participants from several countries. Thematic analysis of the interview data identified the following seven categories related to students’ perceived belonging: team formation and trust, personal introductions, group continuity, access to teachers, use of informal communication channels, shared tools, and digital fluency. Furthermore, working towards a common challenge or goal seems to promote SoB among team members. The results indicate that SoB was shaped by multiple interrelated factors, with team-based collaboration and structured group dynamics playing a central role. The study contributes to ongoing research on student belonging by identifying specific practices that may support SoB in digitally supported, team-oriented learning environments. Full article

Forest harvesting can alter sedimentary processes in catchments by reducing vegetation cover and exposing the soil surface. To mitigate these effects, post-harvest residue management is commonly used, though its effectiveness needs individual evaluation. This study assessed how windrowed harvest residues influence structural sediment connectivity in two forest catchments in south-central Chile with a Mediterranean climate. Using digital terrain models and the Index of Connectivity, scenarios with and without windrows were compared. Despite similar windrow characteristics, effectiveness varied between catchments. In catchment N01 (12.6 ha, average slope 0.28 m m⁻¹), with 13.6% windrow coverage, connectivity remained unchanged, but in contrast, catchment N02 (14 ha, average slope 0.27 m m⁻¹), with 21.9% coverage, showed a significant connectivity reduction. A key factor was windrows’ orientation: 83.9% aligned with contour lines in N02 versus 58.6% in N01. Distance to drainage channels also played a role, with the decreasing effect of connectivity at 50–60 m in N02. Bootstrap analysis confirmed significant differences between catchments. These results suggest that windrow configuration, particularly contour alignment, may be more critical than coverage percentage. For effective connectivity reduction, especially on moderate to steep slopes, forest managers should prioritize contour-aligned windrows. This study enhances our understanding of structural sediment connectivity and offers practical insights for sustainable post-harvest forest management. Full article