Search Results (1,601)

Digital transformation has become a core pathway for enterprises to enhance competitiveness and achieve sustainable development in the digital economy, as it reshapes resource allocation modes and optimizes operational efficiency. This paper takes Chinese A-share listed companies from 2010 to 2021 as the sample and employs text analysis to measure the R&D background of the top manager team (TMT) and enterprise digital transformation (EDT). Based on the ranking of the TMT, a power index is constructed to quantify the power of managers with an R&D background to assess the influence of the R&D background of the TMT on EDT, and a multiple regression model is used to examine the impact of the R&D background of the TMT on EDT, as well as how the power held by these managers with R&D backgrounds within the TMT affects their ability to drive enterprise digital transformation. The results indicate that the R&D backgrounds of TMT members facilitate enterprise digital transformation, which was strengthened when top managers with R&D backgrounds hold greater power. Compared with companies in the growth stage, those in the mature and decline stages experience a more pronounced impact of the TMT’s R&D backgrounds and their power on digital transformation. Additionally, companies in high-tech industries present a more significant boosting effect of managers’ R&D backgrounds and power on EDT compared to those in non-high-tech industries, owing to the closer integration of high-tech industrial development with digital innovation and sustainable technological progress. This study reveals the micro-driving mechanism of enterprise digital transformation from the perspective of top management team characteristics and provides empirical evidence for enterprises to optimize their TMT structure, allocate managerial power rationally, and promote digital transformation oriented to sustainable development. Full article

In recent years, cyberspace governance has become a critical component of national security strategies worldwide. Although social network platforms provide users with convenient channels for expression and information acquisition, unregulated, harmful euphemisms have become increasingly prevalent. These euphemisms disrupt the order of the digital space and trigger secondary harms such as cyberbullying and regional discrimination. Currently, researches on Chinese harmful euphemism detection face three key challenges: the lack of large-scale annotated datasets, the cognitive reasoning deficit in lightweight models, and the latency constraints of Large Language Models (LLMs), which collectively constrain detection performance and real-world generalization. To address these issues, this study first collected a large corpus from social networking platforms and constructed a fine-grained annotated harmful euphemism dataset. Then, a representation learning framework was designed by integrating deep prompt-based chain-of-thought reasoning with multi-head contrastive learning. This framework introduces external knowledge from LLMs to enhance the diversity and precision of semantic representations. Finally, a multi-dimensional semantic perception fusion framework was proposed. It incorporates multiple semantic perception channels and a cross-channel dynamic fusion mechanism, enabling the model to better capture implicit semantics and integrate external contextual knowledge. Experimental results show that our approach significantly outperforms state-of-the-art lightweight models. While large-scale LLMs exhibit superior zero-shot transferability in cross-domain tasks, our proposed model maintains highly competitive performance with substantially lower inference latency and computational overhead. This research provides a novel methodological and technical foundation for detecting harmful euphemisms in social networks. Full article

Purpose: This study explores how older adults with chronic back pain (CBP) evaluate different user interface (UI) designs and gamification elements for an ultrasound-based wearable providing real-time biofeedback during segmental stabilization exercises (SSE). The aim is to identify design preferences and motivational factors to enhance usability, engagement, and adherence in this specific population. Methods: We conducted a mixed-methods study with 15 older adults (aged ≥ 65) experiencing CBP. Participants interacted with three UI mockups (simple, anatomical, and playful) via a Wizard-of-Oz simulation and evaluated additional motivational elements (e.g., points, badges, progress charts). Semi-structured interviews and the Technology Usage Inventory (TUI) subscales were used to assess usability, acceptance, and intention to use. Results: Participants preferred the simple and anatomical UI designs, citing clarity, professionalism, and ease of interpretation. The playful design was viewed as less appropriate due to perceived infantilization. Game elements such as progress tracking, points, and levels were positively received, while competitive features like leaderboards were viewed critically. Most participants expressed interest in integrating pain education, favoring multimedia formats. Conclusions: Digital health tools for older adults must prioritize intuitive, medically reliable interfaces and allow personalization of motivational and educational components. The findings highlight the need for age-appropriate UI design and suggest that well-balanced gamification and educational features may enhance perceived acceptance and have the potential to support long-term use, which should be evaluated in longitudinal studies. Full article

Economic challenges are a latent reality in emerging economies such as Peru, and the growth capacity of entrepreneurs depends largely on certain factors, such as education and financial inclusion. To delve deeper into these factors, this study aims to analyze the association between micro-business performance, education, and financial inclusion, as well as to evaluate the mediating role of financial inclusion in the association between financial education and micro-business performance. The study was of an explanatory design. The research focused on owners, business owners, general managers, and other administrators of micro-businesses who could provide information on the performance of the companies. The results showed a statistically significant positive association between micro-business performance, education, and financial inclusion. It was also proven that financial inclusion is positively associated with micro-business performance, and it was also proven that financial inclusion has a mediating role in the association between financial education and micro-business performance. While these relationships are meaningful, the moderate explanatory power of the model (R² = 0.370–0.488) suggests that financial education and financial inclusion are important but partial contributors to business outcomes in this context. In conclusion, entrepreneurs with stronger financial knowledge appear to be better positioned to navigate business challenges and leverage financial systems, which may contribute to improved micro-business performance indicators. Full article

Soil surface roughness (SSR), referring to surface irregularities, is a key parameter for assessing soil condition and tillage outcomes. Characterizing roughness at fine scales—including clods and depressions—remains challenging for 2.5D digital elevation models (DEMs) collected at the meter scale in the field. This study presents two segmentation methods for high-resolution DEMs from an agricultural site. For clod segmentation, a wavelet-based approach from the literature was used, while a novel histogram-based method was introduced for depressions. Both methods were evaluated on natural soil surfaces with varying roughness levels and a simulated surface, with and without noise, using standard metrics (recall, precision, F1-score, IoU). The best clod segmentation results were achieved on fine seedbeds (95.2% recall, 97.3% precision, 96.2% F1-score), with slightly lower but strong performance on plowed surfaces (84.2% recall, 96.9% precision, 90.1% F1-score). Due to their lower frequency, depressions were primarily assessed visually under field conditions. For the simulated surface (with ground truth), IoU values ranged from 84.2% to 87.9% for clods and around 92% for depressions, demonstrating competitive performance. Additionally, the volume of roughness features was computed and visualized using cumulative distribution functions. These segmentation methods enable monitoring of soil surface conditions, with applications in precision agriculture, surface-water interactions, and meter-scale microwave remote sensing. Full article

The large-scale integration of variable renewable energy sources introduces critical challenges of intermittency and uncertainty, yet consumption forecasting, generation forecasting, and anomaly detection are typically addressed in isolation, neglecting the bidirectional feedback between consumption patterns, generation mix, and public decision-making. This PRISMA 2020-compliant systematic review compared statistical, machine learning, and deep learning models for energy forecasting and machine learning and deep learning models for anomaly detection. Searches in Google Scholar and Scopus used seven targeted strings, restricted to peer-reviewed empirical studies (2022–2026; 2023–2026 for anomaly detection), indexed in Q1–Q3 JCR journals, excluding theoretical and non-benchmarked works. A six-item risk of bias questionnaire—with a threshold of four points—guided inclusion, yielding 60 articles. Addressing the first research question (RQ1) on comparative model performance, hybrid deep learning architectures optimized with bio-inspired metaheuristics achieved the highest forecasting accuracy ($R^2$ up to 0.9984), with metaheuristic optimization acting as a cost-reducing factor; statistical models remained competitive for long-horizon forecasting, while large-language-model-based approaches addressed data scarcity through few-shot learning. Addressing the second research question (RQ2) on smart grid optimization, predictive techniques reduce forecasting errors enabling real-time load adjustment and Demand Response, though a systematic asymmetry constrains their potential: consumption studies integrate socio-economic variables, whereas generation studies rely on meteorological inputs. Addressing the third research question (RQ3) on infrastructure security, supervised and unsupervised approaches detect anomalous operational states and support fault diagnosis, yet remain constrained by scarce labeled fault data and limited cross-regional validation; generative models such as GANs and diffusion models partially address this limitation by enabling Sim2Real strategies and realistic digital twin construction. Evidence is strongest for hybrid forecasting; certainty is lower for anomaly detection given reliance on experimental surrogates. No single paradigm achieves universal superiority. The primary finding is the consistent absence of integrated frameworks jointly modeling consumption, generation, anomaly detection, and public decision-making across the reviewed literature. This result reflects a structural limitation of the current state of the art, rather than a forward-looking research agenda. This study was funded by the ENIA International Chair on Trustworthy Artificial Intelligence European Recovery Plan; the protocol was not pre-registered. Full article

In the context of the digital economy as a driver of economic transformation, digital enterprises have become pivotal actors in value creation and innovation. A conducive business environment is essential for enhancing productivity, competitiveness, and the long-term resilience of entrepreneurial ecosystems. However, the mechanisms through which this environment influences the entry of newborn digital enterprises, a core indicator of sustainable economic activity, remain inadequately explored. This paper develops a government-led business environment index based on three dimensions: the legal environment, the governmental affairs environment, and public services. Using panel data from 35 major Chinese cities spanning 2016 to 2020, we employ a negative binomial regression model to examine how both the overall business environment and its sub-dimensions affect the entry of newborn digital enterprises. The findings reveal that an overall improvement in the urban business environment significantly promotes the entry of newborn digital enterprises and that all three sub-dimensions, namely the legal environment, governmental affairs environment, and public services, collectively facilitate this process. The principal implication is that local governments should focus on the balanced optimization of all business environment elements. Such policies not only stimulate digital startup formation but also contribute to high-quality, resilient, and economically sustainable urban development. Full article

Green finance (GF) is increasingly seen as an important policy tool for promoting sustainable urban development; however, its role in facilitating the green transformation of urban land remains insufficiently understood, particularly from the perspectives of land use efficiency and spatial interactions. This study takes China’s Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment and employs a spatial difference-in-differences framework to examine whether and how GF affects urban land green use efficiency (LGUE). The results indicate that GF significantly improves LGUE in pilot cities, and this finding remains robust across a range of alternative specifications and robustness checks. The mechanism analysis further suggests that GF enhances LGUE primarily by optimizing resource allocation, promoting green innovation, and strengthening information disclosure. In addition, digital development is found to reinforce the positive effects of GF. Compared with existing studies, this paper integrates mechanism analysis with spatial econometric methods to provide a more comprehensive understanding of both the transmission channels and spatial spillover effects of GF. In particular, it provides new evidence on geographically constrained negative spillover effects across cities. The results further indicate that such spillover effects are most pronounced within a 250 km radius, suggesting that GF induces localized inter-city competition and resource reallocation. This finding offers empirical support for understanding the effects of GF from a spatial competition perspective. This study highlights the necessity of coordinating regional policy design to mitigate spatial spillover effects and improve the overall effectiveness of green finance policies. Full article

Digitalization is the driver of regional competitiveness and sustainable development, but its geographical impacts differ significantly across Europe. This study was conducted to determine if digital transformation results in regional sustainability or if it increases spatial inequalities, concentrating on European NUTS-1 regions for the period 2021–2025. A composite Regional Digitalization Index was developed by means of Principal Component Analysis (PCA) based on indicators measuring internet access, internet usage, and the availability of digital public services. Dynamic spatial panel econometric models were used for empirical investigation, including a Spatial Autoregressive (SAR) model and a Spatial Durbin model (SDM), which facilitated the exploration of both temporal dependence and spatial spillover. Three main conclusions can be derived from the results, as follows: The level of digitalization in a region is highly stable over time, whereby the development depends most on previous paths. Subsequently, human capital is highly significant for digital development, and its effects are not only local but also spill over to neighboring regions. Lastly, spatial interactions consist of two opposite forces—the positive diffusion from digitally advanced neighboring regions and the competitive effects related to the economic strength of neighboring regions—that further intensify the core–periphery divide. Full article

Three-phase induction motors account for nearly two-thirds of industrial electricity consumption, making accurate parameter identification essential for efficiency optimization, predictive maintenance, and digital twin calibration. This paper introduces the stochastic spheric navigator algorithm (SSNA) for estimating the equivalent circuit parameters (stator and rotor resistances, leakage reactances, and magnetizing reactance) of induction motors by minimizing the normalized squared error between manufacturer-provided torque characteristics (starting, peak, and full-load) and their analytical counterparts derived from the steady-state Thévenin model. The SSNA employs an adaptive spherical search mechanism with a decaying radius schedule that progressively narrows the exploration neighborhood, enabling a balanced transition from global exploration to local refinement. Validated on 5 hp and 25 hp motors against the genetic algorithm (GA), particle swarm optimizer (PSO), hybrid GA-PSO, and sine–cosine algorithm (SCA), the SSNA demonstrates distinct advantages. For the 5 hp motor, it achieves the lowest errors in maximum torque ($1.34\times {10}^{-4}\%$) and full-load torque ($5.08\times {10}^{-4}\%$). For the previously unreported 25 hp motor, the SSNA yields an objective function value of $4.68\times {10}^{-12}$—six orders of magnitude lower than the SCA—and reduces magnetizing reactance estimation error from 46.55% (SCA) to 16.18%. Statistical analysis over 100 independent runs reveals that the SSNA uniquely combines the lowest minimum (best) value, the lowest maximum (worst) value, and the lowest standard deviation, demonstrating superior accuracy, reliability, and consistency. These results position the SSNA as a highly competitive optimization framework for induction motor parameter identification, with particular suitability for applications demanding high precision and robust performance. Full article

With the intensification of climate change and the acceleration of the low-carbon transition, climate policy uncertainty (CPU) has become a critical external shock shaping firms’ green innovation behavior. Using a panel of Chinese A-share listed firms from 2011 to 2023, this study constructs a firm-level measure of CPU and examines its impact on firms’ green innovation boundaries and the underlying mechanisms. The results show that CPU significantly expands firms’ green innovation boundaries, and this effect is notably obvious in areas with stronger green innovation ecosystems and robust intellectual property protection. Mechanism analyses indicate that green strategic orientation and digital–green technology integration capability play significant partial mediating roles. Moreover, green finance and peer effects significantly strengthen the positive relationship between CPU and green innovation boundaries. Further analyses reveal that expanding green innovation boundaries not only enhances firms’ sustainable green innovation capability but also increases market share, thereby transforming CPU into long-term technological and competitive advantages. Full article

Digitalization and artificial intelligence (AI) are seen as promising pathways for small and medium-sized enterprises (SMEs) to enhance performance while preserving environmental and social resources. This paper identifies organizational determinants of AI maturity that can enable SMEs to use AI in a more sustainable, responsible, and capacity-enhancing manner. AI adoption becomes relevant to sustainability not only because a company adopts advanced technology but because this technology is embedded in leadership practices, employee competencies, interdisciplinary collaboration, and organizational learning. From this perspective, perceived benefits and management commitment are not outcomes of sustainability but mechanisms that help explain how SMEs transition from technological awareness to building organizational capacity. Such capacity building can be a necessary prerequisite for subsequent sustainability-oriented outcomes, such as efficient resource utilization, employee upskilling, responsible AI management, and long-term resilience. We conducted a cross-sectional survey among 402 managers from Polish SMEs (62 micro, 193 small, 147 medium) across manufacturing, services and trade industries. Respondents (mean age ≈ 42.5 years) assessed perceived benefits of AI, engagement of top leadership, AI maturity and competitive pressure. Partial least-squares structural equation modeling revealed that perceived benefits strongly predicted leadership engagement (β = 0.647), explaining 62.8% of its variance. Perceived benefits (β = 0.384) and leadership engagement (β = 0.362) in turn were the key drivers of AI maturity, with the model accounting for 65.5% of variance in AI maturity. Competitive pressure positively but weakly moderated the relationship between perceived benefits and leadership engagement (β = 0.011), while its moderating effect on the relationship between perceived benefits and AI maturity was not significant (β = −0.008). These findings suggest that articulating clear benefits of AI and securing active leadership engagement are more decisive for advancing AI maturity than external competitive pressure. The contribution of the study is to integrate the perceived benefits of AI, top management commitment and AI maturity into a model, empirically validated and interpreted from a socio-technical perspective of sustainable digital transformation in SMEs, while quantifying the moderating role of competitive pressure in the under-researched context of Central and Eastern Europe. For practitioners, investing in awareness of AI’s benefits and developing committed leadership may yield more sustainable digital transformation than reacting solely to external pressures. Full article

In the context of global value chain restructuring and accelerating digital transformation, enterprise competition is increasingly shifting toward sustainable systemic efficiency centered on supply chain operations. Although financial robotic process automation (RPA), as a critical technology enabling financial digitalization, has been widely adopted by firms, its impact on sustainable supply chain operational efficiency (SCOE) and the underlying transmission mechanisms remains underexplored. Drawing on data from Chinese A-share listed firms spanning the period from 2015 to 2024, we investigate the effect of RPA adoption on SCOE. Our analysis reveals that RPA adoption significantly improves firm SCOE, with the effect being more pronounced among non-state-owned enterprises, firms located in eastern and central regions, non-high-tech firms, and large enterprises. Moreover, we identify two underlying mechanisms—enhanced information transparency and optimized capital utilization—as primary channels through which RPA enhances supply chain performance. Further analysis indicates that supply chain concentration (SCC) positively moderates the relationship between RPA adoption and SCOE. These findings provide practical implications for enterprise digital transformation and sustainable supply chain development. Full article

Deploying neural networks on encrypted data requires efficient evaluation of nonlinear activations, especially the ReLU function, without decryption. While the CKKS homomorphic encryption scheme supports packed arithmetic over approximate numbers efficiently, its approximate semantics make direct nonlinear evaluation difficult, and polynomial surrogates often introduce approximation error and non-discrete outputs. In this work, we present a task-specific, non-interactive construction for discrete ReLU evaluation in CKKS by combining modulus-switch-based discretization with interpolation-driven lookup-table (LUT) evaluation. We instantiate this design in two complementary schemes. The first uses trigonometric Hermite interpolation and functional bootstrapping to compute a discrete sign indicator, which is then combined with the encrypted input through conditional multiplication to obtain the ReLU output; this variant is compact and suitable for lightweight settings. The second uses iterative most-significant-bit (MSB) bootstrapping to support larger plaintext moduli and higher-precision regimes through repeated digit extraction. A common enabler of both schemes is a discretization step that maps approximate CKKS plaintexts to a finite integer representation; exactness in our setting therefore refers to exact evaluation over this discretized representation, while the deviation from the original CKKS plaintext is governed by the discretization error analyzed in Lemma 1. Experiments on encrypted MNIST inference and the accompanying LUT/storage analysis indicate that the proposed schemes preserve competitive accuracy relative to polynomial-approximation baselines while maintaining manageable auxiliary storage under the reported parameter settings. These results suggest that interpolation-based discrete activation is a promising alternative to polynomial approximation for selected CKKS-based encrypted inference tasks. Full article

The generation of Digital Surface Models (DSMs) from large-format, high-resolution satellite imagery constitutes a critical component of photogrammetry and computer vision. Achieving efficient, robust, and high-quality DSM reconstruction has therefore become a prominent research focus. However, with the continuous improvement in satellite image resolution and the increasing diversity of image sources, satellite image matching—serving as the fundamental step in DSM generation—still faces significant challenges, including the uneven distribution of feature points and insufficient registration stability in large-scale imagery. To address these issues, this paper presents a refined DSM generation method for high-resolution satellite imagery, termed SAT-MAK. The framework consists of three main stages: (1) sparse matching based on MAK (Multi-type Aggregated Keypoints) extraction; (2) a density-weighted clustering matching optimization strategy; and (3) DSM generation following a conventional photogrammetric pipeline. Experiments were conducted on multiple sets of high-resolution satellite imagery, and the proposed method was compared with four commonly used satellite image 3D reconstruction algorithms. The results demonstrate that, compared with state-of-the-art methods, the proposed SAT-MAK approach improves DSM completeness by 5.29% while maintaining competitive RMSE performance, highlighting its strong potential for practical applications. Full article