Search Results (420)

Urban canopy height is an essential indicator for characterizing vegetation structure and carbon sequestration, yet satellite LiDAR often lacks sufficient spatial resolution, airborne LiDAR is costly, and SAR has limited sensitivity to vegetation structure. This study proposes a canopy height inversion framework using high-resolution stereo pairs from the Gaofen-7 (GF-7) satellite. A 0.65 m Digital Surface Model (DSM) was generated from GF-7 data, and a relative surface height was derived by differencing the GF-7 DSM from a coarse 30 m DSM reference. Key features were selected via Boruta and Random Forest Recursive Feature Elimination (RF-RFE), and six models—linear, polynomial, support vector machine, backpropagation neural network, XGBoost, and RF—were compared. The results showed that the Boruta feature set improved average R² by 8.2%. Among all models, RF performed best (test set R² = 0.71, RMSE = 1.70 m) and exhibited the strongest resistance to overfitting. Canopy heights within Beijing’s Fifth Ring Road showed an “outer-high, inner-low” pattern: large parks exceeded 30 m, while the Central Business District remained below 3 m. GF-7 stereo pairs enable efficient and cost-effective retrieval of canopy height in fragmented urban green spaces, supporting ecological parameter quantification and urban green-space management. Full article

Cities in climatic transition zones face coupled radiative and evaporative stresses, and their carbon emission mechanisms differ significantly from those in humid regions. Taking Xi’an, a typical megacity in the transition zone, as a case study, this research utilises a 500 m × 500 m grid to integrate multi-source data for carbon emission accounting. By applying spatial autocorrelation and the Multi-scale Geographically Weighted Regression (MGWR) model, this study examines the spatial heterogeneity of carbon emissions and the mechanisms through which urban planning influences them. The results indicate that carbon emissions in Xi’an exhibit a “core–periphery” agglomeration pattern, with commercial land use exhibiting the highest emission intensity. Carbon emissions and land surface temperature are spatially coupled, consistent with a hypothesised positive feedback loop of the “dry heat island” effect. Morphological factors exhibit spatial non-stationarity: floor area ratio is positively associated with emissions in the old city centre, whereas mutual shading among super-high-rise buildings in the High-Tech Zone coincides with a weaker effect. Building density shows a positive association only where ventilation is limited. Land use mix and blue–green spaces show non-linear negative associations with emissions, with higher marginal benefits in arid–hot environments. This study proposes carbon reduction strategies for the renewal of old urban areas, business cores, and new ecological districts, providing empirical evidence and decision-making references for low-carbon spatial planning in cities within the climatic transition zone. Full article

To address the problems of blurred lesion boundaries, noise interference, and the lack of lightweight design in segmentation models for breast ultrasound images, this paper proposes a lightweight, high-real-time segmentation model, MV-UNet, based on Mamba architecture. The model employs an improved MambaVision encoder paired with a UNetMamba decoder. This architecture, augmented by a Local Supervision Module (LSM) during training, effectively integrates global context with local details while maintaining linear computational complexity, thereby enhancing boundary delineation capability. The experimental results on the BUSI_WHU dataset show that the MV-UNet achieves 90.51% in mIoU, 90.85% in Recall, and 4.59 pixels in ASSD, surpassing most of the existing advanced models in multiple metrics. At the same time, the number of parameters is only 14.7% of the EMGANet, and the inference speed is increased by 3.2 times. Furthermore, an independent benchmark test on the BUSI dataset demonstrates the model’s practical efficiency, achieving an ASSD of 14.94 pixels while maintaining its clear advantages in model lightness and inference speed. In summary, the excellent balance between segmentation accuracy and model efficiency achieved by MV-UNet provides a novel and effective approach for breast ultrasound image segmentation. Full article

European football has become a massive business. Keeping football clubs financially viable depends on accurate player valuations, which underpin balancing incoming and outgoing transfers, contract negotiations, and other expenses. Players’ market values are generally available on public platforms. Still, clubs and analysts increasingly rely on data-driven approaches to enable consistent valuation across leagues, to assess the main drivers of players’ market value, and to early identify the most promising players. This study attempts to predict and interpret football players’ market value in five major European football leagues (England, Spain, Italy, Germany, and France) using match-derived performance statistics and players’ general information. The dataset analyzed comprises about 14,000 player–season observations available through the worldfootballR package, which aggregates data from FBref and Transfermarkt. Five regression algorithms were evaluated within a unified machine learning framework. Model performance was assessed on a test set using RMSE and R² metrics. Results show that non-linear machine learning models outperform the linear ones. Gradient boosting and neural networks recorded the best predictive performance. Model interpretation techniques reveal playing-time exposure and player age as the main determinants of predicted market value, highlighting the importance of match involvement and career stage in the valuation of football players. Full article

Predictive business process monitoring (PBPM) plays an important role in intelligent workflow management by enabling organizations to anticipate future process behavior and support operational decisions. However, many existing approaches represent execution traces primarily as linear prefixes, thereby limiting their capacity to explicitly capture the control-flow semantics of non-sequential processes. To address this limitation, this paper proposes KG-MTPM, a knowledge-graph-enhanced multi-task framework that integrates process-model-level structural knowledge with prefix-level runtime dynamics in a unified predictive architecture. In particular, control-flow relations are organized as a process knowledge graph so that non-linear execution dependencies can be explicitly represented during prediction. Based on the integrated representation, the model jointly predicts the next-activity, next-activity time, and remaining-time of an ongoing case. Experiments on three real-world event log datasets demonstrate that KG-MTPM achieves the best overall performance among the evaluated baselines, with a marked advantage in time-related prediction tasks. Relative to the best-performing baseline, KG-MTPM improves next-activity prediction accuracy from 0.84 to 0.85, while reducing the mean absolute error (MAE) of next-activity time prediction from 0.81 to 0.25 and that of remaining-time prediction from 0.98 to 0.47. Ablation results confirm the contributions of both the structure-aware representation and the multi-task learning scheme. Overall, the findings suggest that explicit modeling of process structure is beneficial for predictive monitoring in business processes with complex execution behavior. Full article

This systematic review examines how organisational resilience is conceptualised, enacted, and enabled in the Digital Age, characterised by Artificial Intelligence (AI), Generative AI, the Internet of Things (IoT), Big Data, and Robotics. Despite their transformative potential, these technologies are often treated as peripheral tools rather than core mechanisms in resilience architectures. Adopting a critical realist paradigm, we conducted a Systematic Literature Review (SLR) following the PRISMA 2020 protocol to review thirty (30) peer-reviewed empirical studies (2017–present). A pre-SLR conceptual framework, linking Business Intelligence and Responsiveness constructs, guided data extraction and synthesis. Building on this, we propose a conceptual framework and explanatory model grounded in the Context–Mechanism–Outcome logic. The model distinguishes generative mechanisms (real domain), organisational responses (actual domain), and observable indicators (empirical domain). The review identifies Collective Capability (CC), Adaptive Capability (AC) and Dynamic Capability (DC) mechanisms as key generative powers, with Digital Age enablers embedded within Adaptive Capability (AC) and Dynamic Capability (DC). Together, these mechanisms contribute to Systemic Preparedness (SP), Rapid Recovery (RR) and Generative Stability (GS), thereby supporting the emergence of Organisational Resilience (OR). This reconceptualises resilience as an emergent, non-linear outcome of mechanism interactions, offering a unified direction. Future research should prioritise longitudinal multi-case studies and quantitative testing of Context–Mechanism–Outcome configurations, supported by mixed-method designs to validate and refine the proposed framework. Full article

AI/IoT initiatives are increasingly adopted in business, yet reported success varies substantially across firms. This study develops and evaluates a firm-level predictive framework for the reported AI/IoT success rate, measured on a bounded 0–100 scale. Using enterprise survey data from Slovakia and the Czech Republic (n = 1250), we compare a regularized linear baseline (Elastic Net) with nonlinear approaches (Decision Tree and Random Forest) under a consistent out-of-sample evaluation framework, and we examine the best-performing model using permutation importance and PDP/ICE tools. Random Forest achieves the strongest out-of-sample predictive performance and reduces absolute errors relative to Elastic Net for most test observations, although diagnostics also reveal a small tail of extreme errors. Across model families, ai_iot_advantage_share emerges as the most stable predictor of reported AI/IoT success. Nonlinear diagnostics indicate a threshold-like transition in predicted success around the mid-range of advantage attribution and a saturation pattern at higher values. Readiness and performance-related variables are associated with higher predicted success, whereas higher barrier levels are associated with lower predicted success. The results position value realization as the most informative predictive signal in the dataset and provide an interpretable basis for enterprise-level screening and managerial reflection rather than causal inference. Full article

Estimating long-term prices for base metals is central to the financial viability of mining investments, yet prices remain highly volatile and difficult to forecast. This study systematizes the determinants of base metal prices and evaluates their empirical influence using daily and weekly data from the London Metal Exchange (LME) for aluminium, copper, nickel, and zinc between April 2005 and May 2017. In this context, the study aims to identify and evaluate the key economic, financial, and physical drivers of base metal prices, with particular emphasis on distinguishing between short-run predictive factors and long-run equilibrium determinants. After aligning metal prices with candidate explanatory variables, linear associations are quantified through Pearson correlations and alternative functional forms are explored for price modelling, including linear, log-linear, and selected nonlinear transformations. The methodology is complemented with econometric diagnostics. Explanatory variables are grouped into four categories: (i) supply–demand metrics (inventories, production–consumption balances, sales aggregates, and LME position data), (ii) business cycle and income proxies (global GDP growth, China Caixin PMI, the U.S. S&P 500 index, and China steel rebar futures), (iii) investment variables (cross-metal prices and Brent crude), and (iv) monetary indicators (U.S. and the U.S. 10-year yield). Results show that short-run price movements are mainly driven by business cycle indicators and inventory dynamics, while long-run trends reflect structural supply conditions. Monetary variables generate temporary price impulses, and prices tend to lead speculative positioning rather than the reverse. Full article

Urban plastic waste management in large metropolitan regions remains constrained by low recovery rates despite growing policy attention. This study adopts a sustainability-oriented innovation (SOI) perspective to examine how policy–technology integration reshapes system-level performance in urban plastic waste systems. Using Bangkok as a representative case, a system-level model integrates plastic waste generation growth, time-dependent behavioural adoption of separation at source, contamination-sensitive sorting efficiency, and mass-balance material flows. Three scenarios are assessed: Business-as-Usual, separation-at-source policy only, and an integrated policy–technology system with advanced sorting. Results show that the baseline system remains stagnant at approximately 3.1% recovery. Policy intervention alone increases recovery gradually, reaching around 20% by 2045 despite participation approaching an 85% ceiling. In contrast, integrating policy with advanced sorting generates non-linear gains, surpassing 20% recovery within two years and reaching approximately 47% by 2045, driven by substantial contamination reduction. A Monte Carlo sensitivity analysis extends the integrated pathway to 2060. The median recovery trajectory stabilises at 68%, while the probability of achieving more than 70% recovery rises to 28% by 2040 and plateaus at 33% thereafter. The findings demonstrate that circular economy performance is probabilistic and depends on system-level alignment between behavioural participation, material quality, and technological capability. Full article

This study, which uses the 2025 Global Innovation Index dataset, examines the structural architecture of global innovation systems, using Knowledge and Technology Outputs (KTO) as the dependent variable to measure the level of innovation. It aims to identify the latent dimensions of the global innovation system, the heterogeneity of global innovation regimes, and the determinants of global innovation outputs. The study adopts a holistic approach, including PCA, K-means clustering, machine learning algorithms such as MLP, and explainable artificial intelligence methods such as SHAP and LIME. The study finds that the global innovation system is highly concentrated, with 80.22% of the total variance explained by a single component. In addition, the study identifies five sharply differentiated global innovation regimes with near-perfect separability, achieving up to 100% accuracy. The nonlinear MLP model demonstrates strong predictive performance (R² = 0.8836), with Business Sophistication as the main factor affecting KTO, followed by Infrastructure and Human Capital. Explainability analysis shows high consistency between SHAP and LIME $(\rho =0.999)$ and a highly centralized interaction structure, in which a few dimensions have a strong impact on innovation performance. The structural paradigm of the global innovation system is hybrid, with a linear backbone and nonlinear interactions coexisting. This study has contributed to methodological development in the field of innovation research and has provided insights into the development of more precise and effective innovation policy. Full article

Digital transformation has become a central strategic priority as organizations increasingly rely on digital technologies to redesign business processes, governance structures, and value creation mechanisms in digitally evolving environments. However, existing approaches to digital transformation readiness often rely on additive maturity models or capability inventories that assume transformation capacity increases through cumulative capability development. Such approaches overlook how strategic emphasis must be distributed across transformation domains under governance and resource constraints. This study addresses this limitation by conceptualizing digital transformation readiness as a problem of strategic prioritization rather than cumulative capability accumulation. To operationalize this perspective, the study develops the Strategic Focus Index (SFI), a governance-aligned diagnostic instrument that evaluates how organizations distribute strategic attention across interdependent transformation domains. The index is constructed through a two-round Delphi study involving 53 experts from industry, academia, and the public sector, followed by statistical validation and an illustrative diagnostic application. The findings demonstrate how domain-level prioritization patterns can be systematically interpreted to identify potential imbalances in transformation efforts. By reframing readiness assessment as a prioritization-based diagnostic rather than a linear maturity measure, this study contributes a structured approach for evaluating digital transformation in digital business and platform-based environments. Full article

This article examines whether a high-performing fraud detection model can also meet the demands of auditability, documentation, and regulatory transparency. Using the publicly available European credit card fraud dataset of 284,807 transactions, including 492 fraudulent cases, this study compares weighted logistic regression with XGBoost under severe class imbalance. Model performance is assessed through precision, recall, F1 score, ROC AUC, and precision–recall AUC, with particular attention to alert burden and fraud capture. Results show that XGBoost materially outperforms logistic regression in operational terms. While logistic regression achieves slightly higher recall, XGBoost raises precision from 0.061 to 0.562, improves PR AUC from 0.719 to 0.863, and reduces false positives from 1386 to 67. The PR AUC of 0.863 refers to the cross-validated average reported in the model comparison, while the holdout test result reported later in this paper is 0.852. It cuts the review queue from 1476 alerts to 153 while still identifying 86 of 98 fraud cases in the test set. Explainability is then introduced through SHAP, which provides both global feature attribution and transaction-level reasoning. The findings show that SHAP makes the boosted model readable at the level of both overall model behaviour and individual fraud flags, thereby supporting audit review, model validation, and regulatory scrutiny. The article argues that the combination of XGBoost and SHAP offers a stronger fit for auditing than either a weaker but transparent linear model or a stronger opaque classifier. One limit remains, since the dataset contains anonymised principal components rather than original business variables, which restricts semantic interpretation. Even so, the workflow provides a practical bridge between predictive fraud analytics and the demands of explainable, reviewable, and accountable AI in auditing. Full article

This study forecasts inbound tourism demand for the United Kingdom, using monthly data from February 1989 to February 2020. In the empirical analysis, we evaluate and compare the performance of five machine learning models (decision trees, random forests, XGBoost, and support vector regression with the RBF and linear kernels) against a more traditional linear SARIMA regression model. Forecasting performance metrics included MSE, RMSE, MAE, R², and MAPE. The SVR RBF kernel model achieves the highest accuracy, with an MAPE of 0.014% on the training set. To enhance model interpretability, feature importance analysis is applied to identify the most influential predictors of tourist arrivals. This research offers significant policy implications, aiding government policymakers and private industry stakeholders in optimizing their planning and decisions, deploying better long-term business strategies and tourism-related services, and optimizing the allocation of public and private resources to support the tourism sector. Full article

Corporate sustainability has emerged as a critical strategic imperative for organizations seeking to mitigate their environmental impacts amid escalating climate pressures and growing stakeholder demands. This study examines corporate strategies aimed at reducing environmental footprints—including circular economy models, energy efficiency measures, and digitalization—and investigates how young adults perceive and evaluate corporate sustainability commitments, with particular emphasis on greenwashing skepticism. A cross-sectional quantitative survey was administered to 150 university students and young professionals aged 18–25 years in Spain. Data were analyzed using descriptive statistics, analysis of variance (ANOVA), and linear regression to examine the influence of prior sustainability knowledge, academic background, age, and sectoral context on perceived corporate sustainability commitment, greenwashing perception, and willingness to consume sustainable products. The findings reveal that prior sustainability knowledge significantly and positively predicts higher evaluations of corporate environmental commitment, while age and academic background—particularly among students in Economics and Business—are associated with heightened greenwashing skepticism. Perceived corporate sustainability commitment is found to exert a significant positive influence on sustainable consumption intention, and production-intensive sectors are consistently perceived as more environmentally harmful than service-oriented industries. These findings underscore the importance of transparent, credible, and verifiable sustainability strategies in building legitimacy and trust among younger generations, and contribute to the growing literature on stakeholder perceptions of corporate environmental responsibility. Full article

Across the globe, companies are facing significant pressure to reduce waste, improve resource efficiency, and report their sustainability efforts transparently. ESG frameworks have become essential tools for sustainability transformation. However, traditional business models, based on a linear “take–make–dispose” approach, continue to dominate industries, limiting the impact of ESG efforts. The circular economy offers a compelling alternative: it encourages designing products for reuse, recycling, and regeneration, thus aligning closely with ESG principles. When businesses transition to circular models, they reduce their environmental footprint, create new green jobs and social inclusion opportunities, and strengthen accountability across business value chains. This study explores how selected firms in the mining, energy, consumer cyclical, technology, and healthcare sectors are aligning circular principles with ESG practices. Using a longitudinal, multi-sector comparative analysis of ESG indicators spanning 2014–2024, the research examines sector-level ESG evolution, firm-level ESG leadership, and the alignment of ESG performance with circular business model pathways. Rather than directly measuring circular transformation, ESG indicators are interpreted as signals of emerging circular business model pathways. This study identifies ESG-based ways and enabling conditions through which circularity may be increasingly embedded across different sectors. Full article