Search Results (594)

Public-health campaigns have to capture and hold visual attention, but little is known about the influence of message framing and visual appeal on attention to bowel-cancer screening ad campaigns. In a within-subjects test, 42 UK adults aged 40 to 65 viewed 54 static adverts that varied by (i) slogan frame—anticipated regret (AR) vs. positive (P); (ii) image type—hand-drawn, older stock, AI-generated; and (iii) identity congruence—viewer ethnicity matched vs. unmatched to the depicted models. Remote eye-tracking measured time to first fixation (TTFF), dwell, fixations, and revisits on a priori pre-defined regions of interest (ROIs); analyses employed linear mixed-effects models (LMMs), generalized estimating equations (GEEs), and median quantile regressions with cluster at the participant level. Across models, the AR slogans produced faster orienting (smaller TTFF) and more intense maintained attention (longer dwell, more fixations and revisits) than the P slogans. Image type set baseline attention (hand-drawn > old stock > AI) but did not significantly decrease the AR benefit, which was equivalent for all visual styles. Identity congruence enhanced early capture (lower TTFF), with small effects for dwell-based measures, suggesting that tailoring benefits only the “first glance.” Anticipated-regret framing is a reliable, design-level alternative to improving both initial capture and sustained processing of screening messages. In practice, the results indicate that advertisers should pair regret-based slogans with warm, human-centred imagery; place slogans in high-salience, low-competition spaces, and, when incorporating AI-generated imagery, reduce composition complexity and exclude uncanny details. These findings ground regret framing as a visual-attention mechanism for public-health campaigns in empirical fact and provide practical recommendations for testing and production. Full article

Monocular 3D object detection (Mono3D) is essential for autonomous driving and augmented reality, yet its performance degrades significantly at night due to the scarcity of annotated nighttime data. In this paper, we investigate the use of style transfer for nighttime data augmentation and evaluate its effect on individual components of 3D detection. Using CycleGAN, we generated synthetic night images from daytime scenes in the nuScenes dataset and trained a modular Mono3D detector under different configurations. Our results show that training solely on style-transferred images improves certain metrics, such as AP@0.95 (from 0.0299 to 0.0778, a 160% increase) and depth error (11% reduction), compared to daytime-only baselines. However, performance on orientation and dimension estimation deteriorates. When real nighttime data is included, style transfer provides complementary benefits: for cars, depth error decreases from 0.0414 to 0.021, and AP@0.95 remains stable at 0.66; for pedestrians, AP@0.95 improves by 13% (0.297 to 0.336) with a 35% reduction in depth error. Cyclist detection remains unreliable due to limited samples. We conclude that style transfer cannot replace authentic nighttime data, but when combined with it, it reduces false positives and improves depth estimation, leading to more robust detection under low-light conditions. This study highlights both the potential and the limitations of style transfer for augmenting Mono3D training, and it points to future research on more advanced generative models and broader object categories. Full article

Tourism performance often depends on the joint provision of built (“hard”) and environmental (“green”) infrastructure, yet their combined effects are not well established. Using official data for Saudi Arabia’s 13 regions (2023–2024), this study constructs composite hard and green indices, estimates ordinary least squares models with heteroskedasticity-consistent inference, and probes spatial heterogeneity using geographically weighted regression (exploratory) alongside k-means/hierarchical clustering. Hard infrastructure is the strongest and most consistent correlate of overnight visitors and spending, whereas green infrastructure exhibits non-positive marginal effects over the observed range of hard capacity; a negative, statistically significant Hard × Green interaction indicates diminishing returns to greening as built capacity increases. Clustering differentiates metropolitan hubs from nature-oriented regions, underscoring place-specific policy needs. Practically, results support sequencing prioritizing foundational access and basic accommodation in under-served regions, quality upgrades and public-realm enhancement in mature centers, and targeted green interventions where marginal gains are greatest. Key limitations (cross-sectional design; coarse green metrics) motivate richer environmental indicators and longitudinal data to clarify dynamics and thresholds over time. Full article

This paper estimates a nonlinear asymmetric dynamics model in the threshold panel data framework to study the extent to which the quality of governance, investment, and financial stability affect the impact of external debt on economic growth in 47 African countries from 2002 to 2022. As a general approach, we use the first-differenced GMM estimator, which allows both threshold variables and regressors to be endogenous. The results confirm that external debt becomes a drag on growth beyond a threshold of 53.49% relative to GDP. Furthermore, the results show that external debt appears to stimulate economic growth mainly by orienting it towards productive investment. In addition, the results show that better governance quality and financial stability accentuate the positive impact of external debt on economic growth. Based on the findings, this study proposes several policy recommendations. Full article

Student engagement analysis is closely linked with learning outcomes, and its precise identification paves the way for targeted instruction and personalized learning. Current student engagement methods, reliant on either head pose estimation with facial landmarks or eye-trackers, are hardly generalized to authentic classroom teaching environments with high occlusion and non-intrusive requirements. Based on empirical observations that student body orientation and head pose exhibit a high degree of consistency in classroom settings, we propose a novel student engagement analysis algorithm incorporating human body orientation estimation. To better suit classroom settings, we develop a one-stage and end-to-end trainable framework for multi-person body orientation estimation, named JointBDOE. The proposed JointBDOE integrates human bounding box prediction and body orientation into a unified embedding space, enabling the simultaneous and precise estimation of human positions and orientations in multi-person scenarios. Extensive experimental results using the MEBOW dataset demonstrate the superior performance of JointBDOE over the state-of-the-art methods, with an MAE reduced to 10.63° and orientation accuracy exceeding 91% at 22.5°. With the more challenging reconstructed MEBOW dataset, JointBDOE maintains strong robustness with an MAE of 16.07° and an orientation accuracy of 88.3% at 30°. Further analysis of classroom teaching videos validates the reliability and practical value of body orientation as a robust metric for engagement assessment. This research showcases the potential of artificial intelligence in intelligent classroom analysis and provides an extensible solution for body orientation estimation technology in related fields, advancing the practical application of intelligent educational tools. Full article

Traditional cooperative navigation algorithms for multiple AUVs are typically designed for a single specific configuration, such as parallel or leader-slave. This paper proposes a novel cooperative navigation algorithm based on factor graph and Lie group to address the multi-AUV localization problem, which is applicable to various multi-AUV configurations. First, the motion state of an AUV is represented within the two-dimensional special Euclidean group (SE(2)) space from Lie theory. Second, the motion of the AUV and acoustic-based range and bearing measurements are modeled to derive the motion error function and the range and bearing error function, respectively. Depending on the formulation of the motion error function, the proposed approach comprises two methods: Method 1 and Method 2. Third, the Gauss-Newton method is employed for nonlinear optimization to obtain the optimal estimates of the motion states for all AUVs. Finally, a parameter-level simulation system for AUV cooperative navigation is established to evaluate the algorithm’s performance under two different multi-AUV configurations. Method 1 is designed for parallel configurations, reducing the average RMSE of position and orientation errors by 29% compared to the EKF. Method 2 is tailored for leader-slave configurations, reducing the average RMSE of position and orientation errors by 38% compared to the EKF. Simulation results demonstrate that the proposed algorithm achieves superior performance across different AUV configurations compared to conventional EKF-based approaches. Full article

This work presents a task-oriented framework for optimizing robotic surface finishing to improve efficiency and ensure feasibility under realistic kinematic and geometric constraints. The approach combines surface subdivision, optimal placement of the workpiece, and region-based toolpath planning to adapt machining strategies to local surface characteristics. A novel time evaluation criterion is introduced that improves our previous kinematic approach by incorporating dynamic aspects. This advancement enables a more realistic estimation of machining time, providing a more reliable basis for optimization and path planning. The framework determines both the optimal position of the workpiece and the subdivision of its surface into regions systematically, enabling machining directions and speeds to be adapted to the geometry of each region. The methodology was validated on several semi-complex surfaces through simulation and experimental trials with collaborative robotic manipulators. The results demonstrate that improved region-based optimization leads to machining time reductions of 9–26% compared to conventional single-direction machining strategies. The most significant improvements were achieved for larger, more complex geometries and denser machining paths, confirming the method’s industrial relevance. These findings establish the framework as a practical solution for reducing cycle time in specific robotic surface finishing tasks. Full article

Smart contracts empower many blockchain applications but are exposed to code-level defects. Existing methods do not scale to the evolving code, do not represent complex control and data flows, and lack granular and calibrated evidence. To address the above concerns, we present an across-graph corresponding contract-graph method for vulnerability detection: abstract syntax, control flow, and data flow are fused into a typed, directed contract-graph whose nodes are enriched with pre-code embeddings (GraphCodeBERT or CodeT5+). A Graph Matching Network (GMN) with cross-graph attention compares contract-graphs, aligns homologous sub-graphs associated with vulnerabilities, and supports the interpretation of statements at the level of balance between a broad structural coverage and a discriminative pairwise alignment. The evaluation follows a deployment-oriented protocol with thresholds fixed for validation, multi-seed averaging, and a conservative estimate of sensitivity under low-false-positive budgets. On SmartBugs Wild, the method consistently and markedly exceeds strong rule-based and learning baselines and maintains a higher sensitivity to matching false-positive rates; ablations track the gains to multi-graph fusion, pre-trained encoders, and cross-graph matching, stable through seeds. Full article

Visible Light Positioning (VLP) has emerged as a pivotal technology for industrial Internet of Things (IoT) and smart logistics, offering high accuracy, immunity to electromagnetic interference, and cost-effectiveness. However, fluctuations in signal gain caused by target motion significantly degrade the positioning accuracy of current VLP systems. Conventional approaches face intrinsic limitations: propagation-model-based techniques rely on static assumptions, fingerprint-based approaches are highly sensitive to dynamic parameter variations, and although CNN/LSTM-based models achieve high accuracy under static conditions, their inability to capture long-term temporal dependencies leads to unstable performance in dynamic scenarios. To overcome these challenges, we propose a novel dynamic VLP algorithm that incorporates a Spatio-Temporal Feature Information Network (STFI-Net) for joint localization and orientation estimation of moving targets. The proposed method integrates a two-layer convolutional block for spatial feature extraction and employs modern Temporal Convolutional Networks (TCNs) with dilated convolutions to capture multi-scale temporal dependencies in dynamic environments. Experimental results demonstrate that the STFI-Net-based system enhances positioning accuracy by over 26% compared to state-of-the-art methods while maintaining robustness in the face of complex motion patterns and environmental variations. This work introduces a novel framework for deep learning-enabled dynamic VLP systems, providing more efficient, accurate, and scalable solutions for indoor positioning. Full article

Background/Objectives: The glymphatic system is a recently characterized glial-dependent waste clearance pathway in the brain, which makes use of perivascular spaces for cerebrospinal fluid exchange. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) offers a non-invasive method for estimating perivascular flow, but its biological specificity and susceptibility to methodological variation, particularly head position during MRI acquisition, remain as threats to the validity of this technique. This study aimed to assess the prevalence of current DTI-ALPS practices, evaluate the impact of head orientation on ALPS index calculation, and propose a novel computational approach to improve measurement validity. Methods: We briefly reviewed DTI-ALPS literature to determine the use of head-orientation correction strategies. We then analyzed diffusion MRI data from 172 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to quantify the influence of head orientation on ALPS indices computed using the conventional Unrotated-ALPS, a vecrec-corrected ALPS, and the new LD-ALPS method proposed within. Results: A majority of studies employed Unrotated-ALPS, which does not correct for head orientation. In our sample, Unrotated-ALPS values were significantly associated with absolute head pitch (r₁₆₉ = −0.513, p < 0.001), indicating systematic bias. This relationship was eliminated using either vecreg or LD-ALPS. Additionally, LD-ALPS showed more sensitivity to cognitive status as measured by Mini-Mental State Examination scores. Conclusions: Correcting for head orientation is essential in DTI-ALPS studies. The LD-ALPS method, while computationally more demanding, improves the reliability and sensitivity of perivascular fluid estimates, supporting its use in future research on aging and neurodegeneration. Full article

This study addresses the growing importance of integrating innovation into corporate sustainability strategies by examining the financial and environmental performance of ten firms listed on the Borsa Istanbul Sustainability Index over a five-year period. The main objective is to develop and test a novel, data-driven analytical framework that reduces reliance on subjective expert judgments while providing actionable insights for sustainability-oriented decision-making. Within this framework, the entropy method from the Multi-Criteria Decision Making (MCDM) approach is first applied to calculate the objective weights of sustainability criteria, ensuring that the analysis is grounded in real performance data. Building on these weights, an innovative reverse Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, implemented through a custom artificial neural network-based software, is introduced to estimate direct influence matrices and reveal the causal relationships among criteria. This methodological advance makes it possible to explore how environmental and financial factors interact with R&D expenditures and to simulate their systemic interdependencies. The findings demonstrate that R&D serves as a central driver of both environmental and financial sustainability, highlighting its dual role in fostering corporate innovation and long-term resilience. By positioning R&D as both an enabler and outcome of sustainability dynamics, the proposed framework contributes a novel tool for aligning innovation with strategic sustainability goals, offering broader implications for corporate managers, policymakers, and researchers. Full article

Difference in Differences (DiD) is a useful statistical technique employed by researchers to estimate the effects of exogenous events on the outcome of some response variables in random samples of treated units (i.e., units exposed to the event) ideally drawn from an infinite population. The term “effect” should be understood as the discrepancy between the post-event realisation of the response and the hypothetical realisation of that same outcome for the same treated units in the absence of the event. This theoretical discrepancy is clearly unobservable. To circumvent the implicit missing variable problem, DiD methods utilise the realisations of the response variable observed in comparable random samples of untreated units. The latter are samples of units drawn from the same population, but they are not exposed to the event under investigation. They function as the control or comparison group and serve as proxies for the non-existent untreated realisations of the responses in treated units during post-treatment periods. In summary, the DiD model posits that, in the absence of intervention and under specific conditions, treated units would exhibit behaviours that are indistinguishable from those of control or untreated units during the post-treatment periods. For the purpose of estimation, the method employs a combination of before–after and treatment–control group comparisons. The event that affects the response variables is referred to as “treatment.” However, it could also be referred to as “causal factor” to emphasise that, in the DiD approach, the objective is not to estimate a mere statistical association among variables. This review introduces the DiD techniques for researchers in economics, public policy, health research, management, environmental analysis, and other fields. It commences with the rudimentary methods employed to estimate the so-called Average Treatment Effect upon Treated (ATET) in a two-period and two-group case and subsequently addresses numerous issues that arise in a multi-unit and multi-period context. A particular focus is placed on the statistical assumptions necessary for a precise delineation of the identification process of the cause–effect relationship in the multi-period case. These assumptions include the parallel trend hypothesis, the no-anticipation assumption, and the SUTVA assumption. In the multi-period case, both the homogeneous and heterogeneous scenarios are taken into consideration. The homogeneous scenario refers to the situation in which the treated units are initially treated in the same periods. In contrast, the heterogeneous scenario involves the treatment of treated units in different periods. A portion of the presentation will be allocated to the developments associated with the DiD techniques that can be employed in the context of data clustering or spatio-temporal dependence. The present review includes a concise exposition of some policy-oriented papers that incorporate applications of DiD. The areas of focus encompass income taxation, migration, regulation, and environmental management. Full article

Bundle adjustment without Ground Control Points (GCPs) using stereo remote sensing images represents a reliable and efficient approach for realizing the demand for regional and global mapping. This paper proposes a bundled-images based geo-positioning method that leverages a Kalman filter to effectively integrate new image observations with their corresponding historical bundled images. Under the assumption that the noise follows a Gaussian distribution, a linear mean square estimator is employed to orient the new images. The historical bundled images can be updated with posterior covariance information to maintain consistent accuracy with the newly oriented images. This method employs recursive computation to dynamically orient the new images, ensuring consistent accuracy across all the historical and new images. To validate the proposed method, extensive experiments were carried out using two satellite datasets comprising both homologous (IKONOS) and heterogeneous (TH-1 and ZY-3) sources. The experiment results reveal that without using GCPs, the proposed method can meet 1:50,000 mapping standards with heterogeneous TH-1 and ZY-3 datasets and 1:10,000 mapping accuracy requirements with homologous IKONOS datasets. These experiments indicate that as the bundled images expand further, the image quantity growth no longer results in substantial improvements in precision, suggesting the presence of an accuracy ceiling. The final positioning accuracy is predominantly influenced by the initial bundled image quality. Experimental evidence suggests that when using the proposed method, the bundled image sets should exhibit superior precision compared to subsequently new images. In future research, we will expand the coverage to regional or global scales. Full article

Artificial intelligence is transforming industrial marketing by reshaping processes, decision-making, and inter-firm relationships. However, research remains fragmented, with limited evidence on how adoption drivers create new capabilities and sustainability outcomes. This study develops and empirically validates an integrated framework that combines technology, organization, environment, user acceptance, resource-based perspectives, dynamic capabilities, and explainability. A convergent mixed-methods design was applied, combining survey data from industrial firms with thematic analysis of practitioner insights. The findings show that technological readiness, organizational commitment, environmental pressures, and user perceptions jointly determine adoption breadth and depth, which in turn foster marketing capabilities linked to measurable improvements. These include shorter quotation cycles, reduced energy consumption, improved forecasting accuracy, and the introduction of carbon-based pricing mechanisms. Qualitative evidence further indicates that explainability and human–machine collaboration are decisive for trust and practical use, while sustainability-oriented investments act as catalysts for long-term transformation. The study provides the first empirical integration of adoption drivers, capability building, and sustainability outcomes in industrial marketing. By demonstrating that artificial intelligence advances competitiveness and sustainability simultaneously, it positions marketing as a strategic lever in the transition toward digitally enabled and environmentally responsible industrial economies. We also provide a simplified mapping of theoretical lenses, detail B2B-specific scale adaptations, and discuss environmental trade-offs of AI use. Given the convenience/snowball design, estimates should be read as upper-bound effects for mixed-maturity populations; robustness checks (stratification and simple reweighting) confirm sign and significance. Full article

Precision component displacement, processing, and manipulation in an industrial environment require the high-precision positioning and orientation of industrial robots. However, industrial robots’ positioning includes uncertainties due to assembly and manufacturing tolerances. It is therefore required to use calibration techniques for industrial robot parameters. One of the major sources of uncertainty is the one associated with industrial robot geometrical parameter values. In this paper, using multi-objective meta-heuristic optimization approaches and optical metrology measurements, more accurate Denavit–Hartenberg (DH) geometrical parameters of an industrial robot are estimated. The sensor data used to perform this calibration are the absolute 3D position readings using a highly accurate laser tracker (LT) and industrial robot joint angle readings. Other than position accuracy, the mean absolute deviation of the DH parameters from the manufacturer’s given parameters is considered as the second objective function. Therefore, the optimization problem investigated in this paper is a multi-objective one. The solution to the multi-objective optimization problem is obtained using different evolutionary and swarm optimization approaches. The evolutionary optimization approaches are nondominated sorting genetic algorithms and a multi-objective evolutionary algorithm based on decomposition. The swarm optimization approach considered in this paper is multi-objective particle swarm optimization. It is observed that NSGAII outperforms the other two optimization algorithms in terms of a more diverse Pareto front and the function corresponding to the positional accuracy. It is further observed that through using NSGAII for calibration purposes, the root mean squared for positional error has been improved significantly compared with nominal values. Full article