Artificial Intelligence is reshaping global socio-economic structures, yet existing studies often analyze its economic, social, and market impacts in isolation, lacking a unified framework that handles multi-dimensional interactions. This paper constructs an end-to-end machine learning pipeline - from raw heterogeneous data input, through three cascaded modules (micro: Random Forest + SHAP; meso: XGBoost; macro: K-means++ clustering), to actionable policy outputs - to quantify the complex socio-economic effects of AI adoption across countries and industries. Using a comprehensive global dataset (2020-2025), this research finds that: (1) Human-AI Collaboration Rate (HAICR) is the dominant mitigator of AIdriven job loss, outweighing AI Adoption Rate (AIAD), while high AIAD significantly exacerbates job displacement in automated sectors like manufacturing and retail; (2) Strict regulation is the strongest driver of consumer trust, but its efficacy is moderated by cultural contexts and tool transparency; (3) China and the U.S. lead AI-driven markets, whereas India and South Korea require infrastructure and policy upgrades. By coupling machine learning methods in a tripartite analytical pipeline, this study moves beyond isolated econometric models and provides quantifiable, actionable insights for balancing AI innovation with social welfare.
Visual Question Answering (VQA) aims to achieve cross-modal semantic understanding through joint modeling of visual content and natural language. Although existing attention-based approaches effectively align features, they struggle to simultaneously accommodate global semantic modeling and local fine-grained perception within single-path architectures, leading to semantic drift and underutilization of information when addressing multi-perspective reasoning tasks. To mitigate this limitation, we propose a Decoupled Global-Local Collaborative Network (DGL-Net) that structurally partitions the cross-modal reasoning process into two complementary branches: global semantic understanding and local fine-grained modeling. Specifically, the global branch employs hierarchical attention fusion to model long-range dependencies and holistic scene semantics, providing contextual constraints for reasoning. The local branch introduces semantic relation enhancement and relevant region screening to explicitly model intra-modal structural relationships, strengthening fine-grained region-word alignment by injecting structural information. During training and inference, a branch-decoupling and collaborative fusion strategy enables independent yet complementary feature learning. Extensive experimental results demonstrate accuracies of 72.15% and 57.87% on VQA-v2 and GQA benchmarks, respectively. Notably, the proposed method exhibits enhanced robustness and generalization in complex semantic and compositional reasoning tasks, validating the efficacy of the decoupled collaborative strategy.
Data validation in ETL workflows is most often performed after transformation, even though many data quality issues originate earlier in the pipeline. In practice, this means that errors are detected late, when tracing their source and applying corrections already requires additional effort. In this work, we explore whether virtual reality (VR) could be used as an alternative interface for addressing this problem earlier, at the stage of raw data inspection. We present VR-ETL, a proof-of-concept system that allows users to inspect and correct tabular data before transformation by interacting with it inside an immersive 3D environment. The system imports CSV datasets and applies a set of rule-based validation checks targeting common data quality problems, such as missing values, format inconsistencies, and basic referential integrity violations. Detected issues are visualized directly within the 3D representation of the data, using color-coded cells and simple spatial summaries that reflect error distribution across columns and records. A functional prototype was implemented using Unity and OpenXR and tested on structured datasets containing up to 10,000 records. During controlled technical testing, the system supported automated validation, real-time visual feedback, and immediate re-validation after user corrections, while maintaining performance suitable for immersive use on consumer-grade hardware. All intentionally seeded rule-based errors were detected. Although usability and workflow efficiency were not evaluated in this study, the prototype demonstrates that pre-transformation data validation in VR is technically feasible and provides a concrete starting point for future empirical investigations.
Cloud computing has become a dominant paradigm for delivering scalable and flexible on-demand resources; however, efficiently executing high performance computing (HPC) workloads remains challenging, particularly in heterogeneous environments. Conventional static scheduling methods often lead to poor resource utilization and increased makespan, while dynamic approaches improve load distribution but introduce significant overhead due to continuous monitoring and real-time decision-making. To address these challenges, this paper proposes an SLA-aware Dynamic Enhanced Resource-Aware Load Balancing Algorithm (SLADE- RALBA). The algorithm minimizes load imbalance by considering the computational capacities of virtual machines and ensures Service Level Agreement (SLA) compliance through a three-tier priority-based workflow. The proposed approach is evaluated using CloudSim Plus on two benchmark datasets: Heterogeneous Computing Scheduling Problem (HCSP) instances and the Google Cloud Jobs dataset. Results demonstrate that SLA-DE-RALBA consistently outperforms baseline algorithms, including RALBA, DRALBA, DE-RALBA, SLA-RALBA, Dynamic Max- Min, PSSLB, and PSSELB, across key metrics such as makespan, resource utilization, job rejection, throughput, execution time, and cost. Notably, it achieves zero job rejection, reduces energy consumption by up to 85%, improves resource utilization by 11.9%, lowers makespan by 41-45%, and decreases execution time by up to 57%, making it a robust and efficient solution for HPC workload scheduling in cloud environments.
Energy-aware path planning for industrial autonomous vehicles requires more than a geometric shortest path: terrain, payload, drivetrain efficiency, turning, and a hard task-level energy budget jointly affect feasibility. We present Neuro-PathNet-EC, a physics-aware constrained planner with learned multimodal guidance. The planner operates on heading-aware grid states and evaluates directed edge energy from friction and elevation. Exact Pareto labels enforce the hard energy budget, while a goal-conditioned ResNet-18 network fuses four raster channels with vehicle semantics and predicts an eight-channel dense cost-to-go map. The learned heuristic is used only for guidance; it is not assumed admissible or consistent. Exact Energy-A* obtains its guarantee from an analytic admissible anchor, and a FOCAL extension provides the bound C ≤ wC*. On a layout-disjoint synthetic industrial dataset, five training seeds achieve a test relative heuristic MAE of 0.0289 ± 0.0014 and Spearman correlation of 0.9963 ± 0.0004. Across 512 paired binding-budget queries per seed (2,560 seed-query runs), full multimodal neural-constrained search returns the exact constrained objective in every run while reducing expanded labels by 10.73% relative to Energy-A*. The semantic branch also reduces labels relative to an otherwise matched image-only model. FOCAL satisfies its declared bound in all evaluated cases, although its current correctness-first OPEN management does not improve runtime. The evaluation is based on synthetic layouts and does not constitute public-benchmark or real-vehicle validation.
