The Internet of Vehicles (IoV) operates in an environment characterized by asymmetric security threats, where centralized vulnerabilities create a critical imbalance that can be disproportionately exploited by attackers. This study addresses this imbalance by proposing a symmetrical security framework that integrates Blockchain and Federated Learning (FL) to restore equilibrium in the Vehicle–Road–Cloud ecosystem. The evolution toward sixth-generation (6G) technologies amplifies both the potential of vehicle-to-everything (V2X) communications and its inherent security risks. The proposed framework achieves a delicate balance between robust security and operational efficiency. By leveraging blockchain’s symmetrical and decentralized distribution of trust, the framework ensures data and model integrity. Concurrently, the privacy-preserving approach of FL balances the need for collaborative intelligence with the imperative of safeguarding sensitive vehicle data. A novel Cloud Proxy Re-Encryption Offloading (CPRE-IoV) algorithm is introduced to facilitate efficient model updates. The architecture employs a partitioned blockchain and a smart contract-driven FL pipeline to symmetrically neutralize threats from malicious nodes. Finally, extensive simulations validate the framework’s effectiveness in establishing a resilient and symmetrically secure foundation for next-generation IoV networks.

Autonomous driving technology has made significant advancements in recent years, yet challenges remain in ensuring safe and comfortable interactions with human-driven vehicles (HDVs), particularly during lane-changing maneuvers. This paper proposes an improved double quintic polynomial approach for safe and efficient lane-changing in mixed traffic environments. The proposed method integrates a time-to-collision (TTC) based evaluation mechanism directly into the trajectory optimization process, ensuring that the ego vehicle proactively maintains a safe gap from surrounding HDVs throughout the maneuver. The framework comprises state estimation for both the autonomous vehicle (AV) and HDVs, trajectory generation using double quintic polynomials, real-time TTC computation, and adaptive trajectory evaluation. To the best of our knowledge, this is the first work to embed an analytic TTC penalty directly into the closed-form double-quintic polynomial solver, enabling real-time safety-aware trajectory generation without post-hoc validation. Extensive simulations conducted under diverse traffic scenarios demonstrate the safety, efficiency, and comfort of the proposed approach compared to conventional methods such as quintic polynomials, Bezier curves, and B-splines. The results highlight that the improved method not only avoids collisions but also ensures smooth transitions and adaptive decision-making in dynamic environments. This work bridges the gap between model-based and adaptive trajectory planning approaches, offering a stable solution for real-world autonomous driving applications.

As renewable energy penetration in power grids increases, inherent intermittency and volatility pose severe stability challenges. Traditional assessment methods face computational complexity bottlenecks, while machine learning (ML) models, despite strong predictive performance, suffer from “black-box” opacity limiting adoption in safety-critical systems. This study proposes a comprehensive ML framework integrating LIME interpretability and physical mechanism validation for renewable energy grid stability assessment, using a publicly available synthetic benchmark dataset (2000 samples, 15 features) adhering to IEC/IEEE standards. We systematically compare ten classification models spanning traditional ML (logistic regression, K-nearest neighbors, support vector machine, random forest, gradient boosting) and deep learning (MLP, 1D CNN, LSTM, GRU, tabular ResNet). Results demonstrate traditional methods’ competitive performance on this small-scale tabular dataset—gradient boosting achieves 84.5% accuracy, ROC AUC 0.904, MCC 0.626, outperforming deep architectures constrained by overfitting on 1600 training samples. We emphasize that these findings are specific to the limited dataset scale (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} $$n=1600$$); deep learning may demonstrate superior performance with substantially larger training sets (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} $$n > 10,000$$). We develop a four-level LIME interpretability framework: (i) single-sample local interpretation, (ii) global feature importance aggregation identifying FreqDeviation and HarmonicTHD as most critical factors (contributions > 10), (iii) contribution directionality analysis, and (iv) multi-sample consistency verification (CV < 15%). Rationale for CV < 15%: This threshold balances coverage (88% of samples) with reliability, filtering unstable interpretations while retaining robust feature rankings, validated through empirical testing and expert assessment. LIME-identified features align with power system theory—frequency deviation reflects active power imbalance, harmonic distortion affects damping. However, we identify spurious correlations (Sample 127: low harmonics paradoxically promoting instability), underscoring expert validation necessity before deployment. Exploratory LLM integration (GPT-4) demonstrates feasibility of converting LIME outputs into actionable natural language recommendations, achieving expert-rated technical accuracy 4.2 ± 0.6/5. We explicitly acknowledge critical limitations: (i) synthetic data may not capture all real-world complexities (rare failure modes, cascading failures, stochastic weather patterns), requiring validation on actual SCADA/PMU data; (ii) the evaluated deep learning architectures exclude recent state-of-the-art tabular models (e.g., FT-transformer, TabNet), limiting the scope of our deep learning assessment; (iii) model performance may degrade by 5–15% on real data without domain adaptation. This framework advances explainable AI for power systems, providing pragmatic guidance for practitioners and establishing rigorous interpretability methodology transferable to physics-constrained ML tasks.

Remote sensing change detection aims to identify land-cover changes from bi-temporal images. Most existing methods follow a one-shot dense prediction paradigm, directly regressing a change mask from fused features. However, such approaches overlook the intrinsic frequency characteristics of change patterns. We propose Freq-RemoteVAR, a frequency autoregressive framework that reformulates change detection as a structured generation problem in the frequency domain. Instead of predicting the change mask in a single step, we introduce a next-frequency prediction paradigm, where change information is progressively generated from coarse to fine. We design a frequency-aware mask tokenization strategy that decomposes change supervision into multi-frequency token targets via Fourier transformation and quantization. We develop a Frequency VAR Transformer, which performs causal autoregressive modeling over frequency tokens. The model starts from learned mask queries and progressively predicts frequency-level tokens conditioned on previously generated tokens and bi-temporal image features, effectively capturing long-range dependencies across frequency scales. We introduce Scale-Aligned RoPE Cross Attention (SRCA) module, which aligns frequency-domain mask queries with spatial-domain bi-temporal features under a unified coordinate system, enhancing spatial-frequency consistency during generation. We propose a Change-quality Control module that adaptively modulates the generation process through dynamic normalization, attention biasing, and spatial offset adjustment, thereby suppressing pseudo-change responses and improving robustness. Extensive experiments on CDD, GZ-CD, and LEVIR-CD demonstrate that Freq-RemoteVAR consistently outperforms existing methods, particularly in challenging scenarios with complex appearance variations and noisy disturbances.

Automated assessment of second-language (L2) writing supports language teaching and testing by providing rapid, consistent scores across multiple rubric dimensions. We study whether small, openly available language models can be turned into reliable analytic raters, and we introduce PROSE, a rubric-aware, multi-objective fine-tuning method built on a compact DeBERTa-v3 encoder with low-rank adaptation. PROSE trains a regression read-out together with a soft-ordinal auxiliary head, and combines a squared-error term for calibration, a distance-aware soft-ordinal term that respects the ordered rubric bands, a concordance term that restores the spread of human ratings and improves agreement with human ratings, and a trait-relational coherence term. On the ELLIPSE corpus of L2 English essays, PROSE raises the quadratic weighted kappa (QWK) from 0.646 for a strong regression baseline to 0.661—a small but statistically reliable gain under a paired test across folds (mean difference $+0.015$—95% CI $[0.007,0.023]$, and paired t-test $p=0.007$; $p=0.030$ after correcting for the overlap between cross-validation training sets), with comparable error and improvements on all seven scoring dimensions, four of which remain significant after Holm correction, and on the Write & Improve (BEA-2019) proficiency data, it matches the strongest baseline on agreement and accuracy and clearly outperforms classification and pure ordinal training. Zero-shot prompting of three strong large language models (GPT-5.4, Claude-Sonnet-4-6, DeepSeek-Chat) reaches a kappa of only 0.13 to 0.21 on the same essays, and a distributional analysis shows that the tested large models produce compressed score distributions with means below the human mean, while the compact fine-tuned model reproduces the human score distribution. Further analyses show that the gain is stable under reasonable changes of the loss weights and holds for truncated and non-truncated essays and across gender, ethnicity, socioeconomic, and grade groups, although these analyses also reveal a shared grade-related bias and a reliance on length and lexical-diversity cues that call for caution in deployment. The results indicate that, for this task, a task-specific objective applied to a small supervised model is far more effective than general-purpose models used zero-shot; we are careful to attribute the gap to task-specific supervision and objective design rather than to model scale alone.

Precise power load forecasting is crucial for maintaining safe and efficient situation of smart grids, especially with the fusion of clean power sources. Conventional forecasting methods often fail to grasp the complex, multi-scale temporal dependencies in modern power load data. This paper proposes a novel forecasting approach based on Multi-Scale Temporal Feature Fusion with a Deep Long Short-Term Memory (MSTF-DLSTM) model. The method integrates cyclical encoding, lagged statistics, and rolling window features to comprehensively capture the multi-scale nature of load data. Additionally, a two-layer LSTM architecture combined with gradient attenuation and Huber loss enhances the model's robustness and generalization. Our approach is rigorously tested on a dataset of 35,028 hourly load samples, achieving remarkable performance: Mean Absolute Error (MAE) = 155 MW, Root Mean Square Error (RMSE) = 201 MW, R2 = 0.998, and Mean Absolute Percentage Error (MAPE) = 0.56%. The model converges in 43 epochs with a total of 42,401 parameters. Extensive statistical validation, including Shapiro-Wilk, Ljung-Box, and White tests, proves the resilience and consistency of the model. Comparative benchmarks demonstrate that the MSTF-DLSTM method outperforms existing load forecasting models, including basic LSTM and hybrid architectures, from the dual perspectives of precision and resilience. This paper provides a prospective method for improving power demand forecasting precision in smart grids, with potential applications in power generation planning, grid dispatching, and the integration of renewable energy. Future study could condiser incorporating more external variables and optimizing the model for real-time prediction.