The Fourth Industrial Revolution is fundamentally reconfiguring chemical operations through cyber-physical integration and AI-enabled systems, necessitating transformative changes in engineering education. This paper presents a comprehensive framework for developing "Cyberchemical Engineering" competencies required for Industry 4.0 readiness. Through analysis of documented deployments from global industry leaders, seven core operational transformations reshaping professional practice are identified. A tripartite competency architecture is proposed, integrating: (1) Core Process Competencies extended to dynamic, data-rich contexts; (2) Digital & Data-Driven Capabilities encompassing IIoT, machine learning, and cybersecurity; and (3) Transversal Competencies including systems thinking and ethical reasoning. Based on this architecture, a vertically integrated four-year curriculum is designed to systematically interleave foundational engineering principles with computational modeling and project-based learning. The framework explicitly maps industry challenges to pedagogical strategies, competency domains, and measurable learning outcomes aligned with ABET/EUR-ACE accreditation standards. By anchoring curriculum design in verified industrial practice, this work provides engineering educators with an evidence-based blueprint for preparing graduates capable of leading responsible digital transformation in complex, safety-critical process environments.
Automated vulnerability detection represents a fundamental pattern recognition challenge characterized by severe class imbalance and subtle semantic dependencies. While recent approaches based on Large Language Models (LLMs) have demonstrated superior reasoning capabilities, they suffer from prohibitive inference latency and "overthinking" biases when applied to realistic, deduplicated benchmarks. Conversely, static structure-aware models often fail to capture the causal logic required to distinguish complex vulnerabilities. To resolve this disparity, this paper introduces DOR-Vul, a theoretical framework that reformulates vulnerability detection through the lens of Learning Using Privileged Information (LUPI). A Cross-Modal Privileged Distillation strategy is proposed, wherein the latent reasoning patterns of a "Teacher" LLM (Gemini 2.0 Flash), augmented by a hybrid retrieval mechanism, serve as a semantic prior to regularize the latent manifold of a compact "Student" encoder (CodeT5+). A formal justification is derived, demonstrating that the generative objective acts as a semantic regularizer that optimizes a Deterministic Information Bottleneck (DIB), bounding the encoder's Rademacher complexity. This mechanism allows the student to internalize complex semantic dependencies without requiring external retrieval during inference. Empirical evaluations on the highly imbalanced Reveal benchmark ( <10% vulnerability ratio) indicate that DOR-Vul achieves a new State-of-the-Art F1-score of 53.05%, surpassing syntax-aware baselines while reducing inference latency to similar to 59.4 ms/sample (including tokenization, achieving a >15 & times; speedup). Furthermore, a Semantic-Aware Decision Fusion (SADF) mechanism is presented to robustly combine binary and semantic signals, establishing a new benchmark for efficiency and robustness in automated software security. To facilitate future research and ensure full reproducibility, the source code and pre-trained weights of DOR-Vul are made publicly available.
Training artificial neural networks is often challenged by large search spaces, slow convergence, and premature stagnation in local optima. To address these challenges, this work introduces an Enhanced Bat Algorithm (EBA) that incorporates multiple improvement strategies to enhance search robustness and ensure stable convergence. The proposed EBA is applied to optimize Gaussian centers within radial basis function (RBF) networks. Beyond optimizing Gaussian centers, we introduce a joint RBF neural-network training mechanism that simultaneously determines RBF widths and synaptic weights through a new optimization technique fulfilled by Optimum Weight Vector (OWV), the Adam optimizer, and L_2 regularization to improve generalization. Evaluation on 30 benchmark optimization functions in very high dimensional demonstrates that EBA delivers the best performance, achieves the lowest average mean error across all categories, and significantly outperforms well-known optimization algorithms including BA, PSO, GA, ACO, ALO, CSO, FO, and GWO—often by several orders of magnitude. In image recognition tasks, the proposed RBF + EBA + Adam + OWV classifier achieves state-of-the-art performance on four datasets: 97.1
The aim of this research is to introduce a numerical technique relying on the shifted Jacobi polynomials (SJPs) and the Laplace transform (LT) for anatomizing a class of the fractional variational problems (FVPs). The derivative in this problem is in the sense of the Caputo. To anatomizing FVP, first, we make an approximation for the Caputo operator ((c) D( t)(& varsigma;)m(t)) using the SJPs with unknown parameters. Then, applying LT, we access m(t) and put it in the study problem. The process of this method and the Lagrange multiplier method reduces the study problem to a system of nonlinear algebraic equations. In this system, unknown parameters are calculated by solving the obtained system, and it gives m(t). Finally, some illustrative examples are presented to demonstrate the accuracy and validity of the proposed scheme.
Friction stir spot welding (FSSW) is a popular technique for solid-state welding of both weldable and non-weldable materials. As part of this investigation, Al6063-T6 weld specimens were strengthened with silicon carbide (SiC, ∼2 wt.%, average particle size ≈ 10 μm) and graphite (Gr, ∼1 wt.%, average particle size ≈ 8 μm) particles. The current study focused on optimizing the multi-objective FSSW process to achieve the optimal combination of process parameters for stronger welds. The Taguchi L16 orthogonal array was used to design experiments on process factors, including the dwell time, tool shoulder diameter, and tool pin length. Tensile-shear strength and flash height were the two output quality characteristics measured. Multi-objective optimization was performed using a hybrid measurement of alternatives and ranking based on the Compromise Solution (MARCOS)-Taguchi technique. The significance of parameters was determined utilizing the analysis of variance technique, and a confirmatory test verified the optimality of the results. The optimal combination of parameters achieved by Taguchi was also confirmed by the MARCOS method, indicating that both approaches can be used with high reliability to optimize weld quality characteristics. It was observed that the maximum weld strength was achieved with a 2 mm pin length, a 15 mm shoulder diameter, a 1000 rpm rotation speed, and a 9 s dwell time. Pin length had a significant impact on weld quality, followed by rotation speed, shoulder diameter, and dwell time. The presence of SiC and Gr reinforcement improved microhardness and tensile-shear strength. The MARCOS-Taguchi approach effectively optimized FSSW process parameters, with confirmatory testing validating the results. MARCOS reduced the prediction error to 0.3% compared to 4.1% in Grey Relational Analysis (GRA) and 3.9% in Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).
