Browse Articles

The PROTECT databank a population based linked administrative resource on child maltreatment and intellectual disability with early findings

Correction to “The Role of Quantum Tunneling in Metal–Ligand Proton Tautomerism”

Circadian-oriented intermittent versus continuous enteral feeding is associated with differential circadian clock gene expression in critically ill patients: a randomized controlled study

Chemoenzymatic Synthesis of 6-Sulfo Lewis X-Related Glycans for Probing Their Ligand-Binding Proteins

IPGMVL: based on interactive progressive graph convolution with multi-view learning traffic flow forecasting

Renal NR4A1 predicts ischemic AKI severity and recovery after partial nephrectomy

MOD2SAD: enhancing malicious office document detection through semantic-aware deobfuscation

Abstract Advanced Persistent Threat campaigns have increasingly adopted semantic obfuscation techniques in malicious Office macros, rendering the code logic opaque to traditional scrutiny. Despite the decline in volumetric attacks following Microsoft’s default blocking policy, these sophisticated vectors can bypass traditional static syntactic pattern matching and evade dynamic analysis through environment awareness guardrails. To recover logic hidden by such obfuscation, this paper proposes a static analysis framework centered on semantic-aware code reconstruction. Unlike conventional methods, our approach reconstructs the underlying execution logic from obfuscated scripts to extract hidden Indicators of Compromise (IoCs). A notable feature of this framework is the Obfuscation Awareness and Splitting Approach. This algorithmic mechanism addresses the challenges of context window limitations and logical fragmentation by utilizing quantitative metrics to identify high-density obfuscation zones and optimally partition scripts, ensuring the preservation of semantic continuity during reconstruction. We then employ a generative semantic engine to process these partitions, feeding a hybrid feature extraction pipeline for multidimensional threat characterization. We systematically evaluate the framework on a contemporary dataset of obfuscated malicious macros. Experimental results demonstrate that our semantic reconstruction approach achieves an average precision of 74.57% in IoC extraction, outperforming conventional static analysis in our evaluation. When integrated with machine learning classifiers, the framework attains a maximum detection accuracy of 98.89%. The experimental results indicate the effectiveness and robustness of our semantic deobfuscation-based framework in real-world malware detection, offering enterprises a scalable solution for defensive deployment.

Parameter estimation of proton exchange membrane fuel cells using enhanced Fourier transform optimizer with adaptive learning mechanisms

Abstract The seven unknown parameters in the Amphlett semi-empirical proton exchange membrane fuel cell (PEMFC) model are critical to accurate performance prediction, system design, and degradation diagnosis. Gradient-based methods are often unreliable in this context because the sum-of-squared-errors (SSE) objective is highly nonlinear and multimodal, while existing population-based approaches tend to exhibit substantial run-to-run variability. To address these challenges, this paper proposes the Enhanced Fourier Transform Optimizer (EFTO), a hybrid adaptive algorithm that combines a success-history differential evolution branch with elite-guided mutation and a covariance-adaptive sampling branch, dynamically balanced through an online branch-selection mechanism. On the IEEE CEC 2017 benchmark suite (28 functions, D = 50, 30 independent runs), EFTO achieved a Friedman rank of one with an average rank of 1.607 and a Wilcoxon p  ≤ 1.17 × 10⁻ 3 against six competing algorithms. A supplementary comparison with three recognized state-of-the-art solvers found no statistically significant performance differences relative to two of the three solvers ( p  > 0.05), while EFTO recorded the lowest average runtime of 8.653 s. When applied to three commercially validated PEMFC stacks, EFTO achieved machine-precision reproducibility, with a standard deviation below 10⁻ 14 V 2 across 30 independent runs and best SSE values of 2.0656 V 2 (NedStack PS6), 1.0564 V 2 (AVISTA SR-12), and 0.8139 V 2 (Ballard Mark V). These results were statistically confirmed by Wilcoxon signed-rank tests (R⁺ = 465, p  = 1.73 × 10⁻⁶). Component wise ablation showed that the covariance-adaptive sampling mechanism was the most influential component, with removal of the covariance-adaptive sampling mechanism causing a 98.7% degradation in SSE ( p  = 1.09 × 10⁻ 24 ). Stack-specific sensitivity analysis further demonstrated that the identified parameters remain physically consistent with the electrochemical operating regime of each stack, establishing EFTO as a competitive and consistent tool for PEMFC parameters identification on the evaluated benchmark configurations.

Interstitial Nitrogen Tuning of Magnetocrystalline Anisotropy and High-Frequency Electromagnetic Properties in Nd-Based 3:29-Type Intermetallics

Revealing hidden nonlinear soliton dynamics, multistable regimes, and chaotic transitions in regularized long-wave equations under complex external forcing

Theoretical study on N(sp2)+–C(sp2)–H···O hydrogen bonds in the context of drug discovery

Tricationic Xanthylium-Catalyzed Oxyamination of Activated Alkenes Using <i>O</i> -Benzoylhydroxylamines As Bifunctional Reagents

Coagulation-assisted removal of polystyrene microplastics from aqueous solution using amine-functionalized multi-walled carbon nanotubes with okra extract

Mechanism of O ₂ /NO-Promoted Oxidative C–C Bond Cleavage in Linear Alkanes

A privacy-preserving rule fusion approach for uncertainty-aware decision-making in posture detection

Morphodynamic effects of porosity and tailwater depth on scour mitigation by gabion grade control structures

Metaheuristic optimized hybrid machine learning framework for predicting soil compaction parameters

Population-scale network embeddings expose educational divides in network structure related to right-wing populist voting

Abstract Administrative registry data can be used to construct population-scale networks whose ties reflect shared social contexts between persons. With machine learning, such networks can be encoded into numerical representations—embeddings—that automatically capture an individual’s position within the network. We created embeddings for all persons in the Dutch population from a population-scale network that represents five shared contexts: neighborhood, work, family, household, and school. To assess the informativeness of these embeddings, we used them to predict right-wing populist voting. Embeddings alone predicted right-wing populist voting above chance-level but performed worse than individual characteristics. Combining the best subset of embeddings with individual characteristics only slightly improved predictions. After transforming the embeddings to make their dimensions more sparse and orthogonal, we found that one embedding dimension was strongly associated with the outcome. Mapping this dimension back to the population network revealed that differences in educational ties and attainment corresponded to distinct network structures associated with right-wing populist voting. Our study contributes methodologically by demonstrating how population-scale network embeddings can be made interpretable, and substantively by linking structural network differences in education to right-wing populist voting.

Predicting CO2 injection profiles in heterogeneous reservoirs using a physics-aware deep learning framework

A hybrid attention-based spatio-temporal model for deepfake video detection