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SETPA: Structural evasion techniques for PDF malware detection systems
No full textPortable Document Format (PDF) is widely used because of its cross-platform compatibility, document integrity, and security features. However, their structural vulnerabilities make them a prime target for malware attacks. Machine-learning-based detection systems often struggle with feature engineering, dataset diversity, and robustness against adversarial attacks. These limitations result in high false positives, disruption of benign processes, and false negatives, enabling adversarial malware to evade detection. To address these challenges, this study introduces Structural Evasion Techniques for PDF Augmentation (SETPA), a novel evasion framework designed to bypass PDF malware detection systems. SETPA employs eight structural techniques, such as empty object streams, fake XREF table entries, and benign metadata, to obfuscate malicious content while preserving file functionality.Comprehensive experiments conducted on two leading detection models, PDFRate v2.0 and Hidost, demonstrate that SETPA consistently outperforms a Deep Reinforcement Learning (DRL)-based evasion framework. SETPA achieves evasion success rates that are 6 % to 10 % higher, and reduces average detection accuracy by 58 % to 80 %. These results confirm SETPA's robust and reliable evasion performance across various detection systems. The findings highlight SETPA's capability to exploit structural vulnerabilities in PDF detectors and underscore the need for adaptive, behavior-aware defense mechanisms that can counter entropy-driven structural evasions in emerging cyber threats
Intrinsically charge-generating polymers with long-lived free carriers for efficient photon-to-hydrogen conversion
No full textA single organic semiconductor typically struggles with inefficient intrinsic charge generation due to large binding energy (EB ≈ 0.5 electron volts) of Frenkel excitons, particularly in narrow-bandgap organic semiconductors that exhibit near-infrared (NIR) absorption. Here, we develop double-cable polymer–based nanoparticles (NPs), enabling single-component organic photocatalysts to achieve NIR photon absorption and generate long-lived free charges simultaneously. as-DCPIC, a double-cable polymer with donor polymer (PBDB-T) as electron-donating conjugated backbones and pendent NIR acceptor (TPDIC) as the electron-deficient side chains, offers potential for self-sustained photoelectric conversion. Consequently, as-DCPIC NPs exhibit significantly enhanced hydrogen evolution performance (11.88 mmol per hour per gram) compared to pristine PBDB-T or TPDIC NPs. Transient absorption spectroscopy elucidates the effective electron-hole separation inside as-DCPIC NPs, whereas decay kinetics monitor the long-lived free carriers (109 nanoseconds) in as-DCPIC NPs. Our findings demonstrate that double-cable polymers provide a powerful platform for establishing efficient single-component organic photocatalysts to generate long-lived reactive charges.This work was supported by King Abdullah University of Science and Technology (KAUST) and the KAUST Supercomputing Laboratory under project k10175 and Center of Excellence for Renewable Energy and Storage Technologies under award number 5937. W.L. acknowledges the funding support from the Beijing Natural Science Foundation (JQ21006) and National Natural Science Foundation of China (52473165). P.M. acknowledges the funding support from the Anusandhan Research Foundation (ANRF) under grant number RFJ/2023/000041
Enhanced absorption in thin-film silicon solar cells using a broadband plasmonic nanostructure
No full textThe design and fabrication of a metal-dielectric-metal absorber that achieves strong absorption from the ultraviolet (UV) to the near-infrared (near-IR) spectrum are presented. The proposed nanostructure consists of a periodic titanium (Ti) array as the top layer, a thin silicon dioxide (SiO2) spacer, and a continuous aluminum (Al) layer serving as the back reflector. Comprehensive optimization of structural parameters results in an average absorptance of 96% in the 280-1000 nm wavelength range. The proposed design exhibits polarization insensitivity and maintains high absorption efficiency under oblique incidence. Fabrication is carried out using electron beam lithography followed by a lift-off process, ensuring both high performance and manufacturing simplicity. Experimental measurements show strong agreement with numerical simulations, validating the effectiveness of the design. Furthermore, integration of the absorber into a thin-film silicon (Si) solar cell is analyzed, revealing significant enhancement in light absorption within the active layer. Owing to its broadband response, angular robustness, and structural simplicity, the proposed absorber shows strong potential for applications in solar energy harvesting, thermal emission, and advanced photovoltaic technologies.The authors gratefully acknowledge the KAUST Nanofabrication Core Lab for providing the fabrication facilities. They also extend their sincere thanks to Prof. Boon S. Ooi for granting access to the experimental setup in his Photonics Laboratory
A Geometric Approach to Brain Network Connectivity
No full textWe present an exploratory data analysis tool for visualizing and formal testing of the symmetric positive definite matrices (e.g., covariance, spectral and coherence matrices) in a multi-subject experimental setting. Our work is motivated by our clinical collaborator’s interest to determine association between functional brain connectivity (as measured by coherence) and patients’ response to treatment. For each study participant, the geometric surface boxplot (GSBox) is developed to characterize the distribution of coherence matrices through the median matrix and the 50% most central region of the data. The GSBox will also be used to detect the outlier coherence matrices as in the classical boxplot. To investigate the treatment effect, we develop a rank-based nonparametric approach to test for significant differences in coherence matrices between treatment and control groups. The proposed method is applied to a study on infantile spasms to determine the potential impact of functional brain connectivity on the infants’ response to treatment. Supplementary materials for this article are available online.The authors thank the editor and three referees for helpful comments that greatly improved the article. This study was funded in part by an Institute of Clinical and Translational Sciences UC Irvine-Children’s Hospital of Orange County Collaborative Grant and a Children’s Hospital of Orange County Pediatric Subspecialty Faculty Tithe Grant. Duy Ngo was supported by National Science Foundation (DMS-2418816)
TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning
No full textAutonomous agents operating in adversarial scenarios face a fundamental challenge: while they may know their adversaries' high-level objectives, such as reaching specific destinations within time constraints, the exact policies these adversaries will employ remain unknown. Traditional approaches address this challenge by treating the adversary's state as a partially observable element, leading to a formulation as a Partially Observable Markov Decision Process (POMDP). However, the induced belief-space dynamics in a POMDP require knowledge of the system's transition dynamics, which, in this case, depend on the adversary's unknown policy. Our key observation is that while an adversary's exact policy is unknown, their behavior is necessarily constrained by their mission objectives and the physical environment, allowing us to characterize the space of possible behaviors without assuming specific policies. In this paper, we develop Task-Aware Behavior Fields (TAB-Fields), a representation that captures adversary state distributions over time by computing the most unbiased probability distribution consistent with known constraints. We construct TAB-Fields by solving a constrained optimization problem that minimizes additional assumptions about adversary behavior beyond mission and environmental requirements. We integrate TABFields with standard planning algorithms by introducing TAB-conditioned POMCP, an adaptation of Partially Observable Monte Carlo Planning. Through experiments in simulation with underwater robots and hardware implementations with ground robots, we demonstrate that our approach achieves superior performance compared to baselines that either assume specific adversary policies or neglect mission constraints altogether. Evaluation videos and code: https://tab-fields.github.io.The work of Ornik, Puthumanaillam and Song was supported by the Office of Naval Research under grants N00014-23-1-2651 and N00014-23-1-2505. The work of Park and Yesmagambet was supported by funding from King Abdullah University of Science and Technology (KAUST)
Supercritical water deasphalting and desulphurization of heavy fuel oil with comprehensive molecular-level analysis and techno-economic analysis
No full textThe removal of sulphur from heavy fuel oil (HFO) is essential to address environmental concerns and comply with the stringent regulations imposed by the International Maritime Organization (IMO) in 2020. Previously, the acceptable limit for sulphur was 3.5 wt.%, but it was recently changed to 0.5 wt.%. Hence, upgrading HFO into low-sulphur marine fuel can be achieved by removing its heptane-insoluble asphaltene fraction. Solvent deasphalting, typically used in the petroleum industry, can be applied for deasphalting HFO, but this study investigates the supercritical water deasphalting (SCWDA) process, to develop a scalable deasphalting process for upgrading HFO into low-sulphur marine and power-generation fuel. Multiple variations of supercritical water deasphalting experiments were carried out to evaluate the effects of process parameters to optimize upgrading conditions. Characterization of the deasphalted oil (DAO) and the precipitated solid material with nitrogen and sulphur (NS) analyzer, and thermogravimetric analysis (TGA) confirmed the complete removal of asphaltene along with a significant amount of resins from HFO. The HFO, DAO, and the asphaltene fraction were further analyzed by Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and nuclear magnetic resonance (NMR). SCWDA reduced the sulphur content of HFO from 34,270 ppm to 6690 ppm. Techno-economic analysis (TEA) shows significant economic viability, resulting in lower production cost for deasphalted oil (DAO) at 647 USD per tonne (capacity: −40,000 barrel/day feed; yield of DAO: −98%; Discount rate of return: −8%; Reference year: −2023–24; Location: Saskatoon, Canada).Clean Combustion Research Center of King Abdullah University of Science and Technology (KAUST)
The authors are grateful for the financial support provided by the Clean Combustion Research Center of King Abdullah University of Science and Technology (KAUST) through the Center Competitive Funding (CCF) Program
Delay Tolerant Networks for Connectivity Enhancement in Remote Areas: Modeling, Analysis, and Design
No full textUnderstanding the interdependencies among communication performance metrics is crucial for designing efficient vehicle-assisted delay-tolerant networks (DTNs), as these metrics behave differently from those in traditional networks and directly affect data delivery reliability and timeliness. Motivated by this, and unlike most existing works that focus on protocol design, we analyze a vehicle-assisted DTN operated via TV white space (TVWS) from a communications perspective. Specifically, we consider a scenario where cities are connected to the cloud, while remote villages or IoT device clusters rely on vehicles traveling along highways to deliver data packages. Initially, we define and introduce three key performance metrics: uplink/ downlink transmission time, uplink peak age of information (PAoI), and request delay within this vehicle-assisted DTN. To comprehend the behaviors and correlations among these metrics, we first investigate a basic scenario considering only one road: while vehicles moving on a highway from one city to another, delivering data to the remote sites near the highway. Our results reveal correlations between uplink and downlink transmission times, the existence of an optimal transmitted data size per trip to minimize request delay and uplink PAoI, and that the request delay time can be approximated by a Gamma distribution when transmitting large data sizes. Subsequently, we extend the analysis to a more complex scenario involving multiple roads and information exchange between vehicles, with vehicle movement modeled as a 2D random walk. While the communication performance metrics exhibit similar trends to the basic scenario, we capture the effects of distance, vehicle arrival rate, and transmitted data size on data traveling time. Finally, we propose a special case that represents the upper bound of data traveling time within the DTN
Numerical evaluation of fingering behavior for hydrogen in aquifers
No full textUnderground hydrogen storage emerges as a strategy to address the challenge of large-scale, long-term, and economically viable hydrogen preservation, fulfilling energy demands and balancing supply discrepancies in renewable energy frameworks. This study is motivated to investigate and evaluate the fingering behavior during the hydrogen injection process. Numerical cases are performed to evaluate different simulation scenarios and sensitive influencing factors, such as fluid-rock interaction parameters and formation heterogeneity cases, on the influence of fingering flow. Different mesh upscaling schemes are compared to computational efficiency in the scale-up hydrogen injection process. Results show that fingering flow can indeed be induced during the hydrogen injection process due to the difference in flow capability between hydrogen and saline water. The characteristics of fingering flow significantly impact the performance of hydrogen displacement in saline aquifers. The hydrogen displacement path in the saline aquifer is highly sensitive to variations in the autocorrelation length. Compared to the averaging upscaling scenario, the same-statistic upscaling scenario can prevent a significant loss of numerical simulation characteristics.The authors want to thank the financial support from the Joint Industry Project of Reservoir Simulation at The University of Texas at Austin. The authors would also like to thank Computer Modelling Group Ltd (CMG) for the license support
A review of MXene memristors and their applications
No full textThe rapid growth of artificial intelligence (AI) demands efficient management of vast data quantities, a challenge that traditional von Neumann computing struggles to meet due to its power consumption and memory limitations. Memristive devices have emerged as a promising solution to overcome the von Neumann bottleneck through in-memory computing, which is crucial for neuromorphic computing advancements. Among the various materials investigated for memristor development, MXenes have recently gained attention as a highly promising platform. These materials exhibit a wide range of functional behaviors due to their unique electrochemical properties. MXenes offer several advantages, including high electrical conductivity, tunable surface chemistry, and excellent mechanical flexibility, enhancing their potential in advancing memristor technology. This review begins by introducing various MXene-based devices and highlighting switching mechanisms. It then explores the application of MXene memristors in neuromorphic and logic operations. The review concludes by addressing the challenges associated with MXene memristors, examining the obstacles they present, and considering future prospects in this dynamic field.The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST)
Flexible and Efficient Mn²⁺-Activated Organic-inorganic Zinc Halide Screens for X-Ray Imaging Applications
No full textX-ray imaging technologies play a vital role across a wide range of fields, from materials science and high-energy physics to medical diagnostics and security screening. However, conventional imaging screens are hindered by their rigidity, brittleness, and high cost, making them unsuitable for the growing demand for flexible, eco-friendly, and cost-effective imaging solutions.This work was supported by King Abdullah University of Science and Technology (KAUST)