Search Results (110)

In this study, we introduce an enhanced hybrid Autoencoder–Dense–Transformer Neural Network (AE-DTNN) model for developing an effective intrusion detection system (IDS) aimed at improving the performance and robustness of threat detection strategies within a rapidly changing and increasingly complex network landscape. The Autoencoder component restructures network traffic data, while a stack of Dense layers performs feature extraction to generate more meaningful representations. The Transformer network then facilitates highly precise and comprehensive classification. Our strategy incorporates adaptive synthetic sampling (ADASYN) for both binary and multi-class classification tasks, complemented by the edited nearest neighbors (ENN) technique and the use of class weights to mitigate class imbalance issues. In experiments conducted on the NF-BoT-IoT-v2 dataset, the AE-DTNN-based IDS achieved outstanding performance, with 99.98% accuracy in binary classification and 98.30% in multi-class classification. On the NSL-KDD dataset, the model reached 98.57% accuracy for binary classification and 97.50% for multi-class classification. Additionally, the model attained 99.92% and 99.78% accuracy in binary and multi-class classification, respectively, on the CSE-CIC-IDS2018 dataset. These results demonstrate the exceptional effectiveness of the proposed model in contrast to conventional approaches, highlighting its strong potential to detect a broad range of network intrusions with high reliability. Full article

Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing its development from foundational principles to the latest advances. We begin with the theoretical background, detailing the mechanisms by which inelastic tunneling processes generate vibrational fingerprints of molecules, and highlighting how IETS complements optical spectroscopies by accessing electrically driven vibrational excitations. We then discuss recent progress in experimental techniques and device architectures that have broadened the applicability of IETS. Central focus is given to emerging applications of IETS over the last decade: molecular sensing (identification of chemical bonds and conformational changes in junctions), thermoelectric energy conversion (probing vibrational contributions to molecular thermopower), molecular switches and functional devices (monitoring bias-driven molecular state changes via vibrational signatures), spintronic molecular junctions (detecting spin excitations and spin–vibration interplay), and advanced data analysis approaches such as machine learning for interpreting complex tunneling spectra. Finally, we discuss current challenges, including sensitivity at room temperature, spectral interpretation, and integration into practical devices. This review aims to serve as a thorough reference for researchers in physics, chemistry, and materials science, consolidating state-of-the-art understanding of IETS in molecular junctions and its growing role in molecular-scale device characterization. Full article

With the growing number and diversity of network attacks, traditional security measures such as firewalls and data encryption are no longer sufficient to ensure robust network protection. As a result, intrusion detection systems (IDSs) have become a vital component in defending against evolving cyber threats. Although many modern IDS solutions employ machine learning techniques, they often suffer from low detection rates and depend heavily on manual feature engineering. Furthermore, most IDS models are designed to identify only a limited set of attack types, which restricts their effectiveness in practical scenarios where a network may be exposed to a wide array of threats. To overcome these limitations, we propose a novel approach to IDSs by implementing a combined dataset framework based on an enhanced hybrid principal component analysis–Transformer (PCA–Transformer) model, capable of detecting 21 unique classes, comprising 1 benign class and 20 distinct attack types across multiple datasets. The proposed architecture incorporates enhanced preprocessing and feature engineering, followed by the vertical concatenation of the CSE-CIC-IDS2018 and CICIDS2017 datasets. In this design, the PCA component is responsible for feature extraction and dimensionality reduction, while the Transformer component handles the classification task. Class imbalance was addressed using class weights, adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN). Experimental results show that the model achieves 99.80% accuracy for binary classification and 99.28% for multi-class classification on the combined dataset (CSE-CIC-IDS2018 and CICIDS2017), 99.66% accuracy for binary classification and 99.59% for multi-class classification on the CSE-CIC-IDS2018 dataset, 99.75% accuracy for binary classification and 99.51% for multi-class classification on the CICIDS2017 dataset, and 99.98% accuracy for binary classification and 98.01% for multi-class classification on the NF-BoT-IoT-v2 dataset, significantly outperforming existing approaches by distinguishing a wide range of classes, including benign and various attack types, within a unified detection framework. Full article

TiVCr-based alloys are well-explored body-centered cubic (BCC) materials for hydrogen storage applications that can potentially store higher amounts of hydrogen at moderate temperatures. The challenge remains in optimizing the alloy-hydrogen stability, and several transition elements have been found to support the reduction in the hydride stability. In this study, Ni and Nb transition elements were incorporated into the TiVCr alloy system to thoroughly understand their influence on the (de)hydrogenation kinetics and thermodynamic properties. Three different compositions, (TiVCr)₉₅Ni₅, (TiVCr)₉₀ Ni₁₀, and (TiVCr)₉₅Ni₅Nb₅, were prepared via arc melting. The as-prepared samples showed the formation of a dual-phase BCC solid solution and secondary phase precipitates. The samples were characterized using hydrogen sorption studies. Among the studied compositions, (TiVCr)₉₀Ni₁₀ exhibited the highest hydrogen absorption capacity of 3 wt%, whereas both (TiVCr)₉₅Ni₅ and (TiVCr)₉₀Ni₅Nb₅ absorbed up to 2.5 wt% hydrogen. The kinetics of (de)hydrogenation were modeled using the JMAK and 3D Jander diffusion models. The kinetics results showed that the presence of Ni improved hydrogen adsorption at the interface level, whereas Nb substitution enhanced diffusion and hydrogen release at room temperature. Thus, the addition of Ni and Nb to Ti-V-Cr-based high-entropy alloys significantly improved the hydrogen absorption and desorption properties at room temperature for gas-phase hydrogen storage. Full article

Malignant pleural effusion (MPE) can lead to pleural organization with loculation and impaired drainage. This condition is becoming increasingly more common due to advancements in cancer therapy and extended patient survival. Factors such as repeated thoracentesis through an indwelling pleural catheter (IPC), intrapleural bleeding, and tumor progression contribute to MPE organization. Loculated MPE causes breathlessness and reduced quality of life, and current therapies, including intrapleural fibrinolytic or enzymatic therapy (IPFT/IET), have limitations in efficacy and safety. Identifying new therapeutic targets is crucial for improving treatment outcomes. Research is needed to understand the role of profibrogenic factors in pleural neoplasia, their regulation, and their impact on different stages of pleural organization. The development of a rabbit model of organizing MPE could provide insights into underlying mechanisms and novel interventions. Comparative studies of pleural tissues and effusions from MPE patients and other forms of pleural organization may reveal valuable information. Cellular and molecular profiling, assessment of biomarkers, and personalized IPFT dosing are potential areas of investigation. Suppression of PAI-1 activity and the role of hyaluronic acid in malignant mesothelioma are also important research directions. Understanding the profibrogenic capacity of pleural mesothelial cells undergoing mesenchymal transition (MesoMT) and identifying key contributors and effectors involved in this process are essential for developing effective treatments for loculated MPE. Full article

This paper investigates the use of Al₂O₃ nano-powder-stirred micro-EDM process for generating micro-channels. This study focuses on the effect of critical machining process parameters, such as capacitance levels and nano-powder concentration, on the micro-channel fabrication performance in terms of TWR, MRR, depth, and width. A two-stage nested ANOVA is employed to understand the effect of powder concentration within different capacitance levels. The results show that the powder concentration significantly influences the system’s performance in conjunction with the capacitance. At low (100 pF) and high (1000 pF) capacitance, the addition of Al₂O₃ nano-powder increases the MRR, depth, and width but decreases TWR up to a concentration of 1.0 g/L. A desirability function analysis (DFA) tool identified the best overall performance from 14 experiments, revealing that 100 pF and 1 g/L yield the optimal outcomes. Full article

To explore the impact of the aspect ratio of the channels in the flow fields of solid oxide electrolysis cells on the performance of the cell, we developed three-dimensional models for cells with varying aspect ratios. Our findings revealed that channels with low and high aspect ratios exhibit higher maximum pressure drops, whereas those with medium aspect ratios have the lowest pressure drops. Additionally, the mole fraction of the hydrogen decreases as the channel’s aspect ratio increases. We also computed the polarization curves for SOEC operating under three distinct aspect ratio channels. Our results suggest that structures with low aspect ratios exhibit the poorest electrochemical performance, suitable only for brief operations at low current densities; medium aspect ratio structures exhibit a balanced performance, making them suitable for various operating conditions; and high aspect ratio structures are best suited for operations at high current densities. This study on selecting different aspect ratios aids in determining the optimal channel parameters for different operating conditions, ultimately enhancing the performance of solid oxide electrolysis cells. Full article

As the attention to using hydrogen as a potential energy storage medium for power generation and mobility increases, hydrogen production, storage, and transportation safety should be considered. For instance, hydrogen’s extreme physical and chemical properties and the wide range of flammable concentrations raise many concerns about the current safety measures in processing other flammable gases. Hydrogen cloud accumulation in the case of leakage in confined spaces can lead to reaching the hydrogen lower flammability limit (LFL) within seconds if the hydrogen is not properly evacuated from the space. At Jülich Research Centre, hydrogen mixed with natural gas is foreseen to be used as a fuel for the central heating system of the campus. In this work, the release, dispersion, formation, and spread of the hydrogen cloud in the case of hydrogen leakage inside the central utility building of the campus are numerically simulated using the OpenFOAM-based containmentFOAM CFD codes. Additionally, different ventilation scenarios are simulated to investigate the behavior of the hydrogen cloud. The results show that locating exhaust openings close to the ceiling and the potential leakage source can be the most effective way to safely evacuate hydrogen from the building. Additionally, locating the exhaust outlets near the ceiling can decrease the combustible cloud volume by more than 25% compared to side openings far below the ceiling. Also, hydrogen concentrations can reach the LFL in case of improper forced ventilation after only 8 s, while it does not exceed 0.15% in the case of natural ventilation under certain conditions. The results of this work show the significant effect of locating exhaust outlets near the ceiling and the importance of natural ventilation to mitigate the effects of hydrogen leakage. The approach illustrated in this study can be used to study hydrogen dispersion in closed buildings in case of leakage and the proper design of the ventilation outlets for closed spaces with hydrogen systems. Full article

This paper explores Great Basin arid-zone hunter–forager rock art as signalling behaviour. The rock art in Lincoln County, Nevada, is the focus, and this symbolic repertoire is analysed within its broader archaeological and ethnographic contexts. This paper mobilises an explicitly theoretical approach which integrates human behavioural ecology (HBE) and the precepts of information exchange theory (IET), generating assumptions about style and signalling behaviour based on hunter–forager mobility patterns. An archaeological approach is deployed to contextualise two characteristic regional motifs—the Pahranagat solid-bodied and patterned-bodied anthropomorphs. Contemporary Great Basin Native American communities see Great Basin rock writing through a shamanistic ritual explanatory framework, and these figures are understood to be a powerful spirit figure, the Water Baby, and their attendant shamans’ helpers. This analysis proposes an integrated model to understand Great Basin symbolic behaviours through the Holocene: taking a dialogical approach to travel backward from the present to meet the archaeological past. The recursive nature of rock art imagery and its iterative activation by following generations allows for multiple interpretive frameworks to explain Great Basin hunter–forager and subsequent horticulturalist signalling behaviours over the past ca. 15,000 years. Full article

The electrochemical nitrogen reduction reaction (eNRR) for electrochemical ammonia (NH₃) synthesis is considered a promising alternative to the energy-intensive and highly CO₂-emitting Haber-Bosch process. In numerous experiments, the Nafion membrane has been used as an electrolyte or separator. However, Nafion adsorbs and desorbs NH₃, leading to erroneous measurements and making reproducibility extremely difficult. This study systematically investigates the interaction between NH₃ and Nafion, underscoring the strength of the interaction between ammonium-ions (NH₄⁺) and protons (H⁺). We found that minute quantities of synthesized NH₃ are prone to persist within the membrane, albeit without affecting the ion conductivity and resistivity of Nafion. Consequently, the removal of NH₃ from the membrane can occur under conditions where synthesis is not viable. The objective of this work is to heighten awareness regarding the interaction between NH₃ and Nafion and contribute to the attainment of reliable and reproducible outcomes in eNRRs. Full article

International projects study the safety aspects of the storage and long-distance transportation of liquid hydrogen at large scales. Catalytic recombiners, which are today key elements of hydrogen risk mitigation in nuclear power plants, could become an efficient safety device to prevent flammable gas mixtures after liquid hydrogen leakages in closed rooms. This study tackles fundamental questions about the operational behavior of typical recombiner catalysts related to the conditions of the start-up and the termination of the catalytic reaction. For this purpose, small-scale catalyst sheets with coatings containing either platinum or palladium as active materials were exposed to gas mixtures of air and hydrogen of up to 4 vol.% at temperatures between −50 °C and 20 °C. Both platinum and palladium showed variation to performance and had stochastic results. Overall, the initialized platinum catalyst was better than the palladium. The experimental results show that the transfer of the recombiner technology from its current application is not easily possible. Full article

We present a comprehensive vibrational spectroscopic analysis of vertical molecular junction devices constructed using single-layer graphene electrodes separated by an aryl alkane monolayer. In this work, inelastic electron tunneling spectroscopy (IETS) is employed to probe molecular vibrations within the junction, providing an in situ fingerprint of the molecules. Graphene has emerged as a promising electrode material for molecular electronics due to its atomically thin, mechanically robust nature and ability to form stable contacts. However, prior to this study, the vibrational spectra of molecules in graphene-based molecular junctions had not been fully explored. Here, we demonstrate that vertically stacked graphene electrodes can be used to form stable and reproducible molecular junctions that yield well-resolved IETS signatures. The observed IETS spectra exhibit distinct peaks corresponding to the vibrational modes of the sandwiched aryl alkane molecules, and all major features are assigned through density functional theory calculations of molecular vibrational modes. Furthermore, by analyzing the broadening of IETS peaks with temperature and AC modulation amplitude, we extract intrinsic vibrational linewidths, confirming that the spectral features originate from the molecular junction itself rather than extrinsic noise or instrumental artifacts. These findings conclusively verify the presence of the molecular layer between graphene electrodes as the charge transport pathway and highlight the potential of graphene–molecule–graphene junctions for fundamental studies in molecular electronics. Full article

In this work, we present a study on the thermal/transport properties of a novel deep eutectic solvent (DES) obtained by using N-methyltrifluoroacetamide (FNMA) as the hydrogen bond donor (HBD) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) as the hydrogen bond acceptor (HBA). The binary phase diagram, thermal stability, flammability, viscosity and ionic conductivity of the as-prepared DESs were investigated at atmospheric pressure. The binary phase diagram shows a range of eutectic molar ratios (x_LiTFSI = 0.2~0.33), with the lowest deep eutectic temperature of −84 °C. At x_LiTFSI = 0.2 (i.e., FNMA:LiTFSI = 4:1 and denoted as DES-4:1). The as-prepared DES composition exhibits high thermal stability (onset temperature of weight loss = 78 °C), a low viscosity (η = 48.9 mPa s at 25 °C), relatively high ionic conductivity (σ = 0.86 mS cm⁻¹ at 25 °C) and non-flammability. The transport properties, including ionic conductivity and viscosity, as a function of temperature are in accordance with the Vogel–Fulcher–Tammann (VFT) equations. With increasing molar ratio of HBD vs. HBA, the viscosity decreases, and the ionic conductivity increases at a given temperature between 25 °C and 80 °C. The roughly equal pseudo-activation energies for ion transport and viscous flow in each composition imply a strong coupling of ion transport and viscous flow. Walden plots indicate vehicular transport as the main ion transport mechanism for the DES-4:1 and DES-3:1 compositions; meanwhile, it was confirmed that the ionic conductivity and viscous flow are strictly coupled. The present work is expected to provide strategies for the development of wide-temperature-range and safer electrolytes with low salt concentrations. Full article

This article explores the transformative advances in soft machines, where biology, materials science, and engineering have converged. We discuss the remarkable adaptability and versatility of soft machines, whose designs draw inspiration from nature’s elegant solutions. From the intricate movements of octopus tentacles to the resilience of an elephant’s trunk, nature provides a wealth of inspiration for designing robots capable of navigating complex environments with grace and efficiency. Central to this advancement is the ongoing research into bioinspired materials, which serve as the building blocks for creating soft machines with lifelike behaviors and adaptive capabilities. By fostering collaboration and innovation, we can unlock new possibilities in soft machines, shaping a future where robots seamlessly integrate into and interact with the natural world, offering solutions to humanity’s most pressing challenges. Full article

This study aimed to assess the association between the cycle threshold (Ct) values of SARS-CoV-2 and the risk of death from COVID-19 in adult patients from the Amazonas region of Peru during the circulation of the Lambda, Gamma, and Delta variants. The study population included both hospitalized and outpatient patients, symptomatic and asymptomatic, between August 2020 and August 2021. The standardized Ct values of the ORF1ab gene were categorized into low and high Ct groups based on the median Ct value (28.4). Mortality data within 60 days were obtained from the Peruvian epidemiological surveillance system (Notiweb). The risk of death was estimated using Cox regression, adjusting for relevant predictors and potential confounding variables. Among symptomatic COVID-19 patients, no significant difference in the risk of death was observed between those with low and high Ct values (adjusted hazard ratio [aHR] = 1.61; 95% confidence interval [CI], 0.97–2.67; p = 0.067). However, hospitalized patients with low Ct values had a significantly higher risk of death compared to those with high Ct values (aHR = 1.82; 95% CI, 1.06–3.12; p = 0.030). This association persisted after adjusting for age, sex, occupational group, symptom duration, comorbidities, and epidemic dynamics. In conclusion, while Ct values in symptomatic COVID-19 patients (both hospitalized and outpatient) are not associated with a 60-day mortality risk, a low Ct value is linked to an increased risk of death in hospitalized patients. Full article