Search Results (118)

This study presents the first successful integration of Fe₃O₄ and Li_0.5Cr_0.5Fe₂O₄ into a well-defined core/shell nanostructure through a two-step synthesis that combines co-precipitation and sol–gel auto-combustion methods. Unlike conventional composites, the core/shell design effectively suppresses the magnetic dead layer and promotes exchange coupling at the interface, leading to enhanced saturation magnetization, superior magnetic heating (specific absorption rate; SAR), and improved dielectric properties. Our research introduces a novel interfacial engineering strategy that simultaneously optimizes both magnetic and dielectric performance, offering a multifunctional platform for applications in magnetic hyperthermia, electromagnetic interference (EMI) shielding, and microwave devices. Full article

Background and aim: The COVID-19 pandemic, caused by SARS-CoV-2, remains a global health crisis despite vaccination efforts, necessitating novel therapeutic strategies. Natural compounds from Syzygium aromaticum (clove), such as eugenol and β-caryophyllene, exhibit antiviral and anti-inflammatory properties, while host genetic factors, including miR-21 rs1292037 polymorphism, may influence disease susceptibility and severity. This study investigates the dual approach of targeting SARS-CoV-2 via Syzygium aromaticum phytoconstituents while assessing the role of miR-21 rs1292037 in COVID-19 pathogenesis. Methods: Firstly, molecular docking and molecular dynamics simulations were employed to assess the binding affinities of eugenol and caryophyllene against seven key SARS-CoV-2 proteins—including Spike-RBD, 3CLpro, and RdRp—using SwissDock (AutoDock Vina) and the Desmond software package, respectively. Secondly, GC-MS was used to characterize the composition of clove extract. Thirdly, pharmacokinetic profiles were predicted using in silico models. Finally, miR-21 rs1292037 genotyping was performed in 100 COVID-19 patients and 100 controls, with cytokine and coagulation markers analyzed. Results: Docking revealed strong binding of eugenol to viral Envelope Protein (−5.267 kcal/mol) and caryophyllene to RdRp (−6.200 kcal/mol). ADMET profiling indicated favorable absorption and low toxicity. Molecular dynamics simulations confirmed stable binding of methyl eugenol and caryophyllene to SARS-CoV-2 proteins, with caryophyllene–7Z4S showing the highest structural stability, highlighting its strong antiviral potential. Genotyping identified the TC genotype as prevalent in patients (52%), correlating with elevated IL-6 and D-dimer levels (p ≤ 0.01), suggesting a hyperinflammatory phenotype. Males exhibited higher ferritin and D-dimer (p < 0.0001), underscoring sex-based disparities. Conclusion: The bioactive constituents of Syzygium aromaticum exhibit strong potential as multi-target antivirals, with molecular simulations highlighting caryophyllene’s particularly stable interaction with the 7Z4S protein. Methyl eugenol also maintained consistent binding across several SARS-CoV-2 targets. Additionally, the miR-21 rs1292037 polymorphism may influence COVID-19 severity through its role in inflammatory regulation. Together, these results support the combined application of phytochemicals and genetic insights in antiviral research, pending further clinical verification. Full article

The rapid devolopment of Internet of Vehicles (IoV) and Autonomous Connected Vehicles (ACVs) has increased the complexity of in-vehicle networks, exposing security vulnerabilities in traditional Controller Area Network (CAN) systems. CAN security faces dual challenges: stringent computational constraints imposed by automotive functional safety requirements and the impracticality of protocol modifications in multi-device networks. To address this, we propose a lightweight intrusion detection algorithm leveraging information entropy to analyze side-channel CAN message ID distributions. Evaluated in terms of detection accuracy, false positive rate, and sensitivity to bus load variations, the algorithm was implemented on an NXP MPC-5748G embedded platform through the AutoSar Framework. Experimental results demonstrate robust performance under low computational resources, achieving high detection accuracy with high recall (>80%) even at 10% bus load fluctuation thresholds. This work provides a resource-efficient security framework compatible with existing CAN infrastructures, effectively balancing attack detection efficacy with the operational constraints of automotive embedded systems. Full article

Long-span structures are highly vulnerable to wind-induced vibrations, which can pose a significant threat to their structural stability and safety. This paper introduces a novel monitoring method that combines Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology with Auto-Regressive Moving Average (ARMA) models, providing an innovative approach to monitoring wind-induced vibrations in large-span bridges. While previous studies have focused on individual techniques, this integrated approach is largely unexplored and offers a new perspective for structural health monitoring. By collating a series of SAR images and examining phase alterations on the bridge surface, a three-tiered detection methodology is employed to identify stable points accurately. The surface deformation data are then analyzed alongside wind speed and weather data to construct a comprehensive model elucidating the relationship between the bridge and vibrations. The ARMA model is used for real-time monitoring and assessment. Experimental results demonstrate that this method offers precise, real-time monitoring of wind-resistant stability. By leveraging the spatial accuracy and long-term monitoring capability of PS-InSAR, along with the time-series forecasting strength of ARMA models, the method enables data-driven analysis of bridge vibrations. It also provides comprehensive coverage under various conditions, enhancing the safety of long-span bridges through advanced predictive analytics. Full article

In automotive software development, testing and validation workloads are often concentrated at the end of the development cycle, leading to delays and late-stage issue discovery. To address this, virtual Electronic Control Units (vECUs) have gained attention for enabling earlier-stage verification. In our previous work, we developed a Level-4 vECU using a hardware-level emulator. However, when simulating multiple vECUs with independent clocks across distributed emulators, we observed poor timing reproducibility due to the lack of explicit synchronization. To solve this, we implemented an integration layer compliant with the functional mock-up interface (FMI), a widely used standard for simulation tool interoperability. The layer enables synchronized simulation between a centralized simulation master and independently running vECUs. We also developed a virtual CAN bus model to simulate message arbitration and validate inter-vECU communication behavior. Simulation results show that our framework correctly reproduces CAN arbitration logic and significantly improves timing reproducibility compared to conventional Linux-based interfaces. To improve simulation performance, the FMI master algorithm was parallelized, resulting in up to 85.2% reduction in simulation time with eight vECUs. These contributions offer a practical solution for synchronizing distributed Level-4 vECUs and lay the groundwork for future cloud-native simulation of automotive systems. Full article

An exhausted antiviral immune response is observed in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and post-SARS-CoV-2 syndrome, also termed long COVID. In this study, potential mechanisms behind this exhaustion were investigated. First, the viral load of Epstein–Barr virus (EBV), human adenovirus (HAdV), human cytomegalovirus (HCMV), human herpesvirus 6 (HHV6), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was determined in sputum samples (n = 29) derived from ME/CFS patients (n = 13), healthy controls (n = 10), elderly healthy controls (n = 4), and immunosuppressed controls (n = 2). Secondly, autoantibodies (autoAbs) to type I interferon (IFN-I) in sputum were analyzed to possibly explain impaired viral immunity. We found that ME/CFS patients released EBV at a significantly higher level compared to controls (p = 0.0256). HHV6 was present in ~50% of all participants at the same level. HAdV was detected in two cases with immunosuppression and severe ME/CFS, respectively. HCMV and SARS-CoV-2 were found only in immunosuppressed controls. Notably, anti-IFN-I autoAbs in ME/CFS and controls did not differ, except in a severe ME/CFS case showing an increased level. We conclude that ME/CFS patients, compared to controls, have a significantly higher load of EBV. IFN-I autoAbs cannot explain IFN-I dysfunction, with the possible exception of severe cases, also reported in severe SARS-CoV-2. We forward that additional mechanisms, such as the viral evasion of IFN-I effect via the degradation of IFN-receptors, may be present in ME/CFS, which demands further studies. Full article

Modern mobility faces increasing challenges, like carbon-free transportation and the need for flexible transportation solutions. The U-Shift II project addresses these problems through a modular electric vehicle architecture, a drive unit (Driveboard) and a vehicle body (Capsule). This separation offers high flexibility in different use cases. Current architecture paradigms, like AUTOSAR, face limitations in cost and development speed. To address these issues, this paper introduces a hybrid software architecture that integrates signal-oriented architecture (e.g., CAN bus) with service-oriented architecture for enhanced flexibility. A integral component of the hybrid architecture is the dynamic link system, which bridges these architectures by dynamically integrating Capsule-specific components into the Driveboard software stack during runtime. The performance of the developed systen and its functionality were evaluated using a hardware setup integrated into a Driveboard prototype. The dynamic link aystem was evaluated including latency measurements, as well as functionality tests. Additionally, a cloud-based reconfiguration process enhances the versatility of the Driveboard by allowing for over-the-air software updates and resource allocation. The results show a promising hybrid, reconfigurable E/E architecture that aims to enable a robust transition towards a pure service-oriented architecture required in future electric autonomous vehicles. Full article

The SARS-CoV-2 main protease (Mpro, also known as 3CLpro) is a key target for antiviral therapy due to its critical role in viral replication and maturation. This study investigated the inhibitory effects of Bofutrelvir, Nirmatrelvir, and Selinexor on 3CLpro through molecular docking, molecular dynamics (MD) simulations, and free energy calculations. Nirmatrelvir exhibited the strongest binding affinity across docking tools (AutoDock Vina: −8.3 kcal/mol; DiffDock: −7.75 kcal/mol; DynamicBound: 7.59 to 7.89 kcal/mol), outperforming Selinexor and Bofutrelvir. Triplicate 300 ns MD simulations revealed that the Nirmatrelvir-3CLpro complex displayed high conformational stability, reduced root mean square deviation (RMSD), and a modest decrease in solvent-accessible surface area (SASA), indicating enhanced structural rigidity. Gibbs free energy analysis highlighted greater flexibility in unbound 3CLpro, stabilized by Nirmatrelvir binding, supported by stable hydrogen bonds. MolProphet prediction tools, targeting the Cys145 residue, confirmed that Nirmatrelvir exhibited the strongest binding, forming multiple hydrophobic, hydrogen, and π-stacking interactions with key residues, and had the lowest predicted IC₅₀/EC₅₀ (9.18 × 10⁻⁸ mol/L), indicating its superior potency. Bofutrelvir and Selinexor showed weaker interactions and higher IC₅₀/EC₅₀ values. MM/PBSA analysis calculated a binding free energy of −100.664 ± 0.691 kJ/mol for the Nirmatrelvir-3CLpro complex, further supporting its stability and binding potency. These results underscore Nirmatrelvir’s potential as a promising therapeutic agent for SARS-CoV-2 and provide novel insights into dynamic stabilizing interactions through AI-based docking and long-term MD simulations. Full article

This paper presents a simple and streamlined compensation technique for improving the quality of synthetic aperture radar (SAR) images based on the Range Doppler Algorithm (RDA). Incorrect Doppler estimation in the space orbit, caused by unexpected radar motion errors, orbit mismatches, and other factors, can significantly degrade SAR image quality. These inaccuracies result in mismatches between the azimuth-matched filter and the received Doppler chirp signal. To address this issue, we propose a Doppler estimation method that leverages the Fractional Fourier Transform (FrFT) and cross-correlation techniques. The received signals are compared with the azimuth-matched filter based on the rotation angle in the FrFT domain, and the Doppler centroid is adjusted to achieve the optimal alignment. This process ensures high correlation values and enhanced resolution in the final SAR image. The efficacy of the proposed technique is validated through experiments using real spaceborne SAR data from the practical satellite. The results demonstrate significant improvements in image quality and resolution compared to conventional algorithms, highlighting the advantages of our approach for various remote sensing applications. Full article

The rapid evolution of automotive software necessitates efficient and accurate development and verification processes. This study proposes a virtual electronic control unit (vECU) that allows for precise software testing without the need for hardware, thereby reducing developmental costs and enabling cloud-native development. The software was configured and built on a Hyundai Autoever AUTomotive Open System Architecture (AUTOSAR) classic platform, Mobilgene, and Renode was used for high-fidelity emulations. Custom peripherals in C# were implemented for the FlexTimer, system clock generator, and analog-to-digital converter to ensure the proper functionality of the vECU. Renode’s GNU debugger server function facilitates detailed software debugging in a cloud environment, further accelerating the developmental cycle. Additionally, automated testing was implemented using a vECU tester to enable the verification of the vECU. Performance evaluations demonstrated that the vECU’s execution order and timing of tasks and runnable entities closely matched those of the actual ECU. The vECU tester also enabled fast and accurate verification. These findings confirm the potential of the AUTOSAR-compatible Level-4 vECU to replace hardware in development processes. Future efforts will focus on extending capabilities to emulate a broader range of hardware components and complex system integration scenarios, supporting more diverse research and development efforts. Full article

Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. The statistical properties of parameter and forecast estimates strongly depend on the structure of such matrices. The least squares (LS) method is the most flexible and can estimate systems of large dimensions; however, it is biased in the presence of multilateral (sparse) matrices. Instead, the unilateral specification of SAR models provides triangular weight matrices that allow consistent LS estimates and sequential prediction functions. These two properties are strictly related and depend on the linear and recursive nature of the system. In this paper, we show the better performance in out-of-sample forecasting of unilateral SAR (estimated with LS), compared to multilateral SAR (estimated with maximum likelihood, ML). This conclusion is supported by numerical simulations and applications to real geological data, both on regular lattices and irregularly distributed points. Full article

Self-driving vehicles have attracted significant attention in the automotive industry that is heavily investing to reach the level of reliability needed from these safety critical systems. Security of in-vehicle communications is mandatory to achieve this goal. Most of the existing research to detect anomalies for in-vehicle communication does not take into account the low processing power of the in-vehicle Network and ECUs (Electronic Control Units). Also, these approaches do not consider system level isolation challenges such as side-channel vulnerabilities, that may arise due to adoption of new technologies in the automotive domain. This paper introduces and discusses the design of a framework to detect anomalies in in-vehicle communications, including side channel attacks. The proposed framework supports real time monitoring of data exchanges among the components of in-vehicle communication network and ensures the isolation of the components in in-vehicle network by deploying them in Trusted Execution Environments (TEEs). The framework is designed based on the AUTOSAR open standard for automotive software architecture and framework. The paper also discusses the implementation and evaluation of the proposed framework. Full article

A power-efficient 16-bit 1-MS/s successive approximation register (SAR) analog-to-digital converter (ADC) is presented in this paper. High-bit sampling makes the bridge capacitance in the digital-to-analog converter (DAC) a unit one, eliminating fractional capacitance mismatch. The high-precision comparator is composed of a four-stage preamplifier and a strong-arm latch, with auto-zeroing used to mitigate input offset further. Digital foreground calibration based on low-bit weight is implemented to correct DAC capacitance mismatch. The post-layout simulation results show that the core ADC achieves 95.61 dB SNDR and 105.1 dB SFDR with calibration, consuming 5.4 mW power under a 3.3 V supply voltage, corresponding to a Schreier figure of merit (FoM) of 175.3 dB. The ADC core area is 1.06 mm² in the 180 nm CMOS technology. Full article

Due to the increasing complexity of vehicle software, it is becoming increasingly difficult to comprehensively test all requirements. This inevitably means that alternative test methods, e.g., simulation-based methods, must be used more frequently. However, the challenge involves identifying appropriate requirements that can be technically tested in a simulation environment initially. The present work is aimed at evaluation and optimization of the effectiveness of software-in-the-loop (SiL) simulations in the testing process of vehicle software. The focus is on supporting the testing process by shifting specific test cases from hardware-in-the-loop (HiL) test benches to SiL-based simulations. For this purpose, a systematic approach was developed to analyze and categorize requirements, enabling precise and efficient allocation of test cases. Furthermore, a detailed review and recommendation for improving the ProSTEP iViP standard for virtual electronic control units (vECU) was carried out. The developed matrix associates the defined requirement clusters with different classifications of vECUs, facilitating the identification of suitable test environment types for conducting specific test cases. By assigning test cases to appropriate vECU levels, the testing processes can be targeted and cost-optimized. Finally, the theoretical results were evaluated in an SiL simulation environment. It was observed that a significant part of the requirements could effectively be tested using a vECU. These findings confirmed the potential of SiL simulation environments to not only support, but also enhance, the testing process for vehicle software by providing a cost-effective and flexible complement to traditional HiL test benches. Full article

The outbreak of SARS-CoV-2, also known as the COVID-19 pandemic, is still a critical risk factor for both human life and the global economy. Although, several promising therapies have been introduced in the literature to inhibit SARS-CoV-2, most of them are synthetic drugs that may have some adverse effects on the human body. Therefore, the main objective of this study was to carry out an in-silico investigation into the medicinal properties of Petiveria alliacea L. (P. alliacea L.)-mediated phytocompounds for the treatment of SARS-CoV-2 infections since phytochemicals have fewer adverse effects compared to synthetic drugs. To explore potential phytocompounds from P. alliacea L. as candidate drug molecules, we selected the infection-causing main protease (Mpro) of SARS-CoV-2 as the receptor protein. The molecular docking analysis of these receptor proteins with the different phytocompounds of P. alliacea L. was performed using AutoDock Vina. Then, we selected the three top-ranked phytocompounds (myricitrin, engeletin, and astilbin) as the candidate drug molecules based on their highest binding affinity scores of −8.9, −8.7 and −8.3 (Kcal/mol), respectively. Then, a 100 ns molecular dynamics (MD) simulation study was performed for their complexes with Mpro using YASARA software, computed RMSD, RMSF, PCA, DCCM, MM/PBSA, and free energy landscape (FEL), and found their almost stable binding performance. In addition, biological activity, ADME/T, DFT, and drug-likeness analyses exhibited the suitable pharmacokinetics properties of the selected phytocompounds. Therefore, the results of this study might be a useful resource for formulating a safe treatment plan for SARS-CoV-2 infections after experimental validation in wet-lab and clinical trials. Full article