Search Results (1,645)

Connected and Autonomous Vehicles are promising for advancing traffic safety and efficiency. However, the increased connectivity makes these vehicles vulnerable to a broad array of cyber threats. This paper presents a novel hybrid approach for intrusion detection in in-vehicle networks, specifically focusing on the Controller Area Network bus. Ensemble learning techniques are combined with sophisticated optimization techniques and dynamic adaptation mechanisms to develop a robust, accurate, and computationally efficient intrusion detection system. The proposed system is evaluated on real-world automotive network datasets that include various attack types (e.g., Denial of Service, fuzzy, and spoofing attacks). With these results, the proposed hybrid adaptive system achieves an unprecedented accuracy of 99.995% with a 0.00001% false positive rate, which is significantly more accurate than traditional methods. In addition, the system is very robust to novel attack patterns and is tolerant to varying computational constraints and is suitable for deployment on a real-time basis in various automotive platforms. As this research represents a significant advancement in automotive cybersecurity, a scalable and proactive defense mechanism is necessary to safely operate next-generation vehicles. Full article

Achieving carbon emission reduction synergy is vital for green economic transformation. This study examines whether environmental governance pressure promotes such synergy, simultaneously driving carbon reduction and pollution control. Leveraging the 2012 Ambient Air Quality Standard as a quasi-natural experiment, we employ a continuous difference-in-differences (DID) method on 250 prefecture-level cities from 2009 to 2022. Our findings reveal that increased environmental governance pressure significantly reduces both the total amount and intensity of carbon emissions, demonstrating a clear synergistic effect. This synergy is positively correlated with reductions in major air pollutants (e.g., SO₂ and NO_x), indicating that pressure curbs both the total amount and intensity of carbon emissions. Mechanistic analysis shows that this pressure primarily curtails carbon emissions by fostering green innovation and accelerating cleaner energy transitions, with no ‘green paradox’. It also promotes low-carbon industrial restructuring while reducing reliance on end-of-pipe pollution management. Heterogeneity analysis indicates stronger synergistic effects in regions with lower emission reduction costs (e.g., western China, less developed industrial bases). We recommend robust central government environmental regulation policies to amplify local governance pressure, strengthen carbon reduction synergy, and facilitate continuous green development. Full article

Terminal forensics in large mobile networks is a vital activity for identifying compromised devices and analyzing malicious actions. In contrast, the study described here begins with the domain of terminal forensics as the primary focus, rather than the threat itself. This paper proposes a new multi-criteria decision-making (MCDM) model that integrates complex picture fuzzy sets (CPFS) with the combinative distance-based assessment (CODAS), referred to throughout as complex picture fuzzy CODAS (CPF-CODAS). The aim is to assist in forensic analysis for detecting mobile botnet command and control (C&C) systems. The CPF-CODAS model accounts for the uncertainty, hesitation, and complex numerical values involved in expert decision-making, using degrees of membership as positive, neutral, and negative values. An illustrative forensic case study is constructed where three mobile devices are evaluated by three cybersecurity professionals based on six key parameters related to botnet activity. The results demonstrate that the model can effectively distinguish suspicious devices and support the use of the CPF-CODAS approach in terminal forensics of mobile networks. The robustness, symmetry, and advantages of this model over existing MCDM methods are confirmed through sensitivity and comparison analyses. In conclusion, this paper introduces a novel probabilistic decision-support tool that digital forensic specialists can incorporate into their workflow to proactively identify and prevent actions of mobile botnet C&C servers. Full article

This study develops an integrated framework for groundwater pollution source identification by coupling Principal Component Analysis (PCA), Positive Matrix Factorization (PMF), and the Mantel test, with the Qujiang River Basin as a case study. The framework enables a full-process assessment, encompassing qualitative identification, quantitative apportionment, and spatial validation of pollution drivers. Results indicate that groundwater chemistry is primarily influenced by three categories of sources: natural rock weathering, agricultural and domestic activities, and industrial wastewater discharge. Anthropogenic sources account for 73.7% of the total contribution, with mixed agricultural and domestic inputs dominating (38.5%), followed by industrial effluents (35.2%), while natural weathering contributes 26.3%. Mantel test analysis further shows that agricultural and domestic pollution correlates strongly with intensive farmland distribution in the midstream area, natural sources correspond to carbonate outcrops and higher elevations in the upstream, and industrial contributions cluster in downstream industrial zones. By integrating PCA, PMF, and Mantel analysis, this study offers a robust and transferable framework that improves both the accuracy and spatial interpretability of groundwater pollution source identification. The proposed approach provides scientific support for regionalized groundwater pollution prevention and control under complex hydrogeological settings. Full article

In low-risk and open environments, such as farms and mining sites, efficient cargo transportation is essential. Despite the suitability of autonomous driving for these environments, its high deployment and maintenance costs limit large-scale adoption. To address this issue, a modular unmanned ground vehicle (UGV) system is proposed, which is adapted from existing platforms and supports both autonomous and manual control modes. The autonomous mode uses environmental perception and trajectory planning algorithms for efficient transport in structured scenarios, while the manual mode allows human oversight and flexible task management. To mitigate the control latency and execution delays caused by platform modifications, an enhanced transformer-based general dynamics model is introduced. Specifically, the model is trained on a custom-built dataset and optimized within a bicycle kinematic framework to improve control accuracy and system stability. In road tests allowing a positional error of up to 0.5 m, the transformer-based trajectory estimation method achieved 94.8% accuracy, significantly outperforming non-transformer baselines (54.6%). Notably, the test vehicle successfully passed all functional validations in autonomous driving trials, demonstrating the system’s reliability and robustness. The above results demonstrate the system’s stability and cost-effectiveness, providing a potential solution for scalable deployment of autonomous transport in low-risk environments. Full article

High-precision angular positioning mechanisms are essential across diverse scientific and industrial applications, from optical instrumentation to automated mechanical systems. Conventional bronze–steel gear reduction units, while reliable, are often heavy, costly, and unsuitable for chemically aggressive or vacuum environments, limiting their use in advanced research setups. This work introduces a novel 1:360 gear reduction system manufactured by resin-based additive manufacturing, designed to overcome these limitations. The compact worm–gear assembly translates a single crank rotation into a precise one-degree indicator displacement, enabling fine and repeatable angular control. A primary application is the alignment of parabolic mirrors in schlieren systems, where accurate tilt adjustment is critical to correct optical alignment; however, the design is broadly adaptable to other precision positioning tasks in laboratory and industrial contexts. Compared with conventional assemblies, the resin-based reducer offers reduced weight, chemical and vacuum compatibility, and lower production cost. Its three-stage reduction design further enhances load-bearing capacity, achieving approximately double the theoretical torque transfer of equivalent commercial systems. These features establish the device as a robust, scalable, and automation-ready solution for high-accuracy angular adjustment, contributing both to specialized optical research and general-purpose precision engineering. Full article

This study presents the design, development, and experimental validation of a novel cable-driven shoulder exosuit (CDSE) for upper limb rehabilitation and assistance. Unlike existing exoskeletons, which are often bulky, limited in degrees of freedom (DOFs), or impractical for home use, the proposed DSE offers a lightweight (≈2 kg), portable, and wearable solution capable of supporting three shoulder movements: abduction, flexion, and horizontal adduction. The system employs a bioinspired tendon-driven mechanism using Bowden cables, transferring actuation forces from a backpack to the arm, thereby reducing user load and improving comfort. Mathematical models and inverse kinematics were derived to determine cable length variations for targeted motions, while control strategies were implemented using a PID-based approach in MATLAB Simscape-Multibody simulations. The prototype was fabricated in three iterations using PLA, aluminum, and carbon fiber—culminating in a durable and ergonomic final version. Experimental evaluations on a healthy subject demonstrated high accuracy in position tracking (<5% error) and torque profiles consistent with simulation outcomes, validating system robustness. The CDSE successfully supported loads up to 4 kg during rehabilitation tasks, highlighting its potential for clinical and at-home applications. This research contributes to advancing wearable robotics by addressing portability, biomechanical alignment, and multi-DOF functionality in upper limb exosuits. Full article

The objective of this study is to examine the impact of Chief Executive Officer (CEO) attributes on corporate performance in Jordan, a representative frontier market. The analysis focuses on four key CEO attributes, comprising two socio-demographic variables—age and educational—and two corporate governance-related ones—tenure and origin. Return on assets (ROA) and return on equity (ROE) are used as proxies for firm performance. Using a sample of 416 firm-year observations from companies listed on the Amman Stock Exchange (ASE) during 2015–2023, the study employs the system GMM methodology to estimate dynamic panel data models, addressing potential endogeneity and capturing the dynamic nature of firm performance. The results show that CEO age has a positive but insignificant effect, whereas CEO education and tenure significantly enhance firm performance. Conversely, CEO origin has a statistically negative impact on firm performance, reflecting the value of insider CEOs. The significant effects of CEO education, tenure, and origin—observed within the models that also incorporated firm- and country-level controls—reflect their incremental contribution to firm performance in frontier markets. Robustness checks, including controls for the COVID-19 pandemic and industry effects, confirm these findings. The study contributes to the literature by demonstrating the applicability of established theories—namely Upper Echelons, Stewardship, Resource Dependence, and Human Capital Theories—while identifying the CEO traits that drive success in frontier markets. It also offers practical guidance for shareholders, board directors, and policymakers in designing effective leadership and governance strategies. Full article

Cyber-physical infrastructures such as hospitals and smart campuses face hybrid threats that target both digital and physical domains. Traditional security solutions separate surveillance from network monitoring, leaving blind spots when attackers combine these vectors. This paper introduces ARGUS, an autonomous robotic platform designed to close this gap by correlating cyber and physical anomalies in real time. ARGUS integrates computer vision for facial and weapon detection with intrusion detection systems (Snort, Suricata) for monitoring malicious network activity. Operating through an edge-first microservice architecture, it ensures low latency and resilience without reliance on cloud services. Our evaluation covered five scenarios—access control, unauthorized entry, weapon detection, port scanning, and denial-of-service attacks—with each repeated ten times under varied conditions such as low light, occlusion, and crowding. Results show face recognition accuracy of 92.7% (500 samples), weapon detection accuracy of 89.3% (450 samples), and intrusion detection latency below one second, with minimal false positives. Audio analysis of high-risk sounds further enhanced situational awareness. Beyond performance, ARGUS addresses GDPR and ISO 27001 compliance and anticipates adversarial robustness. By unifying cyber and physical detection, ARGUS advances beyond state-of-the-art patrol robots, delivering comprehensive situational awareness and a practical path toward resilient, ethical robotic security. Full article

To address the issue of output anomalies that frequently occur in position sensors of permanent magnet synchronous motors within electromechanical actuation systems operating in harsh environments and can lead to degradation in system performance or operational interruptions, this paper proposes an integrated method for fault diagnosis and fault-tolerant control based on the Cubature Kalman Filter (CKF). This approach effectively combines state reconstruction, fault diagnosis, and fault-tolerant control functions. It employs a CKF observer that utilizes innovation and residual sequences to achieve high-precision reconstruction of rotor position and speed, with convergence assured through Lyapunov stability analysis. Furthermore, a diagnostic mechanism that employs dual-parameter thresholds for position residuals and abnormal duration is introduced, facilitating accurate identification of various fault modes, including signal disconnection, stalling, drift, intermittent disconnection, and their coupled complex faults, while autonomously triggering fault-tolerant strategies. Simulation results indicate that the proposed method maintains excellent accuracy in state reconstruction and fault tolerance under disturbances such as parameter perturbations, sudden load changes, and noise interference, significantly enhancing the system’s operational reliability and robustness in challenging conditions. Full article

Instagram is a hugely popular social media site; however, it has also been cited in many times as being a source of low self-esteem, unhappiness, and body dissatisfaction. Despite this, there is potential to use Instagram as a self-care delivery system and create positive changes in users’ mental health by showing them a specific type of image. In this paper, we use Stress Reduction Theory to demonstrate that viewing images of nature on Instagram can improve well-being (H1), by increasing feelings of connectedness with nature (H2). Furthermore, we posit this same influence will elicit more altruistic behaviour from users (H3). In a laboratory experiment, participants accessed images using either the #naturephotography hashtag, or a control hashtag (#bookshelves). Analyses showed that, in line with the proposed positive effects of SRT, viewing natural images improved well-being and positive emotions, and this was at least partially mediated by increased connectedness to nature. Future studies that use a more longitudinal approach, and examine how images can be presented within a more robust psychiatric intervention are then discussed. Full article

Background/Objectives: Modern healthcare faces a growing burden of antimicrobial resistance, prominently driven by ESKAPE pathogens. These organisms—Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.—are the leading causes of healthcare-associated infections, associated with limited therapeutic options and increased morbidity. Continuous surveillance is crucial for informing empirical therapy and guiding stewardship. Methods: We perform a single-center, seven-year retrospective study (2018–2024) at a 1000-bed tertiary hospital in Warsaw, Poland. Bloodstream isolates of ESKAPE pathogens were identified according to the EUCAST guidelines. Data were analyzed by pathogen, ward, and year of isolation. Results: From 2483 positive blood cultures, 3724 ESKAPE pathogens were recovered. S. aureus and K. pneumoniae predominated, particularly in the Intensive Care Unit and Hematology ward. Resistance analysis revealed persistently high vancomycin resistance in E. faecium, variable but notable methicillin resistance in S. aureus, and frequent ESBL production in K. pneumoniae with an alarming rise in carbapenemase-producing strains, including dual NDM + OXA-48 co-producers. A. baumannii exhibited near-universal multidrug resistance. P. aeruginosa demonstrated lower resistance rates with preserved colistin susceptibility, while Enterobacter spp. remained fully carbapenem-susceptible. Linezolid retained activity against E. faecium, while colistin remained effective against A. baumannii and P. aeruginosa. Modern β-lactam/β-lactamase inhibitor combinations were active against K. pneumoniae. Conclusions: Our findings underscore the critical role of ESKAPE pathogens in bloodstream infections and highlight divergent resistance patterns across species. The emergence of carbapenemase-producing K. pneumoniae and the persistence of multidrug-resistant A. baumannii are of particular concern. Sustained surveillance, robust antimicrobial stewardship, and tailored infection control strategies remained essential to curb the evolving resistance threat in tertiary care settings. Full article

As global climate change intensifies the frequency of extreme weather events, urban flood control and disaster reduction efforts face unprecedented challenges. With the limitations of traditional, top-down emergency management becoming increasingly apparent, many countries are actively incorporating community-based participation into flood risk governance. While research in this area is expanding, the specific impact of urban flood inundation severity on residents’ participation in relief efforts remains significantly underexplored. To address this research gap, this study employs the Community Capitals Framework (CCF) and a Gradient Boosting Decision Tree (GBDT) model to empirically analyze 1322 survey responses from Zhengzhou, China, exploring the non-linear relationship between flood severity and public participation. Our findings are threefold: (1) As the most direct source of residents’ risk perception, flood inundation severity has a significant association with their participation level. (2) This relationship is distinctly non-linear. For instance, inundation severity within a 200 m radius of a resident’s home shows a predominantly negative relation with participation level, with the negative effect lessening at extreme levels of inundation. The distance from inundated areas, conversely, exhibits an “S-shaped” curve. (3) Flood severity exhibits a significant reinforcement interaction with both communication technology levels and government organizational mobilization. This indicates that, during public crises like flash floods, robust information channels and effective organizational support are positively related to residents’ transition from passive to active participation. This study reveals the complex, non-linear associations between flood severity and civic engagement, providing theoretical support and practical insights for optimizing disaster policies and enhancing community resilience within the broader context of urban land management and sustainable development. Full article

Background: The quest for well-defined immunoadjuvants remains one of the highest priorities for the successful development of effective vaccines. Combination adjuvants, which are designed to integrate both the ability to activate a variety of immune mechanisms and synergistically improve the delivery of vaccine components, are well-positioned to address the unmet needs. The development of a preventive vaccine against hepatitis C virus (HCV)—a major public health concern—is a particular instance in which the choice of the immunoadjuvant is of utmost importance. Methods: We assembled a lipid A Toll-like receptor 4 (TLR4) agonist BECC438 and TLR7/8 agonist resiquimod (R848) on a polyphosphazene macromolecule (PCPP) to create a nanoscale immunoadjuvant-vaccine delivery system: PCPP-R+BECC438. This aqueous-based system was formulated with the HCV sE2 antigen, and the resulting vaccine candidate was evaluated in vivo for the ability to induce immune responses. Results: Co-assembly of adjuvants resulted in a visually clear aqueous system of nanoscale dimensions, monomodal size distribution, and entropy-driven interactions between components. Intramuscular immunization of mice with HCV sE2 antigen formulated in a polyphosphazene-based nano-system induced ten-fold higher IgG and IgG2a titers than the antigen adjuvanted with BECC438 alone. PCPP-R+BECC438 formulated HCV sE2 also produced statistically significant improvements in IgG2a/IgG1 ratio and more robust HCVpp neutralization ID₅₀ titers than control formulations. Conclusions: Polyphosphazene-assembled adjuvant nano-system promotes in vivo immune responses of enhanced quantity and quality of antibodies with increased potency of HCV neutralization. Full article

Background/Objectives: Frank’s sign, a diagonal earlobe crease, may reflect systemic microvascular dysfunction. We investigated whether Frank’s sign serves as a clinical marker of white matter hyperintensity (WMH) burden in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), a monogenic model of pure cerebral small vessel disease. Methods: We analyzed 81 genetically confirmed CADASIL patients (61.8 ± 12.6 years, 40.7% female) and 54 age/sex-matched controls (70.3 ± 6.6 years, 48.1% female). Frank’s sign was detected using deep learning from brain MRI-reconstructed 3D facial surfaces. WMH volumes were automatically quantified and adjusted for confounders using Random Forest regression residuals. We compared Frank’s sign prevalence between groups, assessed within-CADASIL associations, and evaluated dose–response relationships across WMH tertiles. Results: Frank’s sign prevalence was significantly higher in CADASIL versus controls (66.7% vs. 42.6%, p = 0.020), with strengthened association after multivariate adjustment (OR = 4.214, 95% CI: 1.128–15.733, p = 0.032). Within CADASIL, Frank’s sign-positive patients showed 72% greater WMH burden (51.5 ± 27.1 vs. 30.0 ± 26.1 mL, p < 0.001) and lower Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total scores (57.7 ± 19.6 vs. 71.2 ± 22.8, p = 0.006), but similar lacunes, microbleeds, and hippocampal volumes. A robust dose–response relationship emerged across WMH tertiles, with Frank’s sign prevalence increasing from 37.0% (lowest) to 74.1% (highest tertile; adjusted OR = 3.571, 95% CI: 1.134–11.253, p = 0.030). Conclusions: Frank’s sign represents an accessible biomarker of WMH burden in CADASIL, demonstrating disease-specificity and dose–response characteristics independent of vascular risk factors. The automated MRI-based detection method of Frank’s sign enables retrospective analysis of existing neuroimaging databases, transforming a bedside observation into a quantifiable neuroimaging biomarker for genetic small vessel disease stratification. Full article