Search Results (196)

Four-wheel independent steering (4WIS) vehicles can improve low-speed maneuverability and high-speed stability by independently regulating the steering angles of all four wheels. However, under large-curvature trajectories, parameter perturbations, and external disturbances, inconsistent coordination among the four-wheel steering angles may increase tire lateral slip, yaw response deviation, and trajectory tracking errors. To address the difficulty of conventional trajectory tracking methods in simultaneously ensuring geometric consistency, tracking accuracy, and robustness, this paper proposes an adaptive sliding mode trajectory tracking control method based on instantaneous center of rotation (ICR) constraints. First, the tire instantaneous turning center (TTC) of each wheel is derived using rigid-body spatial kinematics, and the TTCs are mapped onto a unified vehicle-body reference plane based on the SAE J670 coordinate system to obtain a real-time vehicle-level ICR estimation. Second, a lateral–yaw dynamic model and a trajectory tracking error model are established. The yaw rate and sideslip angle are corrected using ICR geometric information, and an adaptive sliding mode controller is designed with an equivalent control term, adaptive switching gain, adaptive boundary layer, and sideslip suppression term. The uniform ultimate boundedness of the sliding variable and closed-loop tracking errors is proven using Lyapunov theory. Finally, MATLAB (2023a)2024/CarSim (2019) co-simulations are conducted under small-curvature sinusoidal, double-lane-change, large-curvature sinusoidal, low-adhesion, and mass-perturbation conditions. The results show that the proposed ICR-SMC method significantly reduces lateral and heading errors compared with U-LQR and U-SMC, especially under large-curvature and low-adhesion conditions, demonstrating improved tracking accuracy and robustness for 4WIS vehicles. Full article

Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty. This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery. Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery. The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty. Full article

Urban air pollution, especially the problem of PM_2.5, is one of the major health challenges facing the planet today. To provide accurate PM_2.5 predictions despite data noise and missing data, the authors proposed a deep learning model. We constructed a Stacked Autoencoder–Convolutional Neural Network–Bidirectional Long Short-Term Memory–Long Short-Term Memory (SAE-CNN-BiLSTM-LSTM) model that (1) utilises convolutional layers to extract spatial features from the input data, (2) employs bidirectional LSTM layers to capture long-term temporal dependencies, and (3) utilises an autoencoder to learn latent representations of the data to mitigate the effects of missing data. The model was trained on a large dataset of hourly measurements of air quality and meteorological parameters collected between 2018 and 2020 in Klang, Malaysia. The performance of the model on data that were not used during training was evaluated using a range of metrics. The SAE-CNN-BiLSTM-LSTM model achieved a test RMSE of approximately 11.97 µg/m³ and an R² statistic of approximately 0.85 for PM_2.5 concentrations, outperforming the other models tested on the same datasets. The additional metrics of MAE, MAPE, Mean Bias Error, and Index of Agreement confirmed the model’s accuracy and low bias in the prediction of air pollution levels. Statistical tests, such as the Diebold–Mariano test, confirmed the significance of the model’s accuracy over the CNN-LSTM models. These findings indicate that the proposed model effectively captures the dynamics of the air pollution data. The proposed model structure efficiently achieved an accurate and lightweight model for urban air pollution forecasting. Full article

Unexpected cut-in events may elicit driver anger even when braking is partly supported by driver-assistance systems. This preliminary simulator study examined whether SAE Level 1 longitudinal braking assistance alters affective responses to dangerous cut-in events. Ten young male licensed drivers completed three within-subject scenarios: manual driving without a cut-in, manual driving with a dangerous cut-in, and SAE Level 1 braking assistance with a dangerous cut-in. STAXI State Anger and salivary amylase were measured before and after each scenario. STAXI State Anger showed an overall scenario effect (p = 0.0045), but Holm-corrected post hoc comparisons were not statistically significant. In particular, the data did not indicate an anger-reducing effect of braking assistance compared with manual driving during the same cut-in event. Salivary amylase showed no significant scenario effect (p = 0.273). These preliminary findings suggest that physical braking assistance alone may be insufficient to mitigate anger-related responses to sudden cut-in events, and they motivate future controlled studies of cognitive support and system intent communication in ADAS contexts. Full article

Autonomous robotic systems are being deployed in commercial, healthcare, logistics, and mixed-use built environments at a rate that significantly outpaces the adaptive capacity of existing building design and management paradigms. Buildings have historically been conceived exclusively for human occupants, and the resulting absence of a structured, scalable framework for evaluating or designing robot-ready facilities constitutes a critical gap in both research and professional practice. This article introduces the Robot-Friendly Buildings Level of Service (RFB-LOS) framework: a five-tier hierarchical classification system that characterises the degree to which a built environment supports autonomous robotic operations across six evaluative dimensions—building intelligence, active infrastructure, architectural planning, accessibility, observability, and safety. The framework spans a continuum from Robot Excluded (RFB-LOS-1), in which a building has no awareness of its robotic occupants, to Physical AI Robot Optimised (RFB-LOS-5), in which a Physical AI middleware layer assumes the highest command authority within a coordinated human–robot–building ecosystem. Drawing structural inspiration from the SAE J3016 Levels of Driving Automation, the EU Smart Readiness Indicator, HIMSS EMRAM, and BREEAM/LEED sustainability certification, the RFB-LOS framework is positioned as a foundational standard for the built environment and systems engineering community. Five real-world case studies spanning retail, hospitality, healthcare, and corporate sectors across four countries validate the framework’s tier assignments against observed operational outcomes. Full article

The transition to automated vehicles (AVs) introduces complex human factors and system-level challenges across Society of Automotive Engineers (SAE) Levels 0–5, with profound implications for the long-term viability of future transport infrastructure. Drawing on a synthesis of socio-technical, cognitive, and behavioural adaptation theories, this study develops an integrated framework to analyse the evolving relationships among driving automation, human behaviour, system risks, and urban sustainability. The findings demonstrate that human-factor risks are inherently nonlinear, meaning they do not decrease proportionally as technology advances; instead, risk profiles peak significantly at intermediate automation levels (SAE 2–3) due to supervisory fatigue and delayed takeovers, introducing severe traffic flow volatility and localised micro-congestion that directly compromise the environmental efficiency of sustainable transport systems. As these risks reconfigure into institutional and digital infrastructure dependencies at higher levels (SAE 4–5), the primary constraint shifts toward network readiness. Through an analysis of real-world AV deployment case studies and a structured narrative literature review, this paper identifies critical operational discontinuities and mixed-traffic complexities that threaten urban grid resilience. This study proposes a conceptual framework that translates these cross-level socio-technical insights into actionable deployment pathways, providing policymakers with adaptive governance models, transportation planners with mixed-traffic management strategies aimed at preserving network efficiency, infrastructure agencies with physical and digital readiness criteria for long-term asset sustainability, and AV developers with human–machine interface optimisation frameworks to secure human-centric safety within sustainable smart city networks. Full article

Crash severity prediction is critical for emergency response, infrastructure spending, and risk communication. Although machine learning has been widely applied to this problem, three gaps prevent practical deployment: uncalibrated probability scores, SHAP-based explanations whose faithfulness has not been verified, and models never tested outside their training jurisdiction. The proposed framework, SAE-XCrash (Safety-Aware and Explainable Crash Severity Prediction), addresses all three using two public datasets—US-Accidents (7.0 million records, 2016–2023) and UK STATS19 (approximately 1,010,000 records, 2016–2022)—with strict temporal splits throughout. Notably, the US-Accidents severity label measures traffic disruption duration, not injury outcome; results should be interpreted accordingly. Previously unknown label-schema drift led to a revised binary target with Severity 4 as the only positive class. Five classifiers are compared. Post hoc isotonic calibration reduces Expected Calibration Error by 97.3% at negligible discrimination cost. A four-step quantitative SHAP audit confirms statistically significant deletion faithfulness; however, explanation stability fails at realistic perturbation levels (54.3% low-stability fraction at sigma = 0.05), driven by spatial data sparsity in sparse geohash cells—a negative result that carries direct operational implications for deployment. A three-tier cross-dataset transfer experiment (zero-shot, recalibration, full retrain) shows that temporal features transfer robustly across jurisdictions, while spatial memorization is the primary generalization barrier. All code, split indices, and model artifacts are publicly available. Full article

Modern vehicles depend on over-the-air (OTA) updates to maintain security, safety, and functionality, but the attack surface of an update campaign depends on the underlying electrical/electronic architecture and on the controls assumed for update orchestration. This article reports a bounded, model-based TARA study for distributed, domain-based, and zonal vehicle architectures, which is implemented in Medini Analyze and interpreted against ISO/SAE 21434, UNECE Regulations R155 and R156, and ISO 24089 guidance. The comparison unit is not an architecture’s absolute security level. It is the architecture-conditioned instantiation of OTA trust anchors, update paths, STRIDE threat classes, evidence obligations, control-dependency assumptions, and sensitivity of model outputs under a harmonized TARA workflow. The model indicates that distributed architectures expose heterogeneous endpoints and legacy buses, and domain architectures reduce endpoint sprawl while elevating telematics and domain controllers as trust anchors. Moreover, zonal architectures can consolidate orchestration and monitoring under hardening assumptions while concentrating assurance obligations around high-performance computers, backbones, and Zone Controllers. Sensitivity checks show that raw threat counts, High/Critical counts, and severity distributions are model-granularity- and assumption-sensitive; they are therefore reported as diagnostics for traceability and evidence planning, not as real-world security rankings. The contribution is a reproducible interpretation of where OTA threat instances, trust boundaries, and regulatory evidence burdens move as vehicle E/E architectures change. Full article

Background: Splenic artery embolization (SAE) is commonly used in cirrhosis to manage hypersplenism and cytopenia. However, its longer-term clinical impact beyond hematologic parameters remains insufficiently characterized. Aim: To characterize longitudinal inpatient trajectories, clinical patterns, and follow-up features after SAE in patients with cirrhosis treated at a tertiary referral center in Kazakhstan. Methods: This retrospective single-center study included 149 patients with cirrhosis who underwent SAE. Clinical, laboratory, and imaging data were collected across sequential inpatient episodes. Outcomes included longitudinal patterns of hospitalization, laboratory trends, and baseline factors associated with a favorable clinical course. Subsequent hospitalization was defined as any inpatient episode following the index SAE admission, regardless of whether it was planned or unplanned. Results: During follow-up, 59.1% of patients had a second inpatient episode, with progressively fewer patients contributing to later observations. Liver disease severity remained largely stable, with no significant changes in Child–Pugh distribution. Portal hypertension manifestations, including varices and splenomegaly, remained highly prevalent, while recurrent variceal bleeding was relatively uncommon. Laboratory parameters demonstrated modest changes without sustained statistically significant improvement, and the number of recorded inpatient episodes decreased across sequential follow-up. Relatively stable documented follow-up trajectories were more frequently observed in patients with preserved liver function (Child–Pugh A), absence of ascites, and higher albumin levels. The most common causes of subsequent hospitalization were ascites and hepatic decompensation (38.9%) and varices without bleeding (26.2%), while documented major procedure-related complications were infrequent. Conclusions: In this retrospective single-center cohort, predominantly coil-based proximal or selective SAE demonstrated an acceptable documented major complication profile in selected patients with cirrhosis and portal hypertension. Because no untreated control group was available, the findings should be interpreted as descriptive real-world longitudinal data rather than causal evidence of reduced hospitalization burden. Full article

This paper presents a Threat Analysis and Risk Assessment (TARA) of the takeover request (TOR) component in Advanced Driver Assistance Systems (ADAS) for SAE Level 2–3 automation. A TOR prompts the human driver to retake control when the system approaches its Operational Design Domain limits or when risk increases; late, false, or muted requests directly impact safety. The study models the TOR pipeline (perception, driver monitoring, decision logic, in-vehicle networks, and Human–Machine Interface) as assets and data flows, applies STRIDE-based threat identification using Microsoft Threat Modeling Tool and Ansys Medini Analyze, and rates risks under ISO/SAE 21434 with traceability to ISO 26262, ISO 21448, and UNECE R155/R157. The assessment produces 165 threat rows, with an initial risk distribution of 1 Critical, 113 High, 34 Medium, and 17 Low. Results show that tampering, denial of service, and spoofing dominate the TOR threat landscape, with the central processing unit, sensor-to-CPU links, and HMI channels as primary trust anchors. After applying mitigation measures including secure boot, message authentication, intrusion detection, redundancy checks, and encrypted communication, the residual post-mitigation security levels were reduced to 0 Critical, 0 High, 13 Medium, 101 Low, and 51 Negligible. Unlike other ADAS TARA studies, this TOR-focused analysis shows that cybersecurity risk is shaped by the interaction between cyber compromise, driver-readiness estimation, HMI delivery, fallback execution, and the limited handover time budget. The results support a defence-in-depth mitigation strategy for secure TOR operation in SAE Level 2–3 vehicles. Full article

This study investigates class imbalance in motor fault diagnosis. Fault samples, especially those at different severity levels, are often much fewer than healthy samples. To address this issue, a self-attention guided Wasserstein conditional GAN with gradient normalization (SWGAN) is proposed. The method is based on synchronized three-phase current and vibration measurements. It separately generates label-conditioned current spectra and vibration spectra to supplement minority fault classes. Self-attention is used to capture long-range spectral dependencies. Gradient normalization is introduced to improve adversarial training stability. The generated current and vibration spectra are then fused at the feature level and fed into a stacked autoencoder (SAE)-based multi-modal classifier. Experiments were conducted on a PMSM stator fault dataset and a variable-speed three-phase asynchronous motor dataset. On the PMSM dataset, SWGAN achieved highest accuracies of 98.90% and 97.81% under two fault-category imbalance settings. On the variable-speed motor dataset, the proposed method achieved accuracies of 98.10% and 97.65%, respectively. These results show that SWGAN can provide effective supplementary samples for minority fault classes. They also indicate that the proposed method improves diagnostic performance under both fixed-speed and variable-speed conditions. Full article

Background/Objectives: HXP-GPOVac is a locally produced, inactivated Newcastle disease virus-based (NDV-HXP-S) COVID-19 vaccine manufactured in Thailand. This phase II trial compared its safety and immunogenicity with the mRNA vaccine BNT162b2 in adults aged 18–75 years. Methods: In this randomized, double-blind, active-controlled trial registered with the Thai Clinical Trials Registry (TCTR20220819003), 300 participants were assigned 3:1 to receive HXP-GPOVac or BNT162b2 on Days 1 and 29. Solicited adverse events (AEs) were recorded for 7 days after each dose, AEs were summarized through 28 days after each dose, and serious adverse events (SAEs), medically attended AEs (MAAEs), and adverse events of special interest (AESIs) were collected through Day 197. Humoral immunogenicity was assessed by pseudovirus 50% neutralization titers (NT₅₀) and anti-spike IgG concentrations at baseline, Day 29, Day 43, and Day 197. Seroconversion was defined as a ≥4-fold increase from baseline. A predefined subset underwent interferon-γ (IFN-γ) and interleukin-5 (IL-5) ELISpot assays to assess cell-mediated immune responses. The primary immunogenicity analysis assessed non-inferiority of HXP-GPOVac compared with BNT162b2 based on the NT₅₀ geometric mean titer ratio, with a prespecified non-inferiority margin of 0.5. Results: Solicited AEs were predominantly mild and occurred more frequently after the first dose in both groups; one or more solicited local or systemic AEs were reported by 23.7% (95% CI: 18.3–29.8) of HXP-GPOVac recipients and 44.7% (95% CI: 33.3–56.6) of BNT162b2 recipients after the first dose. AEs through 28 days after vaccination and SAEs were uncommon; MAAEs occurred in 17.0% of HXP-GPOVac recipients and 22.4% of BNT162b2 recipients, and none were considered related to vaccination. In the HXP-GPOVac group, NT₅₀ geometric mean titers increased from 5.6 at baseline to 65.5 at Day 29 and 505 at Day 43, declining to 63.6 at Day 197. Anti-spike IgG geometric mean concentrations rose from 7.5 BAU/mL at baseline to 102.7 BAU/mL at Day 29 and 514.6 BAU/mL at Day 43, decreasing to 61.0 BAU/mL at Day 197. BNT162b2 induced higher antibody levels at all time points. The NT₅₀ GMT ratio (HXP-GPOVac/BNT162b2) at Day 43 was 0.51 (95% CI: 0.39–0.67); the lower bound did not exceed the prespecified non-inferiority margin of 0.5, and non-inferiority was not established. Seroconversion rates at Day 43 were 97.6% for HXP-GPOVac and 97.1% for BNT162b2 (neutralizing antibody) and 98.6% and 97.1%, respectively (anti-spike IgG). ELISpot analyses demonstrated increased IFN-γ responses after the second dose without evidence of Th2-dominant skewing. Conclusions: HXP-GPOVac was well tolerated and induced substantial humoral and cellular immune responses, with high seroconversion rates and balanced T-cell polarization. Although absolute antibody levels were lower than those induced by BNT162b2 and the prespecified non-inferiority criterion was not met, these findings support continued evaluation of the inactivated NDV-HXP-S vaccine platform. Full article

Neurodegenerative diseases (NDs) pose a significant health burden globally, and this burden is increasing with an ageing population. Despite this challenge, restorative treatments for NDs remain elusive. In these conditions, the brain is vulnerable to oxidative stress and inflammation due to a deficiency or reduction in antioxidative enzymes. Oxidative stress and inflammation damage neuronal cells, leading to neurodegeneration. Various studies have explored the neuroprotective effects of flavonoids in different in vitro and animal models, primarily due to their antioxidative and anti-inflammatory properties. Crude extracts and active metabolites of Semecarpus anacardium L. have shown potential in reversing dysregulated oxidative stress and neuroinflammation. S. anacardium L. extract (SAE) and its phytocomponents, such as butein, anacardic acid, and amentoflavone, have been experimentally demonstrated to modulate oxidative stress and neuroinflammation through coordinated activation of Nrf2-mediated antioxidant pathways and suppression of NF-ĸB-driven inflammatory signaling. At a molecular level, flavonoids from SAE induce the expression of p38 MAPK and Nrf2, as well as antioxidant enzymes. Furthermore, inflammatory genes such as NF-ĸB, MAPK, AP-1, iNOS, and COX-2 are suppressed following treatment with SAE. NF-ĸB inhibition leads to neuroprotection via inhibiting the function of caspase-3 and apoptosis. Overall, this review discusses the protective role of SAE and its phytocomponents in mitigating neuronal oxidative stress, inflammation, and degeneration. Furthermore, this review highlights the translational potential of SAE and its phytocomponents as complementary therapeutic candidates for neurodegenerative disorders. However, variability in extract composition and limited pharmacokinetic characterization remain key barriers to clinical translation. Full article

Sepsis is a life-threatening syndrome driven by a dysregulated host response to infection and is frequently complicated by sepsis-associated encephalopathy (SAE), which contributes to long-term cognitive and neuropsychiatric sequelae. Despite advances in critical care, effective targeted therapies for SAE remain limited. Aquaporin-4 (AQP4), the predominant astrocytic water channel, plays a central role in cerebral water homeostasis, neuroinflammatory signaling, and blood–brain barrier integrity, suggesting its potential involvement in sepsis-induced cerebral dysfunction and neurorepair processes. Polymicrobial sepsis was induced in C57BL/6J mice using the cecal ligation and puncture (CLP) model. AQP4 activity was pharmacologically modulated through either inhibition or facilitation following sepsis induction. Disease severity was assessed using physiological parameters and a modified murine sepsis score. Neurological outcomes were evaluated through standardized behavioral tests assessing locomotor activity, motor coordination, cognitive performance, and depressive-like behavior. Neuroinflammatory and neuronal changes were examined by immunohistochemical analyses of microglial activation (Iba1), astroglial reactivity (GFAP), neuronal integrity (NeuN), and AQP4 expression. Compared with AQP4 facilitation, pharmacological inhibition of AQP4 was associated with a more favorable clinical recovery profile, reflected by lower sepsis severity scores and a more favorable body weight trajectory during the recovery phase. Behavioral analyses demonstrated preserved cognitive function, enhanced motor coordination, and reduced depressive-like behavior in AQP4 inhibitor-treated mice compared with animals receiving AQP4 facilitation. At the histological level, the inhibitor-treated group showed lower microglial and astroglial activation and better preservation of neuronal markers than the facilitator-treated group, whereas AQP4 facilitation exacerbated neuroinflammatory responses and neuronal alterations. These findings highlight a dual, context-dependent role of AQP4 in sepsis-associated cerebral dysfunction. These findings suggest that AQP4 modulation influences sepsis-associated cerebral dysfunction in a context-dependent manner. Within our experimental design, AQP4 facilitation was associated with worse outcomes, whereas AQP4 inhibition was associated with a comparatively more favorable neurobehavioral and histological profile. Full article

Seismic Shake-e is a free app that provides valuable data and tools related to earthquakes, covering the stages before, during, and after seismic events. In this text, we describe the main features of the Seismic Shake-e 2.1 (SSe) app, the considerations that guided its development, examples of its use, and the challenges for future versions. Version 1.0 of this app was awarded as one of the winners of EOVALUE: Call for Innovative Apps in environmental and social fields, a project by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. SSe recognizes two user levels: basic and intermediate/advanced. There are six modules for each level. The main topics of these modules for both user types are: (1) Accelerometer Networks (AN), (2) Seismograms Analyzer-e (SAe), (3) Seismic Design of Buildings (SDB), (4) Earthquake Preparedness (EP), (5) Earthquake Early Warning Systems (EEWS) & Tsunami Warning Systems (TWS), and (6) Earthquake Emergency Response & Recovery. The two key modules are AN and SAe: the first explains how to obtain seismic records, and the second provides tools for their analysis. We include some applications of SSe, along with their results and discussion. We also list the advantages of the main modules and discuss potential future developments and improvements. The uniqueness of this work is that we highlight the software’s essential features and demonstrate its applications. Full article