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Found 6278 publications
Noise, Vibration, and Harshness (NVH) Challenges in Hydrogen Internal Combustion Engine Vehicles
Publication Name: Energy Science and Engineering
Publication Date: 2026-02-01
Volume: 14
Issue: 2
Page Range: 1067-1080
Description:
This paper presents a state-of-the-art literature review on noise, vibration, and harshness (NVH) in hydrogen-fuelled internal combustion engines. Studies published between 2011 and 2025 were screened, covering fundamental flame physics, test-bench work, and recent prototype vehicles. The review links hydrogen's core properties—high flame speed, wide flammability, low ignition energy, strong diffusivity—to specific NVH outcomes such as rapid pressure rise, knock, back-fire, and block resonance. For each pathway we summarise measured noise levels, vibration signatures, and psycho-acoustic findings. Mitigation methods are then grouped: lean premixing, direct injection, adaptive ignition timing, exhaust tuning, and structural damping. Results show that, with these measures, hydrogen engines can approach the NVH envelope of modern gasoline units. Remaining gaps lie in long-term durability under high-frequency loading and in full-vehicle sound quality. Overall, the review clarifies current knowledge, highlights consistent trends, and points to research still needed for quiet, smooth hydrogen mobility.
Open Access: Yes
DOI: 10.1002/ese3.70400
Digital Resilience and the “Awareness Gap”: An Empirical Study of Youth Perceptions of Hate Speech Governance on Meta Platforms in Hungary
Publication Name: Journal of Cybersecurity and Privacy
Publication Date: 2026-02-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
Online hate speech poses a growing socio-technological threat that undermines democratic resilience and obstructs progress toward Sustainable Development Goal 16 (SDG 16). This study examines the regulatory and behavioral dimensions of this phenomenon through a combined legal analysis of platform governance and an empirical survey conducted on Meta platforms, based on a sample of young Hungarians (N = 301, aged 14–34). This study focuses on Hungary as a relevant case study of a Central and Eastern European (CEE) state. Countries in this region, due to their shared historical development, face similar societal challenges that are also reflected in the online sphere. The combination of high social media penetration, a highly polarized political discourse, and the tensions between platform governance and EU law (the DSA) makes the Hungarian context particularly suitable for examining digital resilience and the legal awareness of young users. The results reveal a significant “awareness gap”: While a majority of young users can intuitively identify overt hate speech, their formal understanding of platform rules is minimal. Furthermore, their sanctioning preferences often diverge from Meta’s actual policies, indicating a lack of clarity and predictability in platform governance. This gap signals a structural weakness that erodes user trust. The legal analysis highlights the limited enforceability and opacity of content moderation mechanisms, even under the Digital Services Act (DSA) framework. The empirical findings show that current self-regulation models fail to empower users with the necessary knowledge. The contribution of this study is to empirically identify and critically reframe this ‘awareness gap’. Moving beyond a simple knowledge deficit, we argue that the gap is a symptom of a deeper legitimacy crisis in platform governance. It reflects a rational user response—manifesting as digital resignation—to opaque, commercially driven, and unaccountable moderation systems. By integrating legal and behavioral insights with critical platform studies, this paper argues that achieving SDG 16 requires a dual strategy: (1) fundamentally increasing transparency and accountability in content governance to rebuild user trust, and (2) enhancing user-centered digital and legal literacy through a shared responsibility model. Such a strategy must involve both public and private actors in a coordinated, rights-based approach. Ultimately, this study calls for policy frameworks that strengthen democratic resilience not only through better regulation, but by empowering citizens to become active participants—rather than passive subjects—in the governance of online spaces.
Open Access: Yes
DOI: 10.3390/jcp6010003
Analyzing On-Board Vehicle Data to Support Sustainable Transport
Publication Name: Future Transportation
Publication Date: 2026-02-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
Energy-efficient driving is essential for reducing the environmental impacts of road transport, especially for electric passenger vehicles. This research aims to build a data-driven behavioral analysis and energy-consumption evaluation model. The model relies on sensor data from the vehicle’s on-board communication network, primarily the CAN (Controller Area Network) bus. We analyze patterns of key powertrain and battery parameters—such as current, voltage, state of charge (SoC), and power—in relation to driver inputs, such as the accelerator pedal position. In the first stage, we review the literature with a focus on machine learning and clustering methods used in behavioral and energy analysis. We also examine the role of on-board telemetry systems. Next, we develop a controlled measurement architecture. It defines reference consumption maps from dynamometer data across operating points and environmental variables, including SoC, temperature, and load. The longer-term goal is a multidimensional behavioral map and profiling framework that can predict energy efficiency from real-time driver inputs. This work lays the foundation for a future system with adaptive, feedback-based driver support. Such a system can promote intelligent, sustainable, and behavior-oriented mobility solutions.
Open Access: Yes
From scroll to sale: Predicting of customer engagement in impulse buying cycle on TikTok through the S-O-R model
Publication Name: Acta Psychologica
Publication Date: 2026-02-01
Volume: 262
Issue: Unknown
Page Range: Unknown
Description:
The popularity of TikTok is evident on a global scale, leading to its adoption by numerous companies as a promotional platform. The Stimulus - Organization - Reaction model has been validated in its capacity to describe the purchase process. The present study investigates the Hungarian manifestations of the factors of this model to develop a forecasting method that will allow for prediction of the success of TikTok impulse purchase marketing strategies. The prediction method under analysis is based on factor and regression analyses and validation. The exploratory part utilizes a questionnaire survey of 283 Hungarian university students. The validity of the results is measured on a different university sample of 104 active users of TikTok and users of alternative social media platforms. The findings indicate that the factors comprising the impulse purchase cycle involvement include the urge to buy impulsively, utilitarian value, perceived similarity, and co-promotion. The validation process revealed that the rates of agreement with the predictions among TikTok users exceeded 90 %, while the rate among users of other social media platforms reached over 80 %. The principal strength of this study lies in its quantification and transformation of questionnaire data into a pivotal key performance indicator suitable for companies.
Open Access: Yes
Constraint-Aware and Economic Optimization of Riverbank Retaining Walls Using Metaheuristic Algorithms
Publication Name: Water Switzerland
Publication Date: 2026-02-01
Volume: 18
Issue: 3
Page Range: Unknown
Description:
The optimal design of riverbank retaining walls requires a careful balance between structural safety, constructability, and economic efficiency. In this study, a constraint-aware optimization framework is developed for the design of concrete gravity retaining walls by explicitly incorporating stability, serviceability, and geometric feasibility constraints. Several metaheuristic algorithms are comparatively evaluated under identical computational conditions using 30 independent runs, a population size of 50, and 1000 iterations. The results demonstrate that enforcing geometric constraints is essential to prevent non-physical designs and to ensure engineering realism. Quantitative analysis shows that the Flower Fertilization Optimization (FFO) algorithm yields the minimum wall weight, reducing material usage by approximately 19% compared to more conservative solutions. In contrast, the adaptive exploration artificial bee colony (AEABC) algorithm exhibits the most robust and repeatable convergence behavior with low statistical dispersion across independent runs. An economic assessment based on concrete volume further confirms the direct impact of material efficiency on construction cost. The proposed framework highlights the importance of constraint-aware optimization for achieving reliable and economically efficient retaining wall designs.
Open Access: Yes
DOI: 10.3390/w18030434
Passive Occupant Safety Solutions for Non-Conventional Seating Positions
Publication Name: Future Transportation
Publication Date: 2026-02-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
In a fully autonomous vehicle, the driver becomes a passenger, free to adopt different seating positions. This change challenges traditional passive safety systems—such as seatbelts, airbags and seat design—that are optimised for a forward-facing position. As autonomous vehicles are integrated into mixed traffic with conventional cars, solutions need to address these challenges. In this intermediate stage, fully autonomous cars will need a system that, in the event of an accident, can rotate the seats to the most ideal position tested by the manufacturer. This could be a number of positions where the seat, airbags and seatbelts are optimised, taking into account the expected direction of impact. It is important that the rotation is not too radical, as this would increase the risk of injury. In addition, the seat dimensions need to be increased to improve energy absorption in the event of a collision, thereby reducing the impact forces on the occupants and improving overall safety. To improve passive protection, airbags will continue to be used in the future, but in completely new positions, sizes and shapes. This research aims to identify potential passive occupant safety solutions for seat positions that have been rotated in fully autonomous vehicles. The finite element simulation model on which the results in this article are based was developed in an earlier phase of the research. The current research combines two previously conducted research directions, using the modified seat and the developed airbag concept. This research’s main outcome is a system that effectively protects occupants in rotated seat positions. It maintains all evaluated injury criteria below their threshold limits and ensures controlled occupant kinematics.
Open Access: Yes
A Hybrid Intrusion Detection Framework Using Deep Autoencoder and Machine Learning Models
Publication Name: AI Switzerland
Publication Date: 2026-02-01
Volume: 7
Issue: 2
Page Range: Unknown
Description:
This study provides a detailed comparative analysis of a three-hybrid intrusion detection method aimed at strengthening network security through precise and adaptive threat identification. The proposed framework integrates an Autoencoder-Gaussian Mixture Model (AE-GMM) with two supervised learning techniques, XGBoost and Logistic Regression, combining deep feature extraction with interpretability and stable generalization. Although the downstream classifiers are trained in a supervised manner, the hybrid intrusion detection nature of the framework is preserved through unsupervised representation learning and probabilistic modeling in the AE-GMM stage. Two benchmark datasets were used for evaluation: NSL-KDD, representing traditional network behavior, and UNSW-NB15, reflecting modern and diverse traffic patterns. A consistent preprocessing pipeline was applied, including normalization, feature selection, and dimensionality reduction, to ensure fair comparison and efficient training. The experimental findings show that hybridizing deep learning with gradient-boosted and linear classifiers markedly enhances detection performance and resilience. The AE–GMM-XGBoost model achieved superior outcomes, reaching an F1-score above 0.94 ± 0.0021 and an AUC greater than 0.97 on both datasets, demonstrating high accuracy in distinguishing legitimate and malicious traffic. AE-GMM-Logistic Regression also achieved strong and balanced performance, recording an F1-score exceeding 0.91 ± 0.0020 with stable generalization across test conditions. Conversely, the standalone AE-GMM effectively captured deep latent patterns but exhibited lower recall, indicating limited sensitivity to subtle or emerging attacks. These results collectively confirm that integrating autoencoder-based representation learning with advanced supervised models significantly improves intrusion detection in complex network settings. The proposed framework therefore provides a solid and extensible basis for future research in explainable and federated intrusion detection, supporting the development of adaptive and proactive cybersecurity defenses.
Open Access: Yes
DOI: 10.3390/ai7020039
Complex Test Scenarios for Functional Validation Prior to Type Approval
Publication Name: Future Transportation
Publication Date: 2026-02-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
The continuous tightening of European regulatory requirements, particularly under the General Safety Regulation (GSR), has considerably increased the scope and cost of proving ground testing required for the validation of Advanced Driver Assistance Systems (ADASs) and Automated Driving Systems (ADSs). This study presents a methodology for constructing complex proving ground test scenarios aimed at supporting early-stage functional validation and cost-efficient preparation for type approval. The method is based on the systematic analysis of proving ground–relevant ADAS regulations and the classification of test case variations according to sensing, actuation, and execution complexity. By filtering and combining representative test cases, minimum and maximum complexity scenarios were developed and evaluated on the ZalaZONE proving ground in Hungary. The results demonstrate that the proposed approach can substantially reduce test duration, facility occupancy, and overall validation costs, while maintaining the representativeness and credibility of results. Beyond cost savings, the methodology offers a scalable and practical framework for physical validation, supporting manufacturers in achieving regulatory compliance with reduced time and expenditure.
Open Access: Yes
Experimental investigation and finite element analysis of varying bitumen content in asphalt mixtures
Publication Name: Discover Applied Sciences
Publication Date: 2026-02-01
Volume: 8
Issue: 2
Page Range: Unknown
Description:
The percentage of bitumen in asphalt mixtures plays a crucial role in determining pavement performance throughout its service life. This study investigates the effect of varying bitumen contents on the mechanical behaviour and durability of asphalt mixtures. Three mixtures containing 4.7%, 5.1%, and 5.5% bitumen binder were evaluated through a comprehensive set of laboratory tests, including Marshall stability and flow, semi-circular bending, pressure aging vessel, wheel rutting, dynamic modulus, creep compliance, and fatigue performance tests, supported by finite element modeling. The nonlinear plastic behaviour and damage evolution were analyzed using the Perzyna-type viscoplastic model and Lemaitre’s isotropic damage model. Results indicate that mixtures with lower bitumen content (4.7%) exhibit earlier fatigue damage, while higher bitumen content (5.5%) leads to increased rutting and creep compliance. The 5.1% bitumen mixture demonstrated the most balanced performance, showing 40% less induced plastic strain damage than the 4.7% mixture and 27% less than the 5.5% mixture.
Open Access: Yes
