Search Results (323)

Background: Although there is a considerable body of research evidence on active school travel (AST), the number of children walking to school has decreased over the years in Australia, as well as around the world. Different factors influence AST; the most cited in Melbourne is traffic safety perception. Traffic safety perception is influenced by built environment elements, and improving the built environment can enhance parental perception of traffic safety. Studies have shown that lateral separation from traffic and the provision of a buffer improve the perception of traffic safety, and this has to be explored for children walking to school based on the existing ground situation on a typical street near a school in Melbourne. Based on this background, a pilot study was carried out before the main study to test the reliability and validity of the survey questionnaire. Methods: The survey instrument was developed based on perceptions and/or AST studies, and included safety and probability aspects of the perception construct to elicit responses on perception. The perception of traffic safety was to be rated based on the streetscape videos embedded in the survey. The reliability was tested using Cronbach’s alpha and validity was explored through exploratory factor analysis. The study also checked the feasibility of the recruitment method and whether there would be an observable outcome from the study. The participants were recruited online through community Facebook groups. Results: The survey instrument had excellent reliability (α = 0.945) and was valid. The recruitment method through a Facebook community group was apt for recruiting participants. The preliminary analysis of the pilot data revealed a difference in perception ratings based on the streetscape element intervention. Conclusions: The survey instrument can be used for similar AST research, as it proved to be reliable and valid. Full article

LACMA’s 19th-century Tibetan Altar Table with Auspicious Symbols is characterized by a complex stratigraphy comprising animal glue-based ground and paint layers, a presumably original tung oil-based varnish, and a dark surface layer composed of a complex mixture of paraffinic wax, shellac, and rapeseed oil, which obscures the object’s original decorative scheme. This study examines the use of nanostructured fluids and organic solvents confined within hydrogels and organogels for the selective removal of the dark surface layer while preserving the underlying paint and varnish. Following the analysis of the artwork’s constituent materials, cleaning tests were conducted and evaluated using visible and ultraviolet fluorescence (UVF) imaging, spectrophotometry, and digital microscopy. The homogeneous absorption of solvent mixtures by the organogels was assessed through gas chromatography–mass spectrometry (GC–MS). Results indicate that confining cleaning fluids within the gels’ porous networks significantly improved solvent retention and control of fluid release. While conventional cleaning methods proved insufficiently selective, the gradual release of a nanostructured fluid containing a small amount of benzyl alcohol, combined with the nanostructural properties of the poly(vinyl alcohol)–sebacic acid (PSA2) hydrogel, enabled targeted removal of the surface layer while preserving the integrity of the underlying layers. Full article

Unmanned aerial vehicles (UAVs) are regarded as a novel mode for urban air mobility, earning increasing attention on many commercial and civil applications. The risk of UAVs to people on the ground is heightened by airspace range and operational risks, and the quantitative ground risk buffer estimation are highly required to protect the people on the ground. In this work, a ground risk buffer estimation method based on the analysis of the UAVs dynamics is proposed. It is a 3D contour map, incorporated with flight test parameters, to determine the ground risk buffer for both, rotorcraft UAVs and fixed-wing UAVs. The contour map is generated through UAVs dynamics analysis and combines several parameter layers, including altitude and speed at moment of failure occurence, environment conditions and the lift-to-drag ratio. Each location of the map has associated a value that quantifies the area of the ground risk buffer for a specific test flight condition. The ground risk buffer determined by the current Specific Operations Risk Assessment framework using the 1-to-1 principle is provided for comparison. The proposed method exhibits greater safety margin and further proves the potential of the new estimation method in the perspective of risk quantification and practical engineering applications. Full article

This paper presents an experimental study of the Automatic Emergency Braking (AEB) system, focusing on three essential testing phases: verifying the match between calculated and actual brake actuator operation time, validating the forecasted vs. real-time stabilized deceleration onset duration, and comparing the theoretically computed braking distance derived from mathematical models with its actual measurement. Standard instrumentation coupled with an original test procedure was utilized during the experiments. A full-scale experimental campaign was conducted on a specialized proving ground, thus substantiating the validity and robustness of the computational models used for assessing the AEB system parameters. The empirical outcomes confirmed that current-generation AEB systems offer dependable predictions regarding braking dynamics and exhibit prompt responsiveness to imminent collisions. However, it should be noted that variations in road conditions, driver behavior, and sensor precision may affect their performance. Consequently, additional efforts aimed at optimizing existing AEB solutions are required to minimize potential errors and enhance overall reliability. Finally, the significance of complying with design specifications and continuously upgrading AEB systems to meet evolving road safety standards is emphasized. Full article

This study presents a real-time capable methodology for quantifying the hydroplaning risk of a passenger car tire using data from an intelligent tire system. An analytical water lift force formulation is applied to convert measured peak lift force values into longitudinal water velocity. Based on the water velocity and groove dimensions, the intake flow rate that the tire must evacuate is estimated. Hydroplaning risk is then defined as the ratio between the intake flow rate and the maximum flow capacity of the tire before total hydroplaning occurs. Experimental investigations under real-world conditions were carried out at 45 mph and 65 mph, yielding average hydroplaning risk values of 12.6% and 21.3%. The proposed model was validated by performing hydroplaning tests under a controlled water depth of 1 mm at Michelin Laurens Proving Grounds. The hydroplaning risk values computed by the intelligent tire system were compared with reference data from the literature obtained under similar test conditions. Additionally, the critical hydroplaning speed of the test tire was estimated and compared against predictions from established numerical models, such as those proposed by Gengenbach and Spitzhüttl. The methodology is confirmed as a reliable algorithm for real-time hydroplaning risk monitoring with the potential to improve vehicle safety. Full article

This paper evaluates Advanced Driver Assistance Systems (ADASs) in test scenarios derived from naturalistic driving and crash data, mapped to ISO 26262, ISO/PAS 21448 (SOTIF), and ISO 34502. From eight high-risk scenarios, it is validated for left turns across oncoming traffic on a proving ground using a Škoda Superb iV against a soft Global Vehicle Target. ODD and spatiotemporal thresholds are parameterized and speed/acceleration profiles from GNSS/IMU data are analyzed. AEB and FCW performance varies across nominally identical runs, driven by human-in-the-loop variability and target detectability. In successful interventions, peak deceleration reached −0.64 g, meeting UNECE R152 criteria; in other runs, late detection narrowed TTC below intervention thresholds, leading to contact. Limitations in current protocols are identified and argue for scenario catalogs with realistic context (weather, surface, masking) and latency-aware metrics. The results motivate extending validation beyond standard tracks toward mixed methods linking simulation, scenario databases, and instrumented field trials. Full article

This work reports the results of an on-ground experimental test campaign performed in the relevant environment (i.e., inside a vacuum chamber) of a blowdown H₂O₂ monopropellant propulsion system designed for CubeSat applications for the assessment of its propulsive performance and its thermomechanical behavior, both in continuous and pulse modes. The complete experimental characterization of the most important propulsive parameters of the engineering model of the propulsion system has been carried out with a suitably designed diagnostic equipment, consisting of a thrust balance capable of hosting an entire 3U CubeSat and all the relevant sensors. The propulsion system proved to match most of the requirements, both in pulse and continuous mode operations. Full article

This paper reviews the impact of various types of AI education on the sustainability attitudes of Generation Z in Istanbul in the context of the new model of Hospitality 5.0. The study concentrates on three major aspects of learning related to AI, namely knowledge, practical implementation, and value-based orientation, and their interim impact on shaping sustainability-oriented perceptions of young people. The study established the reliability and stability of the constructs using psychometric testing, and factor analysis and structural modeling proved that every educational dimension of AI is positively related to sustainability attitudes. Of them, knowledge utilization proved to be the most powerful predictor. Further residual results revealed behavioral anomalies by demonstrating that those who outperformed were people with moderate technical abilities and high sustainability values and those who underperformed possessed high digital abilities without integrity value-based alignment. These results demonstrate that formal AI education is a stabilizing force, which encourages more consistent and sustainability-focused attitudes. Altogether, the findings point to the significance of educational models combining technical skills with ethical and environmental consciousness in helping Generation Z to find a middle ground in terms of sustainable change in the digital hospitality industry. Full article

To address the problems of low inversion accuracy and poor noise resistance in pulsed eddy current (PEC) grounding grid depth detection, this study proposes a novel inversion model (IE-CBiLSTM). This model integrates the Informer Encoder with the CNN-BiLSTM for the first time to detect the depth of the PEC grounding grid and conducts experimental verification based on an independently designed pulsed eddy current detection device and a dedicated coil sensor. The model design employs a two-dimensional convolutional neural network (CNN) to extract local spatial features, combines a bidirectional long short-term memory network (Bi-LSTM) to model temporal dependencies, and introduces a multi-head attention mechanism along with the Informer structure to enhance the expression of key features. In terms of data construction, the design integrates both forward simulation data and measured data to improve the model’s generalization capability. Experimental validation includes self-burial experiments and field tests at a substation. In the self-burial test, the IE-CBiLSTM inversion results show high consistency with actual burial depths under various conditions (1.0 m, 1.2 m, and 1.5 m), significantly outperforming other optimization algorithms, achieving a coefficient of determination (R²) of 0.861, along with root mean square error (E_RMS) and mean relative error (E_MR) values of 17.54 Ω·m and 0.061 Ω·m, respectively. In the field test, the inversion results also closely match the design depths from engineering drawings, with an R² of 0.933, E_RMS of 11.30 Ω·m, and E_MR of 0.046 Ω·m. These results are significantly better than those obtained using traditional Occam and LSTM methods. At the same time, based on the inversion results, a three-dimensional inversion map of the grounding grid and a buried depth profile were drawn, and the spatial direction and buried depth distribution of the underground flat steel were clearly displayed, proving the visualization ability of the model and its engineering practicality under complex working conditions. This method provides an efficient and reliable inversion strategy for deep PEC nondestructive testing of grounding grid laying. Full article

(1) Background: Climate change has intensified extreme heat and localized rainfall, exposing South Korea’s protected trees to new risks. Despite their ecological and cultural value, prior research has been largely local or qualitative, leaving little basis for nationwide prioritization. (2) Methods: We developed a composite risk index that integrates heat and rainfall exposure with species sensitivities, covering nearly the entire national inventory (≈10,000 individuals). Risks were calculated at the tree level, aggregated to district, provincial, and national scales, and tested for robustness across weighting and normalization choices. Spatial clustering was assessed with Moran’s I and LISA. (3) Results: High-risk clusters were consistently identified in southern and southwestern regions. Mean and tail indicators showed that average-based approaches obscure extreme vulnerabilities, while LISA confirmed significant High–High clusters. Rankings proved robust across scenarios, indicating that results reflect structural signals rather than parameter settings. Priority areas defined by the presence of extreme-risk individuals emerged as stable candidates for intervention. (4) Conclusions: The study establishes a transparent, operational rule for prioritization and offers tailored strategies—such as drainage infrastructure, shading, and root-zone management—while informing medium-term planning. It provides the first nationwide, empirically grounded framework for conserving protected trees under climate transition. Full article

The study estimated the shelf life of specialty coffee packaged in six types of packaging (Tocuyo bag (TB), Double-bilaminate foil and aluminuim bag (DFAB), Ecotac vacuum bag (EV), Pressed cardboard box (PCB), Double-laminated bag without valve, with opening and zipper (DBOZ), Double-laminated bag with degassing valve and zipper (DBDVZ) and Triple-laminated bag with degassing valve and zipper (TBDVZ)). The estimation of shelf life was conducted by means of cup scores provided by six coffee tasters for coffee stored at 40, 50, and 60 °C. The Arrhenius equation was employed to obtain accelerated models for predicting shelf life. It was determined that green coffee beans are most effectively preserved in DBOZ, maintaining their freshness for a period of up to 55.13 days. The second-best option was EV, which has a shelf life of up to 35.21 days. The sole packaging alternative that was subjected to testing for roasted coffee beans was found to allow for their preservation for a period of up to 32 days. However, for roasted and ground coffee, of the four alternatives evaluated, the TBDVZ proved to be the optimal alternative, at 12.18 days. However, the other alternatives (DBOZ and DBDVZ) allow for very similar storage times, at 11.99 and 11.48 days, respectively. PCB does not appear to be a viable packaging alternative for roasted and ground coffee (7.85 days). Finally, we found that coffee stored in DFAB and aluminum bags at 20 °C has been shown to retain its quality for up to 250 days. Furthermore, if the temperature is reduced to 10 °C, the coffee’s shelf life is extended to more than 600 days. The insights derived from this research are of significant value to industry stakeholders, consumers, and developers of specialty coffee packaging. Full article

The frequency content of strong ground motion significantly affects the response of engineered systems under seismic excitation. Among some scalar parameters which exist in the literature, the mean period T_m has proved to be the most efficient. Ground Motion Predictive Equations (GMPEs) are usually developed for ground motion parameters through the calibration of coefficients of predefined functional forms, via linear or nonlinear regression, and based on recorded ground motion data. Such expressions of T_m are rare in the literature. Recently, the use of machine learning (ML) algorithms in earthquake engineering and engineering seismology has increased. The Artificial Neural Network (ANN) is an effective ML algorithm which has already been explored for the development of GMPEs for amplitude-based ground motion parameters. Within the work presented herein, multiple nonlinear regression (NLR)- and ANN-based GMPEs are developed for T_m using the latest strong motion database for shallow earthquakes in Greece. To the author’s knowledge, the implementation of ANN for producing GMPEs for T_m for shallow earthquake events has not been explored. Direct comparison between the NLR- and ANN-based GMPEs is performed, in terms of performance indexes, aleatory uncertainty, and working examples, as well as testing against earthquake events not included in the original dataset. The results reveal that the ANN-based GMPEs are useful in reducing aleatory uncertainty, although care should be taken in their implementation to avoid overfitting issues. Full article

To achieve professional roe deer population management and to mitigate wildlife-related agricultural damage, a wildlife population estimation trial was conducted in Hungary using an ultralight aircraft with dual sensors (thermal and DSLR camera) to assess the method’s applicability, using the roe deer as a model species. The test took place in early spring, at an altitude of 400 m above ground level and a flight speed of 150 km/h. The survey targeted a total count of a 1040 hectare area using adjacent 200 m-wide strips. This strip-based design also allowed for a methodological comparison between total count and strip sample count approaches. Object-based image classification was applied, and species-level validation was performed. During the survey, a total of 213 roe deer were localised. The average group size was 9.17 ± 1.7 ($\overline{\mathrm{x}}$ ± SE), with two prominent outliers (28 and 34 individuals). Compared to the density value of 0.205 individuals/ha established through the full-area census, the simulated estimations (50% and 25%) showed considerable under- and overestimation, primarily due to the aggregative behaviour of roe deer. Based on the test, aerial population estimation using dual-sensor technology proved to be effective in agricultural habitats; however, the accuracy of the results is strongly influenced by the sampling design applied. Full article

Undetected cracks in railroad wheels pose significant safety and economic risks, while current inspection methods are limited by cost, coverage, or contact requirements. This study explores the use of passive, air-coupled ultrasonic acoustic (UA) sensors for detecting wheel damage on stationary or moving wheels. Two controlled datasets of wheelsets, one with clear damage and another with early, service-induced defects, were tested using hammer impacts. An automated system identified high-energy bursts and extracted features in both time and frequency domains, such as decay rate, spectral centroid, and entropy. The results demonstrate the effectiveness of UAE (ultrasonic acoustic emission) techniques through Kernel Density Estimation (KDE) visualization, hypothesis testing with effect sizes, and Receiver Operating Characteristic (ROC) analysis. The decay rate consistently proved to be the most effective discriminator, achieving near-perfect classification of severely damaged wheels and maintaining meaningful separation for early defects. Spectral features provided additional information but were less decisive. The frequency spectrum characteristics were effective across both axial and radial sensor orientations, with ultrasonic frequencies (20–80 kHz) offering higher spectral fidelity than sonic frequencies (1–20 kHz). This work establishes a validated “ground-truth” signature essential for developing a practical wayside detection system. The findings guide a targeted engineering approach to physically isolate this known signature from ambient noise and develop advanced models for reliable in-motion detection. Full article

In recent years, graphitic carbon nitride (g-C₃N₄) has gained considerable ground in the field of heterogeneous photocatalysis for the abatement of emerging contaminants from aqueous environments. Nonetheless, certain limitations, including a small surface area and a high recombination rate, limit its photocatalytic efficacy. In this study, g-C₃N₄ was synthesized from urea and then underwent thermal exfoliation. A portion of the exfoliated material was subsequently subjected to protonation via acid treatment, and both protonated and non-protonated variants of exfoliated g-C₃N₄ were combined with small amounts of Ti₃C₂T_x MXene. The morphology, chemical structure, and optical properties of the synthesized materials were examined using various characterization techniques. Additionally, their photocatalytic performance was evaluated through laboratory tests using the commonly detected anti-hypertensive drug valsartan as a model pollutant. The degradation kinetics of valsartan revealed that combining 1% Ti₃C₂T_x MXene with exfoliated g-C₃N₄ (both protonated and non-protonated) achieves optimal removal. Notably, the composite material 1%-pCNMX (protonated variant) displayed a 20% higher removal kinetic rate than unmodified exfoliated g-C₃N₄, removing a higher quantity of valsartan within the same time frame. Furthermore, all protonated composites proved more effective in degrading valsartan than their non-protonated counterparts, demonstrating the positive impact of acid treatment. The improved photocatalytic activity was attributed to the successful formation of Schottky junctions between g-C₃N₄ and Ti₃C₂T_x, which reduced the recombination rate of photogenerated charge carriers. Full article