Search Results (3,699)

The Yellow River Great Bend Urban Agglomeration is a key area in the ecological protection and high-quality development strategy of the Yellow River Basin. In the process of coordinated regional development, the contradiction between economic development and environmental protection has become increasingly prominent, and the pollution problems of PM_2.5 and O₃ have become prominent. Based on the observation data of air pollutants and meteorological data of 15 cities from 2020 to 2023, this study explored the spatio-temporal variation characteristics of PM_2.5 and O₃ concentrations in this region and the influence of meteorological factors (temperature, relative humidity, wind speed, and precipitation). The results showed that the proportion of days with good air quality in the Yellow River Great Bend Urban Agglomeration metropolitan area increased first and then decreased from 2020 to 2023. PM_2.5 concentrations were highest in winter and lowest in summer, with moderate levels in spring and autumn. In contrast, O₃ concentrations peaked in summer and reached their lowest levels in winter. In terms of spatial variation, the spatial distribution of the number of PM_2.5 polluted days roughly decreases from northwest to southeast, with Taiyuan City having the largest number of polluted days. The number of days with O₃ pollution roughly shows a pattern of more in the middle and less around the periphery. Spatial autocorrelation analysis indicates that the PM_2.5 concentration and O₃ concentration in the Yellow River Great Bend Urban Agglomeration have obvious high-value and low-value spatial agglomeration characteristics. Meteorological elements have a significant influence on the concentrations of PM_2.5 and O₃. The occurrence frequencies of PM_2.5 pollution and O₃ pollution were significantly higher respectively within the temperature ranges of −10 to 15 °C and 20 to 30 °C, as well as under the condition of RH > 50% and in the range of 30% to 70% of the relative humidity. Statistical analysis revealed a universally significant negative correlation between wind speed and PM_2.5 concentrations across all cities (mean R = −0.09, binomial test p < 0.001), confirming the critical role of stagnant conditions in local pollutant accumulation. The results of this study can provide important references for regional precise pollution control and environmental quality improvement and are of great significance for promoting regional sustainable development. Full article

Low-level jets (LLJs) are important mesoscale features in the Arctic and are highly relevant for the atmospheric transport of heat, moisture, and air pollutants, as well as for wind energy and aircraft operations. In this paper, LLJs at the Tiksi observatory in the Laptev Sea region are investigated during the period 2014–2020 using simulations performed with the regional climate model CCLM with a 5 km resolution. The main synoptic weather patterns for LLJs at Tiksi were identified using a self-organizing map (SOM) analysis. LLJs occurred in about 55% of all profiles with an average height of about 400 m and an average speed of about 13 m/s. About 60% of the LLJs had core speeds larger than 10 m/s (strong jets). The occurrence frequency for all jets showed a pronounced seasonal cycle with more and stronger LLJs during winter. The turbulent kinetic energy in the lower ABL was four times as large for LLJs than for situations without LLJs, which underlines the impact of LLJs on turbulent processes in the ABL. The mean duration of LLJ events (duration of at least 6 h) was almost 24 h and the 90th percentile was about two days. About 70% of the LLJ events were associated with downslope winds of the local mountain ridge and had a longer duration of about three days for the 90th percentile. Full article

The harvesting of Camellia oleifera fruit in hilly areas faces core problems such as low manual efficiency, poor terrain adaptability of existing machinery, and severe emissions and noise from traditional equipment. This study designed a crawler chassis utilizing a permanent magnet synchronous motor-driven hydraulic system. The research integrated kinematic modeling and resistance calculations for parameter matching, followed by AMESim dynamic simulations and motor calibration experiments. Finally, comprehensive field tests were conducted to evaluate the prototype. The results indicate the chassis achieves a maximum travel speed >1.5 m∙s⁻¹, a climbing angle of 41.4°, and a turning radius of 0.72 m, with noise levels strictly below 80 dB(A). Significantly, dynamic power characteristic tests under actual vibration harvesting conditions revealed that the 45 kW motor maintains a rapid response with ample power reserve. The input power exhibited a distinct square-wave pattern synchronized with hydraulic valve commands, peaking at 18.1 kW during vibration bursts. These findings confirm the system’s stability under coupled driving and harvesting loads. This design offers a viable, low-noise solution for electrifying and intelligently upgrading Camellia oleifera harvesting equipment in complex terrains. Full article

As the new energy strategy progresses, desert, Gobi, and wasteland areas have become key areas for photovoltaic (PV) development, inevitably bringing new environmental challenges. Although PV power stations act as obstacles with some wind and sand control effects, aeolian erosion remains a problem, especially in localized areas where erosion intensifies. To address this issue, this study uses the PV power station layout in the semi-arid wind and sand region of Yudaokou, Hebei, as a case study. Using computational fluid dynamics (CFD) numerical simulations, a combined layout of PV panels and sand barriers is proposed. It is first assumed that this combined layout improves wind protection compared to photovoltaic arrays. The impact of different sand barrier configurations on the airflow field is analyzed to explore their role in controlling aeolian erosion. By analyzing the airflow field, areas of intensified and potentially intensified aeolian erosion are identified. Based on this, sand barriers are strategically placed in key protective zones on the windward side of the PV array, and the combined layout of PV panels and sand barriers is optimized to improve aeolian erosion control effectiveness and promote the sustainable development of PV power stations. The results indicate that PV panels significantly reduce wind speed by altering local airflow and flow patterns, with the impact primarily concentrated in the first 3 to 4 rows on the windward side of the PV array. By establishing sand barriers beneath the PV panels on the windward side, aeolian erosion can be effectively reduced, with the effect on the airflow field primarily occurring within the 0–0.3 m height above the ground. Continuously establishing sand barriers up to the third row of PV panels effectively reduces wind speed, with further extension not significantly improving wind protection, indicating that the third row of PV panels serves as the critical point for sand barrier establishment. This configuration provides the ideal layout for achieving effective protection and offers theoretical and practical guidance for improving the layout of combined PV power stations. Comprehensive analysis suggests that the optimized configuration of PV arrays and sand barrier layout effectively controls aeolian erosion, with the Model 3, which places sand barriers up to the third row of PV panels, ensuring efficient resource utilization. This study offers a practical approach to reducing damage from wind and sand by optimizing the layout of sand barriers and PV panels, thereby providing important guidance for the sustainable development of PV power stations in arid areas. Full article

Real-time detection of gait phase is a critical challenge for closed-loop neuromodulation systems aimed at restoring locomotion after spinal cord injury (SCI). However, many existing gait analysis approaches rely on offline processing or computationally intensive models that are unsuitable for low-latency, embedded deployment. In this study, we present a hybrid AI-based sensing architecture that enables real-time kinematic extraction and on-device gait-phase classification for closed-loop neuromodulation in SCI mice. A vision AI module performs marker-assisted, high-speed pose estimation to extract hindlimb joint angles during treadmill locomotion, while a lightweight edge AI model deployed on a microcontroller classifies gait phase and generates real-time phase-dependent stimulation triggers for closed-loop neuromodulation. The integrated system generalized to unseen SCI gait patterns without injury-specific retraining and enabled precise phase-locked biphasic stimulation in a bench-top closed-loop evaluation. This work demonstrates a low-latency, attachment-free sensing and control framework for gait-responsive neuromodulation, supporting future translation to wearable or implantable closed-loop neurorehabilitation systems. Full article

The vehicular energy consumption, primarily determined by the vehicle’s characteristics, exhibits significant variations influenced by driving behavior, traffic, and road attributes, with repercussions for emissions. This paper presents experimental results from real-traffic runs to characterize the relationship between fuel consumption and these factors. Data on consumption, performance, and kinematics of a light-duty vehicle were obtained using low-cost devices, including an On-Board Diagnostics (OBD) scanner, a unit integrating an Inertial Measurement Unit (IMU) and a Global Positioning System (GPS) receiver. The data allowed distinguishing consumption patterns between two distinct scenarios: a collector road stretch with deteriorated pavement and an express road stretch with lower surface roughness. Relevant association was identified between fuel consumption and factors such as discrete pavement anomalies and variables related to driving and traffic. Moderate correlations were observed with slope, and weaker ones with pavement roughness. Regarding the regression analysis, results identified acceleration and engine speed as the primary operational determinants of fuel consumption, with road grade emerging as the dominant geometric constraint across all scenarios. The results reveal relevant associations between fuel consumption and road, driving, and traffic-related factors while simultaneously demonstrating a robust and replicable experimental methodology based on commercially available sensing devices for real-traffic energy and emission assessments. Full article

Ultrafast laser processing is constrained by an inherent throughput–resolution trade-off, typically addressed either by high-speed single-beam scanning or by parallel processing approaches. Here, a scanning-based dynamic mask projection concept is presented, combining both strategies by integrating a digital micromirror device (DMD) for dynamic binary amplitude mask generation with galvanometric scanning for high-speed lateral repositioning of the projected pattern. A high-numerical-aperture microscope objective is used to project the mask for thin film laser ablation with sub-micrometer feature sizes, while scanning extends the processing area beyond a single projected pattern, ultimately limited by the objective’s field of view. The concept is demonstrated by selective single-pulse pattern ablation of 10 nm thick tantalum nitride (TaN) thin films on glass substrates using 230 fs pulses at a center wavelength of 515 nm. The optical system enables a 770 nm minimum feature size across a scan field with an area-equivalent circular diameter of 550 µm. Dynamic mask projection combined with fast scanning offers a scalable route to high-throughput laser nanoprocessing and is relevant to fabrication and processing of nanomaterials, digital mask lithography, and micro- and nanomachining. Full article

Interchange merging areas are critical safety hotspots in urban road networks, where complex vehicle interactions challenge traffic safety and efficiency. Improving safety performance at these locations is essential for developing sustainable, resilient, and intelligent urban transportation systems. To overcome the limitations of single-driver simulators, this study developed a multi-driver simulation platform based on Unity3D (Version 2022.3.1f1c1), enabling real-time interaction among multiple human drivers. High-resolution trajectory data were collected from 231 valid interaction events. An eight-direction relative position model was employed to classify behaviors into four patterns: longitudinal, lateral, front cut-in, and rear cut-in. Risk was quantified using time-exposed and time-integrated Anticipated Collision Time metrics, with events subsequently clustered into low (n = 138), medium (n = 67), and high-risk (n = 26) categories. An ordered logit regression model identified key risk factors. The results quantitatively demonstrate that interaction risk escalates significantly with abrupt speed changes (OR = 16.22) and late-stage occurrence of speed extremes (OR = 6.76) in the interacting vehicle, as well as large initial speed differences (OR = 2.45). Conversely, stable speed regulation and adaptive acceleration by the subject vehicle proved to be potent mitigating factors. These findings provide actionable insights for the development of intelligent collision warning systems and the sustainable design of interchange infrastructure. Full article

The successful integration of autonomous vehicle (AV) technologies into future mobility systems depends not only on technological maturity but also on user acceptance and perceived value. While existing research has identified several demographic determinants of AV acceptance, the role of educational background—particularly differences between humanities and STEM graduates—has received limited attention within the context of user-centred mobility research. This study examines how educational background and gender influence attitudes toward autonomous vehicle technologies using a large-scale survey conducted in Hungary (N = 8663). The analysis combines non-parametric statistical tests with effect size measures, exploratory factor analysis, and structural equation modelling (SEM) to capture both group differences and underlying attitudinal mechanisms. The results indicate no meaningful differences between humanities and STEM graduates in overall acceptance of autonomous vehicles or trust in the technology. Statistically significant differences are observed only in two dimensions: willingness to spend on autonomous driving features and expectations regarding improved travel speed. However, effect size analyses reveal that these differences are negligible in practical terms, indicating substantial overlap in user attitudes. SEM results show that educational background does not directly determine acceptance of autonomous vehicle technologies. Instead, its influence is mediated through three latent attitude dimensions relevant for electric and autonomous mobility adoption: willingness to invest, functional expectations (e.g., time savings and convenience), and safety orientation. Humanities graduates—especially men—exhibit slightly higher financial openness toward autonomous features, whereas STEM graduates place greater emphasis on functional performance. Safety-related attitudes play a central mediating role, with gender-specific patterns. By integrating large-sample effect size interpretation with SEM-based modelling, this study provides a nuanced understanding of user acceptance of autonomous vehicle technologies. The findings suggest that differences between educational groups reflect variations in attitudinal emphasis rather than fundamental divides, offering relevant insights for user-centred AV development, mobility policy design, and communication strategies in the transition toward automated and electric mobility systems. Full article

Machine vision based on artificial intelligence technology is being actively utilized to reduce defect rates in the photovoltaic module production process. This study aims to propose a machine vision approach using EfficientNet-B0 for defective photovoltaic module detection. In particular, the proposed approach is applied to the electroluminescence (EL) operation, which identifies microcracks in PV modules by using polarization current. The proposed approach extracts low-level structures and local brightness variations, such as busbars, fingers, and cell boundaries, from a single convolutional block. Furthermore, the mobile inverted bottleneck convolution (MBConv) block progressively transforms defect patterns—such as microcracks and dark spots—that appear at various shooting angles into high-level feature representations. The converted image is then processed using global average pooling (GAP), Dropout, and a final fully connected layer (Dense) to calculate the probability of a defective module. A sigmoid activation function is then used to determine whether a PV module is defective. Experiments show that the proposed Efficient-B0-based methodology can stably achieve defect detection accuracy comparable to AlexNet and GoogLeNet, despite its relatively small number of parameters and fast processing speed. Therefore, this study will contribute to increasing the efficiency of EL operation in industrial fields and improving the productivity of PV modules. Full article

To evaluate the cracking characteristics of asphalt pavements under thermal stresses, the finite element (FE) software ABAQUS 2021 was used in this paper to establish thermal and mechanical parameter models, respectively. The temperature field distributions in winter and summer were analyzed according to the actual situation based on fracture mechanics theory and the extended FE method, as well as the most unfavorable crack type for crack propagation was also studied. Further, the impact of the propagation of transverse cracks on the road surface was investigated by changing the solar radiation, sunshine duration, and wind speed. Finally, the propagation pattern of reflective cracks was observed under the cyclic temperature field. The results show that under the action of the temperature field alone, type I cracks, which are cracks that undergo opening displacement due to the vertical tensile stress acting on the crack surface, are the main type of cracks, while the trend of crack propagation was much higher in winter than in summer. It was also found that changing the parameters of solar radiation, sunshine duration, and wind speed could significantly impact cracking. Under the cyclic temperature field, the length of reflective cracks was proportional to time, and the initial crack length significantly affected the pavement life. Therefore, pavement inspection should be more stringent in winter, and initial cracks should be avoided as much as possible during paving. Full article

While light-based external human–machine interfaces (eHMIs) on automated vehicles (AVs) are increasingly studied to mediate pedestrian–vehicle conflicts, gaps persist in understanding how specific dynamic features of the AV’s headlights influence pedestrians’ prediction of its yielding intention and their crossing behavior. This study systematically investigates the effects of dynamic elements of vehicle lighting—including animation patterns, animation speed, and light-emitting area—on pedestrians’ objective and subjective evaluations. A factorial design framework was employed, where participants viewed video simulations of an approaching AV displaying headlight designs combining multiple dynamic features. For different vehicle motion states, the vehicle–pedestrian distance was integrated as a variable to examine its interaction effect with lighting features. Objective measures of cueing effects were complemented by subjective ratings and user preference study via questionnaires. Results showed that there were more crossing behaviors of the pedestrian when presenting higher animation speed of dynamic light eHMIs. Animation pattern and light-emitting area does not play an important role in pedestrian decision-making, but proper design of these two features can evoke higher visual attention. When the vehicle–pedestrian distance is longer, the dynamic features of lighting will more affect people’s willingness to cross. The effects of light eHMIs seemed more significant for the AV travelling in constant speed. Our findings advance preliminary suggestions for selecting light-based eHMIs in the appropriate scenarios and can contribute actionable insights for designing intuitive, human-centric AV–pedestrian negotiation strategies. Full article

The rapid acceleration of artificial intelligence (AI) and, more recently, generative AI is reshaping journalism in ways that extend far beyond earlier forms of news automation. As generative AI tools become widely accessible and capable of processing unstructured data, longstanding definitions of automated journalism—once centered on structured datasets and template-based text generation—are being fundamentally reconfigured. This paper presents the most comprehensive and up-to-date systematized review of automated journalism scholarship, expanding on earlier research by synthesizing 185 peer-reviewed, English studies published between 2012 and 2024 about machine-generated textual news content published online. Through a rigorously designed search strategy across four major social science databases, this review maps how the field’s conceptual, methodological, and geographical contours have transformed as AI and generative AI become increasingly ubiquitous. The findings show a surge of research in 2024 alone, as well as the emergence of more than 150 overlapping terms used to describe AI- and algorithmically generated news, illustrating significant conceptual fragmentation. Despite no overly dominant theories, concepts or frameworks, key themes include credibility and trust, human–machine collaboration, newsroom adoption and institutional logics, transparency and disclosure, and the ethical and regulatory challenges introduced by increasingly sophisticated AI systems. By consolidating patterns, evaluating an expanded selection of key terms, and assessing theoretical and conceptual frameworks, this review demonstrates how AI and especially generative AI reflect the speed of industrial change, but also the lack of shared academic frameworks to make sense of that change. Full article

Communication is defined as the process of transferring data and exchanging information between interconnected systems. Due to the increasing reliance on digital infrastructures by the military, financial, and healthcare sectors, it is important to ensure the confidential, authentication, and tamper-proof nature of communications. In addition, the increasing need for secure communications in the fields of network security and cryptography have led to the development of numerous systems. The basic requirement of these systems is that under the same key, identical plaintexts do not result in identical ciphertexts. The most significant contribution to this requirement has came from block cipher modes. There are many traditional modes of operation such as the Electronic Code Book (ECB) compromises between simplicity and security. Probabilistic Modes such as the Cipher Block Chaining Mode (CBC) provide a method to randomize data so that the potential for pattern analysis is eliminated, while Deterministic Modes such as ECB enable potential access to the patterns within the plaintexts. Conversely, since the randomization is in the Probabilistic Mode, there is no access to the patterns; however, the sequentiality of the blocks creates dependence and increases the computing overhead. To address these issues, a novel block cipher mode that provides the highest level of security and the most effective method for performing encryption and decryption will be proposed in this paper. It is anticipated that the improved security features and efficient encryption and decryption procedures will significantly improve confidentiality. The methods proposed will utilize compact key structures, parallel processing, a header generation based on multiple random values, and a Key-derived S Box. The experimental results show that SEBCM is more effective than CBC with respect to speed in both encryption and decryption. Full article

This paper presents a cover lens concept for camera modules based on surface acoustic waves (SAW) to mitigate the degradation of physical AI optical sensor field-of-view performance caused by surface contamination. The proposed approach utilizes a single-phase unidirectional transducer (SPUDT) that intentionally breaks left–right symmetry through a geometrically asymmetric electrode array to generate SAW, thereby removing droplet contamination. First, the acoustic streaming induced inside a single sessile droplet by the SAW was visualized, and the dynamic behavior of the droplet upon SAW actuation was observed using a high-speed camera. The internal flow developed into a recirculating vortex structure with directional deflection relative to the SAW propagation direction, indicating a symmetry-broken streaming pattern rather than a purely symmetric circulation. Upon the application of the SAW, the droplet was confirmed to move a total of 7.2 mm along the SAW propagation direction, accompanied by interfacial deformation and oscillation. Next, an analysis of transport trajectories for five sessile droplets dispensed at different y-coordinates ($y_1$–$y_5$) revealed that all droplets were transported along the x-axis regardless of their initial positions. Furthermore, the analysis of transport velocity as a function of droplet viscosity (1 $\mathrm{c}\mathrm{P}$ and 10 $\mathrm{c}\mathrm{P}$) and volume (2$\mathrm{\mu }\mathrm{L}$, 4$\mathrm{\mu }\mathrm{L}$, and 6 $\mathrm{\mu }\mathrm{L}$) demonstrated that the transport velocity gradually increased with driving voltage but decreased as viscosity increased under identical actuation conditions. Finally, the proposed cover lens was applied to an automotive front camera module to verify its effectiveness in improving object recognition performance by removing surface contamination. Based on its simple structure and driving principle, the proposed technology is deemed to be expandable as a surface contamination cleaning technology for various physical AI perception systems, including intelligent security cameras and drone camera lenses. Full article