Search Results (14,314)

The rapid development of the digital economy (DE) has provided innovative solutions for the transformation and upgrade of the construction industry. Leveraging technologies such as intelligent management, the Internet of Things, and artificial intelligence effectively enhances the construction industry’s green total factor productivity (GTFP). Based on data from 30 Chinese provinces spanning 2012 to 2022, this paper systematically investigates the mechanisms through which the DE influences the GTFP of the construction industry from multiple dimensions, including direct effects, indirect effects, and threshold effects. The findings reveal that the DE significantly promotes the improvement of GTFP in the construction industry. The DE indirectly enhances GTFP through technological innovation and environmental regulation, with the mediating effect of technological innovation being more pronounced. Urbanization exhibits a significant single-threshold effect in moderating the relationship between the DE and GTFP, with the impact of the DE on GTFP following a “U-shaped” trajectory. Full article

After almost a century of global generational IQ test score gains, the Flynn effect has, in the past decades, been observed to show stagnation and reversals in several countries. Tentative evidence from academic achievement data has suggested that these trajectory changes may be rooted in a decreasing strength of the positive manifold of intelligence due to increasing ability differentiation and specialization in the general population. Here, we provide direct evidence for generational IQ test score and positive manifold strength changes based on IQ test standardization data from 1392 Austrian residents between 2005 and 2018. Our analyses revealed positive Flynn effects across all domains of the IQ test (Cohen’s d from 0.21 to 0.91) but a trend toward decreasing strength in the positive manifold of intelligence (R² from .908 to .892), though these changes were not statistically significant. Our results are consistent with the idea that increasingly inconsistent Flynn effect trajectories may be attributed to increasing ability differentiation and specialization in the general population over time. Full article

Area coverage and target tracking are important applications of UAV swarms. However, attempting to perform both tasks simultaneously can be a challenge, particularly under resource constraints. In such scenarios, UAV swarms must collaborate to cover extensive areas while simultaneously tracking multiple targets. This paper proposes a deep reinforcement learning (DRL)-based, scalable UAV swarm control method for a simultaneous coverage and tracking (SCT) task, called the SCT-DRL algorithm. SCT-DRL simplifies the interaction between UAV swarms into a series of pairwise interactions and aggregates the information of perceived targets in advance, based on which forms the control framework with a variable number of neighboring UAVs and targets. Another highlight of SCT-DRL is using the trajectories of the traditional one-step optimization method to initialize the value network, which encourages the UAVs to select the actions leading to the state with less rest time to task completion to avoid extensive random exploration at the beginning of training. SCT-DRL can be seen as a special improvement of the traditional one-step optimization method, shaped by the samples derived from the latter, and gradually overcomes the inherent myopic issue with the far-sighted value estimation through RL training. Finally, the effectiveness of the proposed method is demonstrated through numerical experiments. Full article

Single lane changing is one of the typical scenarios in vehicle driving. Planning a suitable single lane changing trajectory and tracking that trajectory accurately is very important for intelligent vehicles. The contribution of this study is twofold: (i) to plan lane change trajectories that cater to different driving styles (including aspects such as safety, efficiency, comfort, and balanced performance) by a 7th-degree polynomial; and (ii) to track the predefined trajectory by model predictive control (MPC) through four-wheel steering. The growing complexity of autonomous driving systems requires precise and comfortable trajectory planning and tracking. While 5th-degree polynomials are commonly used for single-lane change maneuvers, they may fail to adequately address lateral jerk, resulting in less comfortable trajectories. The main challenges are: (i) trajectory planning and (ii) trajectory tracking. Front-wheel steering MPC, although widely used, struggles to accurately track trajectories from point mass models, especially when considering vehicle dynamics, leading to excessive lateral jerk. To address these issues, we propose a novel approach combining: (i) 7th-degree polynomial trajectory planning, which provides better control over lateral jerk for smoother and more comfortable maneuvers, and (ii) four-wheel steering MPC, which offers superior maneuverability and control compared to front-wheel steering, allowing for more precise trajectory tracking. Extensive MATLAB/Simulink simulations demonstrate the effectiveness of our approach, showing improved comfort and tracking performance. Key findings include: (i) improved trajectory tracking: Four-wheel steering MPC outperforms front-wheel steering in accurately following desired trajectories, especially when considering vehicle dynamics. (ii) better ride comfort: 7th-degree polynomial trajectories, with improved control over lateral jerk, result in a smoother driving experience. Combining these two techniques enables safer, more efficient, and more comfortable autonomous driving. Full article

Advancements in underwater robotic fish have generated new requirements for diverse underwater scenarios, presenting challenges in attaining efficient and precise control, particularly in the realm of classical trajectory tracking. In response to the inherently nonlinear and underactuated characteristics of underwater robot control design, this study introduces a trajectory tracking backstepping control method for the planar motion of underactuated underwater robotic systems. The method is grounded in dung beetle optimization (DBO) backstepping control. Firstly, a dynamic model of a single-node tail-actuated robotic fish is introduced, and the model is averaged. Based on the averaged model and Lyapunov functions, the design of the backstepping control scheme is derived to ensure the stability of the control system. Subsequently, the derived backstepping control is further optimized through the application of the DBO optimization algorithm, then the optimal backstepping control (OBC) approach is presented. Finally, the proposed control scheme is applied to the simulation experiments with the robotic fish. The simulation results for straight-line tracking indicate that OBC is superior to the PID method in terms of overshoot performance, reducing the average overshoot from 0.23 to 0.02. Additionally, OBC reduces the average velocity error from 0.043 m/s (backstepping control) to 0.035 m/s, which is lower than that of the PID method, with an average velocity error of 0.054 m/s. In turn tracking, the simulation results reveal that OBC reduces the average velocity error from 0.067 m/s (backstepping control) to 0.055 m/s and demonstrates better performance than the PID method, with an average velocity error of 0.066 m/s. Under various disturbance conditions, the simulations reveal that OBC exhibits superior performance when compared to other control methods. Full article

Radar echo extrapolation by ground-based remote sensing is essential for weather prediction and flight guiding. Existing radar echo extrapolation methods can hardly capture complex spatiotemporal features, resulting in the low accuracy of predictions, and, therefore, severely restrict their use in extreme weather situations. A deep learning method was recently applied for extrapolating radar echoes; however, its accuracy declines too quickly over a short time. In this study, we introduce a solution: Residual Transformer and Unet (ResTUnet), a novel model that improves prediction accuracy and exhibits good stability with a slow rate of accuracy decline. This presented Rest-Net model is designed to solve the issue of declining prediction accuracy by integrating a 1*1 convolution to diminish the neural network parameters. We constructed an observed dataset by Zhengzhou East Airport radar observation from July 2022 to August 2022 and performed 90 min experiments comprising five aspects, including extrapolation images, the Probability of Detection (POD) index, the Critical Success Index (CSI), the False Alarm Rate (FAR) index, and the Heidke Skill Score (HSS) index. The experimental results show that the ResTUnet model improved the CSI, HSS index, and the POD index by 17.20%, 11.97%, and 11.35%, compared to current models, including Convolutional Long Short-Term Memory (convLSTM), the Convolutional Gated Recurrent Unit (convGRU), the Trajectory Gated Recurrent Unit (TrajGRU), and the improved recurrent network for video predictive learning, the Predictive Recurrent Neural Network++ (predRNN++). In addition, the mean squared error of the ResTUnet model remains stable at 15% between 0 and 60 min and starts to increase after 60–90 min, which is 12% better than the current models. This enhancement in prediction accuracy has practical applications in meteorological services and decision making. Full article

Background/Objectives: In utero exposure to painkillers has raised concerns regarding its potential impact on neurodevelopmental disorders, such as autism spectrum disorder (ASD). This study investigates the association between in utero exposure to painkillers and trajectories of hyperactivity and emotional problems in children with and without ASD, separately. Methods: Data were drawn from 5107 participants enrolled in the Longitudinal Study of Australian Children. Emotional and behavioral problems were assessed using the Strengths and Difficulties Questionnaire at ages 4, 6, and 8 years. ASD diagnosis was determined based on parental self-report by age 12. To examine the association between the exposure and the outcomes, mixed linear models were applied to assess the impact of in utero exposure to painkillers on hyperactivity and emotional problems, controlling for sex, time, and other perinatal risk factors. The interaction term between exposure and time was included to evaluate the effect of exposure on the trajectory over time. Results: In utero exposure to painkillers did not significantly affect hyperactivity or emotional problem trajectories in children with ASD. However, in non-ASD children, painkiller exposure was associated with worsening emotional problems by age 8, with males being affected to a greater extent than females. Further, emotional problem scores increased over time by gender, reflecting developmental challenges in early childhood. Conclusions: These findings indicate that prenatal painkiller exposure is unlikely to be a major determinant of the severity of neurodevelopmental outcomes in autistic children, but its role in neurodevelopmental outcomes among neurotypical children warrants further investigation. Future research should prioritize precise exposure assessments and integrate multi-environment interactions to further elucidate the long-term impacts of prenatal painkiller use. Full article

Background/Objectives: DDX3X syndrome is often misdiagnosed as autism spectrum disorder (ASD, Rett Syndrome, and Dandy–Walker Syndrome). Precise phenotyping is needed with reference to neurodevelopmental diagnosis. Observation of behavior and communication in parents with DDX3X syndrome in the USA, France, and Poland; conversations with the parents of patients; and rudimentary information in evidence-based medical articles prompted us to identify differences in communication, play, and social interaction between children with ASD only, those with both ASD and DDX3X, and those with DDX3X only. Methods: As diagnostic tool for DDX3X patients, we created a questionnaire divided into four sections: medical, social, play, and communication. Results: The results showed inconsistent diagnoses in different countries where children could have been diagnosed with DDX3X. In a comparative analysis, individuals with DDX3X exhibited greater social skills than individuals with ASD. Furthermore, those with DDX3X demonstrated higher levels of social functioning compared to children with ASD. Therefore, parents of children recently diagnosed with ASD or similar conditions are encouraged to complete a survey to determine if their child is likely to have features of DDX3X syndrome. Conclusion: Identification of early behavioral markers that differentiate children with ASD and those with DDX3X could lead to the earliest opportunity for identification and intervention, and can significantly impact developmental trajectories, leading to better long-term outcomes. Full article

In this study, we aim to construct an evaluation system to accurately measure the development status and trends of China’s new quality productivity, which is pivotal for the sustainable development of the Chinese economy. In light of the current lack of a standardized evaluation index system and precise measurement methods, we have established an evaluation index system comprising three dimensions—scientific and technological innovation, industrial upgrading, and factor transformation—in alignment with the essence and traits of new quality productivity. By the combination of the entropy method and multi-period weights, we assess the development level of new quality productivity across China’s 31 provinces from 2013 to 2022. The findings reveal the following: (1) Substantial regional disproportions exist among provinces in the advancement of new quality productivity, with Shanghai and Beijing demonstrating a notable first-mover advantage. (2) While the levels of new quality productivity in most provinces are generally modest, an overall positive development trajectory is observed. Drawing upon these outcomes, a set of targeted development strategies are put forward, such as leading scientific and technological innovation, promoting industrial upgrading, and realizing the transformation of elements. This article can enhance our understanding of the spatiotemporal development pattern of China’s new quality productivity, offering a novel theoretical framework and practical approach for fostering new quality productivity tailored to unique circumstances. Consequently, it may facilitate the promotion of economic sustainability. Full article

Invasive species are a major driver of environmental change and pose a significPant threat to native biodiversity due to their ability to invade and establish themselves in natural or semi-natural ecosystems. This study analyzed the presence, abundance, and distribution of invasive alien plant species in a Mediterranean urban area, the municipality of Córdoba (Andalusia region, southern Spain). It includes an important historic city center, urbanized areas but also peri-urban natural and semi-natural sites around. A detailed review of bibliography and computerized botanical databases was conducted prior to an extensive fieldwork and GIS analysis carried out during 2021, 2022, and 2023. Our research identified 227 populations of 17 invasive plant species from 10 different families. These species represent 35% of the invasive plant species reported in the Andalusia region and 27% of those reported in Spain. In total, 53% of the species were introduced from America, especially South America, but no alien invasive species from other European regions were detected. The highest concentrations were found in ruderal areas, followed by abandoned fields, but also in urbanized areas, including the UNESCO World Heritage historic city center. Seven invasive herbaceous species were identified (perennial herbs), compared to 10 invasive tree species, with phanerophytes being the most abundant life form. This likely reflects the predominant use of alien woody species for ornamental and reforestation purposes, as well as the greater resilience of woody and perennial species to the increasingly warm and dry conditions of the Mediterranean climate—a phenomenon exacerbated in recent years by climate change. Our findings suggest that the use of non-native species as ornamentals is the primary driver of the establishment, spread, and ecological impact of invasive plants in the study area. This study provides valuable insights into the current situation and the potential future trajectory of invasive species, facilitating the development of management strategies and restoration efforts to address the growing issue of biological invasions in the Mediterranean region. Full article

With the advancement of service robot technology, the demand for higher boundary precision in indoor semantic segmentation has increased. Traditional methods of extracting Euclidean features using point cloud and voxel data often neglect geodesic information, reducing boundary accuracy for adjacent objects and consuming significant computational resources. This study proposes a novel network, the Euclidean–geodesic network (EGNet), which uses point cloud–voxel–mesh data to characterize detail, contour, and geodesic features, respectively. The EGNet performs feature fusion through Euclidean and geodesic branches. In the Euclidean branch, the features extracted from point cloud data compensate for the detail features lost by voxel data. In the geodesic branch, geodesic features from mesh data are extracted using inter-domain fusion and aggregation modules. These geodesic features are then combined with contextual features from the Euclidean branch, and the simplified trajectory map of the grid is used for up-sampling to produce the final semantic segmentation results. The Scannet and Matterport datasets were used to demonstrate the effectiveness of the EGNet through visual comparisons with other models. The results demonstrate the effectiveness of integrating Euclidean and geodesic features for improved semantic segmentation. This approach can inspire further research combining these feature types for enhanced segmentation accuracy. Full article

The chemistry of atmospheric precipitation serves as an important proxy for discriminating the source(s) of air contaminants in urban environments as well as to discuss the dynamic of atmospheric chemistry exchanges. This approach can be undertaken at time scales varying from single events to seasonal and yearly time frames. Here, we characterized the chemical composition of two single rain episodes (18 July 2018 and 21 February 2019) collected in Wrocław (SW Poland). Our results demonstrated inner variations and seasonality (within the rain event as well as between summer and winter), both in ion concentrations as well as in their potential relations with local air contaminants and scavenging processes. Coupling statistical analysis of chemical parameters with meteorological/synoptic conditions and HYSPLIT back trajectories allowed us to identify three main factors (i.e., principal components; PC) controlling the chemical composition of precipitation, and that these fluctuated during each event: (i) PC1 (40%) was interpreted as reflecting the long-range transport and/or anthropogenic influences of emission sources that included biomass burning, fossil fuel combustion, industrial processes, and inputs of crustal origin; (ii) PC2 (20%) represents the dissolution of atmospheric CO₂ and HF into ionic forms; and (iii) PC3 (20%) originates from agricultural activities and/or biomass burning. Time variations during the rain events showed that each factor was more important at the start of the event. The study of both SO₄²⁻ and Ca²⁺ concentrations showed that while sea spray inputs fluctuated during both rain events, their overall impact was relatively low. Finally, below-cloud particle scavenging processes were only observed for PM₁₀ at the start of the winter rain episode, which was probably explained by the corresponding low rain intensity and an overlap from local aerosol emissions. Our study demonstrates the importance of multi-time scale approaches to explain the chemical variability in rainwater and both its relation to emission sources and the atmosphere operating processes. Full article

In physical spaces, pointing interactions cannot rely on cursors, rays, or virtual hands for feedback as in virtual environments; users must rely solely on their perception and experience to capture targets. Currently, research on modeling target distribution for pointing interactions in physical space is relatively sparse. Area division is typically simplistic, and theoretical models are lacking. To address this issue, we propose two models for target distribution in physical space-pointing interactions: the single-target pointing endpoint distribution model (ST-PEDM) and the multi-target pointing endpoint distribution model (MT-PEDM). Based on these models, we have developed a basic region partitioning algorithm (BRPA) and an enhanced region partitioning algorithm (ERPA). We conducted experiments with 15 participants (11 males, and four females) to validate the proposed distribution models and region partitioning algorithm. The results indicate that these target distribution models accurately describe the distribution areas of targets, and the region partitioning algorithm demonstrates high precision and efficiency in determining user intentions during pointing interactions. At target distances of 200 cm and 300 cm, the accuracy without any algorithm is 60.54% and 42.39%, respectively. Using the BRPA algorithm, the accuracy is 72.94% and 68.57%, while, with the ERPA algorithm, the accuracy reaches 84.11% and 82.74%, respectively. This technology can be utilized in interaction scenarios involving handheld pointing devices, such as handheld remote controls. Additionally, it can be applied to the rapid capture control and trajectory planning of drone swarms. Users can quickly and accurately capture and control target drones using pointing interaction technology, issue commands, and transmit data through smart glasses, thereby achieving effective drone control and trajectory planning. Full article

This study examines the photovoltaic (PV) landscape-related literature indexed in the Web of Science database from 2005 to 2024, employing a combination of bibliometric analysis software and a manual review to analyze, explore, and summarize the development trajectory and future trends in PV landscape research. Over the past two decades, PV landscape research has progressed through three stages: the foundational stage from 2005 to 2008, during which studies primarily focused on the environmental impacts of PV installations; the developmental stage from 2009 to 2020, characterized by interdisciplinary integration, with research shifting its focus to the combination of PV systems with living and production environments, advancements in PV landscape technologies, and innovations in PV materials; and the maturity stage from 2021 to 2024, which has seen heightened requirements for energy conversion efficiency and stability in PV systems, along with the establishment of a systematic research framework for PV landscapes, enabling more diverse explorations of its development. Based on this analysis, this study summarizes key research frontiers in PV landscapes, including the impacts and assessment of PV installations on the ecological environment, the deep integration of PV systems with living environments, and the visual aesthetic impacts and evaluation of PV landscapes. Finally, this study proposes three future prospects for PV landscapes and briefly discusses the limitations of this research. Full article

Trajectory planning in tight spaces presents a significant challenge due to the complex maneuvering required under kinematic and obstacle avoidance constraints. When obstacles are densely distributed near the target state, the limited connectivity between the feasible states and terminal state can further decrease the efficiency and success rate of trajectory planning. To address this challenge, we propose a novel Dual-Stage Motion Pattern Tree (DS-MPT) algorithm. DS-MPT decomposes the trajectory generation process into two stages: merging and posture adjustment. Each stage utilizes specific heuristic information to guide the construction of the trajectory tree. Our experimental results demonstrate the high robustness and computational efficiency of the proposed method in various parallel parking scenarios. Additionally, we introduce an enhanced driving corridor generation strategy for trajectory optimization, reducing computation time by 54% to 84% compared to traditional methods. Further experiments validate the improved stability and success rate of our approach. Full article