Search Results (4,272)

Recent technological advancements have resulted in the emergence of portable electronic devices (PEDs), including mobile phones equipped with satellite communication capabilities. These devices generally emit higher power, which can potentially cause electromagnetic interference to GPS antennas. This study uses both simulation and experimental methods to evaluate the interference path loss (IPL) between PEDs located inside an A320 aircraft and an external GPS antenna. The effects of PED location, antenna polarization, and frequency bands on IPL were simulated and analyzed. Additionally, measurement experiments were conducted on an A320 aircraft, and statistical methods were used to compare the experimental data with the simulation results. Considering the front-door coupling of both spurious and intentional radiated emissions, the measured IPL is up to 15 ± 3 dB lower than the $IP{L}_{target}$. This result should be interpreted with caution. This issue offers new insights into the potential risks of electromagnetic interference in aviation environments. The findings help quantify the probability of interference with GPS antennas. Furthermore, the modeling simplification method used in this study may be applicable to the analysis of other large and complex structures. Full article

We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments. Full article

In Europe, corporate sustainability reporting through the double materiality assessment was formally introduced with the Corporate Sustainability Reporting Directive in response to the European Sustainability Reporting Standards. The double materiality assessment is essential not only to determine the scope of corporate sustainability reporting but also to guide companies toward an efficient allocation of resources and shape corporate sustainability strategies. However, although EFRAG represents the technical adviser of the European Commission, there are numerous “interoperable” standards related to the assessment of double materiality, including the Global Reporting Initiative (GRI), or UNI 11919-1:2023. This research intends to systematically analyze similarities and divergences between the most widespread double materiality assessment standards at the global scale, highlighting their strengths and weaknesses and trying to identify a comparable path toward the creation of a set of common guidelines. This analysis is carried out through the systematic study of seven standards and by answering nine questions ranging from generic ones, such as “what is the concept of double materiality?”, to more technical questions like “does the standard identify thresholds?”, but adding original prospects such as “does the standard refer to different types of capital?”. Findings highlight that EFRAG, UNI 11919-1:2023, and GRI represent the most complete and least-discretionary standards, but some methodological aspects need to be enhanced. In the double materiality assessment, companies must identify key stakeholders, material topics and material risks, and must develop the double materiality matrix, promoting transparent disclosure, continuous monitoring, and stakeholders’ engagement. While comparability is principally required among companies operating within the same sector and of similar size, this does not preclude the possibility of comparing firms across different sectors with respect to specific indicators, when appropriate or necessary. Full article

This study investigates the dynamic response mechanisms of discharge capacity in the Han River Estuary to hydrological process changes at the Yangtze–Han River confluence. By constructing a one-dimensional hydrodynamic model for the 265 km Xinglong–Hankou reach, we quantitatively decouple the synergistic effects of riverbed scouring (mean annual incision rate: 0.12 m) and Three Gorges Dam (TGD) operation through four orthogonal scenarios. Key findings reveal: (1) Riverbed incision dominates discharge variation (annual mean contribution >84%), enhancing flood conveyance efficiency with a peak flow increase of 21.3 m³/s during July–September; (2) TGD regulation exhibits spatiotemporal intermittency, contributing 25–36% during impoundment periods (September–October) by reducing Yangtze backwater effects; (3) Nonlinear interactions between drivers reconfigure flow paths—antagonism occurs at low confluence ratios (R < 0.15, e.g., Cd increases to 45 under TGD but decreases to 8 under incision), while synergy at high ratios (R > 0.25) reduces Hanchuan Station flow by 13.84 m³/s; (4) The 180–265 km confluence-proximal zone is identified as a sensitive area, where coupled drivers amplify water surface gradients to −1.41 × 10⁻³ m/km (2.3× upstream) and velocity increments to 0.0027 m/s. The proposed “Natural/Anthropogenic Dual-Stressor Framework” elucidates estuary discharge mechanisms under intensive human interference, providing critical insights for flood control and trans-basin water resource management in tide-free estuaries globally. Full article

Chemical modifications are the standard for small interfering RNAs (siRNAs) in therapeutic applications, but predicting their off-target effects remains a significant challenge. Current approaches often rely on sequence-based encodings, which fail to fully capture the structural and protein–RNA interaction details critical for off-target prediction. In this study, we developed a framework to generate reproducible structure-based chemical features, incorporating both molecular fingerprints and computationally derived siRNA–hAgo2 complex structures. Using an RNA-Seq off-target study, we generated over 30,000 siRNA–gene data points and systematically compared nine distinct types of feature representation strategies. Among the datasets, the highest predictive performance was achieved by Dataset 3, which used extended connectivity fingerprints (ECFPs) to encode siRNA and mRNA features. An energy-minimized dataset (7R), representing siRNA–hAgo2 structural alignments, was the second-best performer, underscoring the value of incorporating reproducible structural information into feature engineering. Our findings demonstrate that combining detailed structural representations with sequence-based features enables the generation of robust, reproducible chemical features for machine learning models, offering a promising path forward for off-target prediction and siRNA therapeutic design that can be seamlessly extended to include any modification, such as clinically relevant 2′-F or 2′-OMe. Full article

Identifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-sourced road networks. Traditional road-network matching usually relies on feature engineering and parameter selection of the geometry and topology of road networks for similarity measurement, resulting in poor performance when dealing with dense and irregular road network structures. Recent development of graph neural networks (GNNs) has demonstrated unsupervised modeling power on road network data, which learn the embedded vector representation of road networks through spatial feature induction and topology-based neighbor aggregation. However, weighting spatial information on the node feature alone fails to give full play to the expressive power of GNNs. To this end, this paper proposes a Spatial Pattern-aware Graph EMbedding learning method for road-network matching, named SP-GEM, which explores the idea of spatially-explicit modeling by identifying spatial patterns in neighbor aggregation. Firstly, a road graph is constructed from the road network data, and geometric, topological features are extracted as node features of the road graph. Then, four spatial patterns, including grid, high branching degree, irregular grid, and circuitous, are modelled in a sector-based road neighborhood for road embedding. Finally, the similarity of road embedding is used to find data correspondences between road networks. We conduct an algorithmic accuracy test to verify the effectiveness of SP-GEM on OSM and Tele Atlas data. The algorithmic accuracy experiments show that SP-GEM improves the matching accuracy and recall by at least 6.7% and 10.2% among the baselines, with high matching success rate (>70%), and improves the matching accuracy and recall by at least 17.7% and 17.0%, compared to the baseline GNNs, without spatially-explicit modeling. Further embedding analysis also verifies the effectiveness of the induction of spatial patterns. This study not only provides an effective and practical algorithm for road-network matching, but also serves as a test bed in exploring the role of spatially-explicit modeling in GNN-based road network modeling. The experimental performances of SP-GEM illuminate the path to develop GeoEmbedding services for geospatial applications. Full article

Urban vegetation shows significant spatial differences due to the combined effects of natural and human factors, yet fine-scale evolutionary patterns and their cross-scale feedback mechanisms remain limited. This study focuses on the Yangtze River Delta (YRD), the top economic area in China. By integrating data from multiple Landsat sensors, we built a high—resolution framework to track vegetation dynamics from 1990 to 2020. It generates annual 30-m Enhanced Vegetation Index (EVI) data and uses a new Vegetation Green—Brown Balance Index (VBI) to measure changes between greening and browning. We combined Mann-Kendall trend analysis with machine—learning based attribution analysis to look into vegetation changes across different city types and urban—rural gradients. Over 30 years, the YRD’s annual EVI increased by 0.015/10 a, with greening areas 3.07 times larger than browning. Spatially, urban centers show strong greening, while peri—urban areas experience remarkable browning. Vegetation changes showed a city-size effect: larger cities had higher browning proportions but stronger urban cores’ greening trends. Cluster analysis finds four main evolution types, showing imbalances in grey—green infrastructure allocation. Vegetation baseline in 1990 is the main factor driving the long-term trend of vegetation greenness, while socioeconomic and climate drivers have different impacts depending on city size and position on the urban—rural continuum. In areas with low urbanization levels, climate factors matter more than human factors. These multi-scale patterns challenge traditional urban greening ideas, highlighting the need for vegetation governance that adapts to specific spatial conditions and city—unique evolution paths. Full article

This study investigates the relationship between sovereign credit rating transitions and domestic equity market performance, focusing on Greece from 2004 to 2024. Although credit ratings are central to sovereign risk assessment, their immediate influence on financial markets remains contested. This research adopts a multi-method analytical framework combining algebraic combinatorics and time-series econometrics. The methodology incorporates the construction of a directed credit rating transition graph, the partially ordered set representation of rating hierarchies, rolling-window correlation analysis, Granger causality testing, event study evaluation, and the formulation of a reward matrix with optimal rating path optimization. Empirical results indicate that credit rating announcements in Greece exert only modest short-term effects on the Athens Stock Exchange General Index, implying that markets often anticipate these changes. In contrast, sequential downgrade trajectories elicit more pronounced and persistent market responses. The reward matrix and path optimization approach reveal structured investor behavior that is sensitive to the cumulative pattern of rating changes. These findings offer a more nuanced interpretation of how sovereign credit risk is processed and priced in transparent and fiscally disciplined environments. By bridging network-based algebraic structures and economic data science, the study contributes a novel methodology for understanding systemic financial signals within sovereign credit systems. Full article

With the rapid expansion of higher education in China, university teachers are facing increasing workloads and mounting performance pressures, posing significant threats to their occupational health. Consequently, how to enhance job performance while safeguarding faculty well-being has become a critical issue for higher education administrators. This study aims to explore the role of health-promoting leadership (HPL) in addressing the dual challenge of enhancing university teachers’ job performance while maintaining their occupational health. Drawing on the Conservation of Resources (COR) theory, this study conceptualizes job burnout as both a core indicator of occupational health and a mediating variable, as well as proposing a dual-path model to examine the direct and indirect effects of HPL on teachers’ job performance. A survey of 556 university teachers in Jiangxi Province, China, was conducted; the data were analyzed using IBM SPSS Statistics version 22.0 and AMOS version 26.0 (IBM Corp., Armonk, NY, USA). The findings suggest that HPL is positively associated with job performance, both directly and indirectly through reduced burnout, supporting a dual-pathway mechanism consistent with COR theory. These results contribute to a better understanding of the potential role of HPL in balancing teacher well-being and performance in the context of Chinese higher education. This study also extends the cross-cultural application of COR theory and provides theoretical and practical insights into how HPL may help alleviate teacher burnout and support the development of health-promoting universities. Full article

Exercise addiction (EA) is a maladaptive behavior characterized by excessive physical activity, often linked to negative psychological outcomes. This study investigated the relationships between perfectionism, sport commitment, and EA in a sample of 219 Hungarian athletes (M = 22.19 years). Using path analysis, we tested a model hypothesizing that adaptive and maladaptive perfectionism differentially predict enthusiastic and constrained commitment, which in turn influences EA. Our results showed that maladaptive perfectionism positively predicted constrained commitment (β = 0.70) and EA (β = 0.63), while negatively relating to enthusiastic commitment (β = −0.17). Conversely, adaptive perfectionism was positively associated with enthusiastic commitment (β = 0.24) and negatively with constrained commitment (β = −0.12). Moreover, enthusiastic commitment positively predicted EA (β = 0.24). We found a significant indirect effect between adaptive and maladaptive perfectionism when controlling for enthusiastic commitment, suggesting its dual role in this context. Our study suggests that enthusiastic commitment serves as a source of exercise addiction (EA) and has a dual role, acting as both a protective factor and a risk factor for it. Additionally, we found that maladaptive perfectionism is associated with higher levels of constrained commitment and EA, while correlating with lower levels of enthusiastic commitment. Conversely, adaptive perfectionism increases enthusiastic commitment and decreases constrained commitment. These findings highlight the associations between motivational and personality factors in EA, indicating that even adaptive traits can contribute to unhealthy exercise patterns in athletic environments. Full article

Aerosols are critical to the Earth’s atmosphere, influencing climate through interactions with solar radiation and clouds. However, accurately replicating the interactions between aerosols and clouds remains challenging due to the complexity of the physical processes involved. This study evaluated the performance of Coupled Model Intercomparison Project phase 6 (CMIP6) models in simulating aerosol optical depth (AOD), cloud fraction (CF), and liquid water path (LWP) by comparing them with satellite observations from MODIS and AMSR-E. Using 30 years of CMIP6 model simulations and available satellite observations during the satellite era, the results show that most CMIP6 models underestimate CF and LWP by 24.3% for LWP in the Northern Hemisphere. An assessment of spatial patterns indicates that models generally align more closely with observations in the Northern Hemisphere than in the Southern Hemisphere. Latitudinal profiles reveal that while most models capture the overall distribution patterns, they struggle to accurately reproduce observed magnitudes. A quantitative scoring system is applied to evaluate each model’s ability to replicate the spatial characteristics of multi-year mean aerosol and cloud properties. Overall, the findings suggest that CMIP6 models perform better in simulating AOD and CF than LWP, particularly in the Southern Hemisphere. Full article

As data becomes a core driver of modern business innovation, mobile applications increasingly collect and process users’ personal information, posing significant challenges to the effectiveness of informed consent and the legitimacy of user authorization. Existing research on privacy informed consent mechanisms has predominantly focused on privacy policy texts and normative legal discussions, often overlooking a critical touchpoint—the launch-time privacy pop-up window. Moreover, empirical investigations from the user’s perspective remain limited. To address these issues, this study employs a two-stage approach combining compliance audit and grounded theory. The preliminary audit of 21 mobile apps assesses the compliance of privacy pop-ups, and the formal study uses thematic analysis of interviews with 19 participants to construct a dual-path explanatory framework. Key findings reveal that: (1) while the reviewed apps partially safeguarded users’ right to be informed, compliance deficiencies still persist; (2) trust and privacy fatigue emerge as dual motivations driving user consent. Trust plays a critical role in amplifying the impact of positive messages within privacy pop-ups by enhancing the consistency among users’ cognition, affect, and behavior, thereby reducing resistance to privacy consent and improving the effectiveness of the current informed consent framework. Conversely, privacy fatigue increases the inconsistency among these factors, undermining consent effectiveness and exacerbating the challenges associated with informed consent. This study offers a user-centered framework to explain the dynamics of informed consent in mobile privacy pop-ups and provides actionable insights for regulators, developers, and privacy advocates seeking to enhance transparency and user autonomy. Full article

In social networks like community Question-Answering (cQA) services, members interact with each other by asking and answering each other’s questions. This way they find counsel and solutions to very specific real-life situations. Thus, it is safe to say that community fellows log into this kind of social network with the goal of satisfying information needs that cannot be readily resolved via traditional web searches. And in order to expedite this process, these platforms also allow registered, and many times unregistered, internauts to browse their archives. As a means of encouraging fruitful interactions, these websites need to be efficient when displaying contextualized/personalized material and when connecting unresolved questions to people willing to help. Here, demographic factors (i.e., gender) together with frontier deep neural networks have proved to be instrumental in adequately overcoming these challenges. In fact, current approaches have demonstrated that it is perfectly plausible to achieve high gender classification rates by inspecting profile images or textual interactions. This work advances this body of knowledge by leveraging lexicalized dependency paths to control the level of abstraction across texts. Our qualitative results suggest that cost-efficient approaches exploit distilled frontier deep architectures (i.e., DistillRoBERTa) and coarse-grained semantic information embodied in the first three levels of the respective dependency tree. Our outcomes also indicate that relative/prepositional clauses conveying geographical locations, relationships, and finance yield a marginal contribution when they show up deep in dependency trees. Full article

Depression and cognitive impairment frequently co-occur in older adults, but their temporal relationship remains unclear. While depression is often considered a risk factor for cognitive decline, evidence is mixed, particularly in individuals with mild cognitive impairment or early dementia (MCI/ED). This study analyzed longitudinal data from 1086 participants (M = 74.49, SD = 7.24) in the SMART4MD clinical trial, conducted in Spain and Sweden over 18 months, with assessments every six months. Cognitive impairment was measured using the Mini-Mental State Examination, and depression was assessed with the Geriatric Depression Scale-15. Findings revealed a concurrent association between depressive symptoms and cognitive impairment. In regression mixed analysis, depression levels predicted increased cognitive decline over time, but no evidence was found for cognitive impairment predicting future depression. These associations were confirmed using a bivariate latent growth curve model with cross-lagged paths, which revealed early but attenuating bidirectional effects between depression and cognition. These results highlight depression as a medium-term risk factor for cognitive decline, emphasizing the importance of addressing depressive symptoms to mitigate cognitive deterioration in MCI/ED populations. Full article

This study systematically investigates the effect of steel fiber content on the fractal evolution characteristics of bending cracks in alkali-activated slag concrete (AASC) beams. A four-point bending test on simply supported beams, combined with digital image correlation (DIC) technology, was employed to quantitatively analyze the fractal dimension of crack propagation paths in AASC beams with steel fiber contents ranging from 0% to 1.4%, using the box-counting method. The relationship between fracture energy and fractal dimension was examined, along with the fractal control mechanisms of mid-span deflection, crack width, and the fractal evolution of fracture toughness parameters. The results revealed that as the steel fiber content increased, the crack fractal dimension decreased from 1.287 to 1.155, while the critical fracture energy of AASC beams increased by approximately 75%. Both mid-span deflection and maximum crack width were positively correlated with the crack fractal dimension, whereas the fractal dimension showed a negative correlation with critical cracking stress and fracture toughness and a positive correlation with the energy release rate. When the steel fiber content exceeded 1.2%, the performance gains began to diminish due to fiber agglomeration effects. Overall, the findings suggest that an optimal steel fiber content range of 1.0% to 1.2% provides the best crack control and mechanical performance, offering a theoretical basis for the design of AASC structures. Full article