Search Results (79)

Collaborative driving, in which autonomous vehicles cooperate with other vehicles and roadside infrastructure to improve safety, perception, and traffic efficiency, is emerging as a key paradigm for next-generation transportation systems. While such collaboration enhances situational awareness, it also introduces new security vulnerabilities across perception, communication, planning, decision-making, and control layers. In this survey, we present a unified taxonomy of security threats and defense mechanisms in collaborative driving systems, systematically organizing attacks and countermeasures across system layers. We further examine the integration of language models, including vision-based and multimodal reasoning models, into collaborative driving pipelines, highlighting the resulting security risks and design challenges. Finally, we identify key open research challenges, including cross-layer and end-to-end security, uncertainty-aware defenses, and real-world validation, outlining promising directions for future work toward secure and resilient collaborative autonomous mobility. Full article

With global warming and the increasing frequency of extreme heat events, heat health risk assessment (HHRA) has become a critical topic in climate change studies. However, the study themes, methods, and governance orientation of HHRA vary significantly across spatial scales, limiting the comparability and practical integration of assessment outcomes. This study conducts a review of the HHRA literature from 2007 to 2025, analyzing publication trends and evolving research paradigms. The results indicate the following: (1) rapid growth in the field with a notable shift from identifying static vulnerabilities to adopting “Hazard–Exposure–Vulnerability–Adaptability” (HEVA) frameworks, particularly at the micro-scale; (2) a clear scale-dependent hierarchy in assessment focus, where macro-scale studies identify regional trends, meso-scale research targets urban spatial heterogeneity and green–blue infrastructure, and micro-scale assessments emphasize housing conditions and individual perceptions; and (3) machine learning has been widely applied to capture complex nonlinear mechanisms and threshold effects. Finally, this study further emphasizes the importance of establishing a full-process feedback mechanism from macro-level early warning to meso-scale planning and micro-scale intervention, bridging the gap between regional policy and community-level action and providing a theoretical foundation for building climate-resilient cities. Full article

Multi-agent trajectory prediction serves as a critical component in autonomous driving systems, bridging environment perception, behavior understanding, and motion planning. Its outputs not only affect candidate trajectory evaluation and interactive decision-making but also directly influence downstream processes such as risk anticipation, braking and yielding, and safety margin allocation. Therefore, obtaining accurate and stable prediction results is of great importance. Although existing methods have achieved remarkable progress in single-timestep prediction accuracy, most of them still adopt an independent decoding paradigm under a sliding-window setting. As a result, during continuous online prediction, these models are prone to frequent mode switching, temporal discontinuities in overlapping segments, and local trajectory jitter, which become particularly pronounced in complex interactive scenarios such as yielding, merging, and unprotected turning. To address these issues, this paper proposes PHR-Net, a two-stage proposal-level historical retrieval framework that introduces cross-timestep historical context to perform consistency-aware refinement of current predictions on top of multimodal coarse proposals. Experiments on the Argoverse 1 benchmark show that PHR-Net achieves competitive performance under both Top-1 and Top-6 settings. PHR-Net obtains a Top-1 minFDE of 1.0834 and MR of 0.1046 and achieves an MR of 0.1027 under the Top-6 setting. In the overlapping-interval consistency evaluation, PHR-Net reduces the summed ADE to 2.08. These results show that proposal-level historical retrieval improves endpoint reliability and cross-timestep temporal consistency. Full article

Withthe widespread deployment of intelligent terminals, mobile payment platforms, and Internet of Things devices, security systems are being progressively transformed from traditional transaction outcome analysis toward an intelligent perception paradigm centered on user behavior, device states, and environmental context. To address the challenges of multimodal data heterogeneity, non-independent and identically distributed data across nodes, and the difficulty of centralized modeling under privacy constraints in distributed scenarios, an artificial intelligence-driven federated multimodal security perception framework, namely FMS-LLM, is proposed. At its core, the framework introduces a Non-IID adaptive federated fusion mechanism that achieves dual-level alignment—structural alignment via parameter-level masks and semantic alignment via feature consistency constraints—to effectively mitigate cross-node distribution discrepancies. Additionally, an LLM-driven semantic enhancement module is developed, utilizing trend-guided token selection and inertia-suppression to map low-level sensing features into high-level risk semantic representations, thereby supporting logical reasoning and explainable decision-making. This framework takes user behavioral sensing data, device state information, environmental context data, and transaction behavior data as inputs, and constructs an integrated security analysis pipeline of “perception–collaboration–reasoning”. Experimental results on the distributed multimodal security perception task demonstrate that the proposed method achieves an Accuracy of 91.62%, a Precision of 91.04%, a Recall of 90.37%, an F1-score of 90.70%, and a ROC-AUC of 94.73%, consistently outperforming baseline methods including Logistic Regression, Random Forest, LSTM, the centralized multimodal deep model, FedAvg, FedProx, and MOON. Under strongly Non-IID conditions, when $\alpha =0.1$, the model still maintains an Accuracy of 88.47% and an F1-score of 87.11%, demonstrating stronger cross-node robustness. The ablation study further indicates that the complete model attains the best classification performance while reducing communication cost to 18.92 MB/Round. These results demonstrate that the proposed method can effectively fuse multi-source sensing information under privacy-preserving conditions and support intelligent security perception tasks with higher accuracy, stronger robustness, and improved interpretability. Full article

Against the background of highly interconnected global capital markets and rapidly propagating cross-lingual information streams, traditional anomaly detection paradigms based solely on single-modality numerical time-series sensors are insufficient for forward-looking risk sensing. From the perspective of artificial intelligence-driven sensing, this study proposes a multilingual semantic–numerical collaborative Transformer framework to construct a unified multimodal financial sensing architecture for intelligent anomaly sensing and risk perception. Within the proposed sensing paradigm, multilingual texts are conceptualized as semantic sensors that continuously emit event-driven sensing signals, while market prices, trading volumes, and order book dynamics are modeled as heterogeneous numerical sensor streams reflecting behavioral market sensing responses. These heterogeneous sensors are jointly integrated through a cross-modal sensor fusion architecture. A cross-modal temporal alignment attention mechanism is designed to explicitly model dynamic lag structures between semantic sensing signals and numerical sensor responses, enabling temporally adaptive sensor-level alignment and fusion. To enhance sensing robustness, a multilingual semantic noise-robust encoding module is introduced to suppress unreliable textual sensor noise and stabilize cross-lingual semantic sensing representations. Furthermore, a semantic–numerical collaborative risk fusion module is constructed within a shared latent sensing space to achieve adaptive sensor contribution weighting and cross-sensor feature coupling, thereby improving anomaly sensing accuracy and robustness under complex multimodal sensing environments. Extensive experiments conducted on real-world multi-market financial sensing datasets demonstrate that the proposed artificial intelligence-driven sensing framework significantly outperforms representative statistical and deep learning baselines. The framework achieves a Precision of $0.852$, Recall of $0.781$, F1-score of $0.815$, and an AUC of $0.892$, while substantially improving early warning time in practical risk sensing scenarios. In cross-market transfer settings, the proposed sensing architecture maintains stable anomaly sensing performance under bidirectional domain shifts, with AUC consistently exceeding $0.86$, indicating strong structural generalization across heterogeneous sensing environments. Ablation analysis further verifies that temporal sensor alignment, semantic sensor denoising, and collaborative cross-sensor risk coupling contribute independently and synergistically to the overall sensing performance. Overall, this study establishes a scalable multimodal intelligent sensing framework for dynamic financial anomaly sensing, providing an effective artificial intelligence-driven sensing solution for cross-market risk surveillance and adaptive financial signal sensing. Full article

Rapid post-disaster building damage assessment requires recognizing explicit structural failures and interpreting implicit situational cues in visually complex scenes. Whereas conventional automated methods are often confined to detecting explicit damage patterns, human perception naturally integrates both types of information into a holistic risk judgment. This study presents an exploratory investigation into the neural signatures underlying this integrated judgment process using electroencephalography. A modified paradigm was employed to probe the cognitive dynamics of risk evaluation in participants with civil engineering backgrounds. Although participants were instructed only to identify damaged buildings without explicit severity grading, event-related potential analysis revealed systematic, graded neural responses that scaled with damage severity. This suggests that the brain encodes damage-related information not as a binary state but as a continuous spectrum of perceived risk, implicitly processing severity, even in the absence of explicit instructions. Furthermore, single-trial analysis demonstrated that time-domain features contain robust discriminative information, verifying the feasibility of decoding these latent judgments from brain activity. These findings provide a physiological basis for developing future cognition-informed algorithms and human-in-the-loop frameworks, bridging the semantic gap to enhance the reliability of automated disaster assessment. Full article

Unmanned vehicles are becoming increasingly essential in executing high-risk missions in unknown environments such as search and rescue. As the complexity of operational environments escalates, carrying out unmanned tasks becomes cumbersome or even infeasible for a single vehicle, hampered by limited perception and operational constraints. Aiming at enhancing the flexibility of unmanned operations under complicated scenarios, this study introduces AGC-NeRF, an innovative air–ground collaborative exploration framework that harnesses the functional complementarity of UAVs and UGVs—enabling a UGV to navigate through a complex scenario with the assistance of a UAV via referencing a neural radiance map. First, a UAV is employed to collect aerial images for reconstructing the environment to be explored by a UGV, leveraging its aerial perspective to achieve wide-area coverage and global environmental perception that is unattainable for a single UGV. Concurrently, an innovative image saliency evaluation approach is introduced to meticulously select landmarks that are contributive to the UGV’s navigation system, yielding a pre-trained NeRF model of the operation scene. Then, a landmark-aware 6-DOF ego-motion estimator and collision-free trajectory optimizer are designed for the UGV based on the NeRF map. Finally, an online replanning architecture is established which relies on a ground station for NeRF training and state optimization by synergizing the trajectory planner and the state estimator, which forms a dual-agent vision-only navigation pipeline. Simulations and experiments validate that AGC-NeRF enables reliable UGV trajectory planning and state estimation in unknown environments, demonstrating superior efficacy and robustness of the air–ground collaborative paradigm. Full article

Accurate gold price forecasting is crucial for economic stability and investment decision-making. In order to improve the accuracy of gold price prediction and quantify the uncertainty of gold price fluctuation, this paper proposes a hybrid model (CNN-QRLSTM) that integrates convolutional neural network (CNN) and quantile regression long- and short-term memory network (QRLSTM) and innovatively introduces news text data to quantify the media sentiment. We combine EEMD with the Hurst index to remove white noise from the original signal, and the processed data is used as the input layer of the prediction model. Furthermore, to demonstrate the impact of news sentiment on gold prices, this paper employs entropy measurement methods based on information theory to quantify the uncertainty and information content embedded within processed gold price sequences and derived sentiment indicators. The mutual information (MI) algorithm, based on information entropy, captures the nonlinear correlations between financial keywords and market sentiment. It constructs a financial sentiment lexicon (covering keywords such as economic policies and geopolitical conflicts), combines semantic rules with context-weighted strategies, calculates sentiment scores for news texts, and generates daily aggregated media sentiment indicators. This entropy-based perception method not only enhances the interpretability of emotion-driven fluctuations but also provides a theoretical foundation for reducing prediction uncertainty through multi-source data fusion. The experiment uses 2022–2025 daily London gold spot price data, Shanghai Gold Exchange gold price data, and the same period of Gold Investment Network gold market news to carry out the study. The empirical study shows that the synergy of multi-source data fusion and the quantile regression mechanism can improve the accuracy of gold price prediction and the new paradigm of risk interpretation while providing theoretical support for the formulation of quantitative investment strategies. Full article

With the rapid proliferation of short-video platforms and live-streaming commerce ecosystems, marketing activities are increasingly manifested through complex multimodal sensing signals. These heterogeneous sensor data streams exhibit strong temporal dependency, high cross-modal coupling, and progressive evolutionary characteristics, making early-stage fraud perception particularly challenging for conventional unimodal or static analytical paradigms. Existing approaches often fail to effectively capture weak anomalous cues emerging across multimodal channels during the initial stages of fraudulent campaigns. To address these limitations, an artificial intelligence-driven multimodal sensor perception framework is proposed for temporal fraud detection in short-video environments. A multimodal temporal alignment module is first designed to synchronize heterogeneous sensor signals with inconsistent sampling granularities. Subsequently, a shared temporal encoding network is constructed to learn evolution-aware representations across multimodal sensor sequences. On this basis, a cross-modal temporal attention fusion mechanism is introduced to dynamically weight sensor contributions at different behavioral stages. Finally, a fraud evolution modeling and early risk prediction module is developed to characterize the progressive intensification of fraudulent activities and to enable risk assessment under incomplete temporal observations. Extensive experiments conducted on real-world datasets collected from multiple mainstream short-video platforms demonstrate the effectiveness of the proposed AI-driven sensing framework. The model achieves an overall accuracy of 0.941, precision of 0.865, recall of 0.812, and F1 score of 0.838, with the AUC further reaching 0.956, significantly outperforming text-based, vision-based, temporal, and conventional multimodal baselines. In early-stage detection scenarios utilizing only the first 30% of video content, the framework maintains stable performance advantages, achieving a precision of 0.812, recall of 0.704, and F1 score of 0.754, validating its capability for proactive fraud warning. Full article

Background: This integrative review investigates how behavioural and psychological factors shape non-market environmental valuation within the scope of sustainable development. Unlike traditional technical-economic approaches, the novelty of this work lies in reframing socio-cultural drivers of pro-environmental behaviours (PEBs) within macro sustainability paradigms and proposing a socially and ethically grounded framework. The review has three objectives: (i) to incorporate psychological and socio-cultural dimensions into the sustainable development agenda; (ii) to demonstrate how values, norms, and perceptions drive PEBs; and (iii) to call for an ethical consensus across socio-economic and environmental sustainability. Methods: The review follows PRISMA 2020 guidelines and synthesises English-language empirical and conceptual studies (2010–2025) from Scopus and Web of Science, supplemented by Google Scholar. The literature search was conducted in December 2025, and rigorous screening and exclusion criteria were applied to ensure methodological reliability. Results: The review includes 69 interdisciplinary studies and 2 reports. The synthesis yields a framework on ethics that integrates psychological, behavioural, and economic perspectives in non-market environmental valuation and informs the weak vs. strong sustainability debate. Discussion: The findings connect sustainability debates to socio-cultural theories to explain how values, norms, and perceptions shape PEBs and valuation-relevant preferences. The review is limited by its integrative (non-meta-analytic) design, which relies on qualitative synthesis and expert judgement across heterogeneous theoretical and empirical traditions; therefore, a formal risk-of-bias assessment was not conducted. The review protocol was registered on OSF (registration ID W9Y8T). Full article

This paper investigates travellers’ intentions, with regard to preferences for a green and/or ethical hotel, boycotting hotels accused of extreme environmental damages or over-exploitation of workers, and sharing relevant information on social media. Questioning the claim that intentions to prefer a green hotel are based mainly or even solely on practical criteria, this study focuses on examining the influencing power of values. The Values-Beliefs-Norms model was employed and modified as the New Environmental Paradigm was replaced by climate change risk perception. Personal interviews were conducted with consumers in the urban area of Thessaloniki, Greece, using a structured questionnaire for data collection. Area sampling, in combination with quota sampling, in terms of gender and age, was used. Results provided that egoistic and altruistic values were excluded from the final structural model, and just biospheric values indicated a statistically significant positive relationship with Risk Perception. The other hypothesised consecutive relationships between Biospheric Values (BV), Risk Perception (RP), Awareness of Consequences (AC), Ascription of Responsibility (AR), Personal Norms (PN) and Intentions (Int) were found to be statistically significant and positive. Overall, 80.9% of the variance in Intentions was explained, while Personal Norms indicated the stronger impact on Intentions among all other relationships in the chain. Eventually, theoretical and practical implications, as well as future research directions, are suggested. Full article

Industrial heritage—particularly large modern cement plants—serves as a crucial witness to the architectural and technological evolution of modern urbanization. In Europe, North America, and East Asia, many decommissioned cement factories have been transformed into cultural venues, creative districts, or urban landmarks, while a greater number of sites still face the risks of functional decline and spatial disappearance. In China, early large-scale cement plants have received limited attention in international industrial heritage research, and their conservation and adaptive reuse practices remain underdeveloped. This study takes the Huaxin Cement Plant, founded in 1907, as the research object. As the birthplace of China’s modern cement industry, it preserves the world’s only complete wet-process rotary kiln production line, representing exceptional rarity and typological significance. Combining social media perception analysis with the Hidalgo-Giralt four-quadrant model, the study aims to clarify the plant’s value positioning and propose a design-oriented pathway for spatial activation. Based on 378 short videos and 75,001 words of textual data collected from five major platforms, the study conducts a value-tag analysis of public perceptions across five dimensions—historical, technological, social, aesthetic, and economic. Two composite indicators, Cultural Representativeness (CR) and Utilization Intensity (UI), are further established to evaluate the relationship between heritage value and spatial performance. The findings indicate that (1) historical and aesthetic values dominate public perception, whereas social and economic values are significantly underrepresented; (2) the Huaxin Cement Plant falls within the “high cultural representativeness/low utilization intensity” quadrant, revealing concentrated heritage value but insufficient spatial activation; (3) the gap between value cognition and spatial transformation primarily arises from limited public accessibility, weak interpretive narratives, and a lack of immersive experience. In response, the study proposes five optimization strategies: expanding public access, building a multi-layered interpretive system, introducing immersive and interactive design, integrating into the Yangtze River Industrial Heritage Corridor, and encouraging community co-participation. As a representative case of modern Chinese industrial heritage distinguished by its integrity and scarcity, the Huaxin Cement Plant not only enriches the understanding of industrial heritage typology in China but also provides a methodological paradigm for the “value positioning–spatial utilization–heritage activation” framework, bearing both international comparability and disciplinary methodological significance. Full article

Historic religious cities are living examples of cultural landscapes where spiritual traditions, heritage, and visitor experiences combine to demonstrate a timeless experience. It is very challenging to achieve balance among the demands of mass pilgrimage, heritage preservation, and urbanization. Govardhan, India is a Hindu religious town with historical significance. Millions of pilgrims travel to Govardhan every year to perform parikrama and take a holy dip in kunds. The quality of the visitor experience, spatial coherence, and heritage conservation are all at risk due to increasing urbanization and tourism. The study intends to create a paradigm for the sustainable management of religious heritage towns by evaluating the factors involving visitor perception, historical significance, and spatial visibility, employing a combination of computational methods and cognitive assessments. The study employed space syntax tools (visibility graph analysis and isovist area analysis) to quantify spatial significance (SS) and identify patterns of openness, congestion, and visibility along the parikrama route of Govardhan. By examining pilgrims’ cognitive surveys for openness, orientation, congestion, and spiritual impression, a cognitive index (CI) and heritage importance scores (HIS) have been developed. The computed spatial significance (SS) has been correlated with cognitive index (CI) and heritage importance (HIS) scores to create an experiential priority index (EPI). The study employs a mixed-method approach that incorporates heritage significance scoring, cognitive surveys, and spatial analytics, including methods such as the isovist area analysis and visibility graph analysis. In order to assess how spatial arrangement and intangible perceptions together influence visitor experience, these statistics are further combined using a composite experiential priority index (EPI). The findings show a strong correlation between spiritual orientation, visual connectivity, and spatial openness; locations such as ‘punchari ka lota temple’ and ‘kusum sarovar’ are high-priority nodes. In accordance with United Nation Sustainable Development Goals (SDGs) (11, 9, 12, 4.7, and 8.9), this research proposes a heritage impact assessment (HIA) framework that provides workable solutions for ecological restoration, heritage-sensitive zoning, sustainable pilgrimage management, and enhanced tourism. Full article

Background/Objectives: Physical inactivity is associated with increased cardiometabolic disease risk and poor cognition in children and their parents. Family-based physical activity offers an opportunity for children and their parents to engage in physical activity concurrently. The present study examined the effect of an acute bout of family-based tag rugby on risk factors for cardiometabolic disease and cognition in families. Additionally, this study qualitatively explored families’ perceptions of enjoyment and factors affecting implementation with considerations for socioeconomic status. Methods: Sixteen families (27 children, 20 parents) participated in an exercise (45 min family-based tag rugby) and resting control trial (45 min seated rest), separated by seven days. Postprandial gylcaemia, insulinaemia, lipaemia and cognitive function were measured following exercise/rest. Families also participated in whole-family focus groups and separate parent and child interviews. Results: In parents, postprandial plasma insulin concentrations were lower on the exercise trial than the rested control trial at 30 min (p = 0.004) and 120 min following the consumption of a standardised lunch (p = 0.011). In children, a significant trial*time interaction for inverse efficiency scores on the Sternberg paradigm (three-item) was exhibited (p = 0.016). In parents, a significant trial*time interaction for inverse efficiency score on the Stroop congruent test was exhibited (trial*time interaction; p = 0.012), whereby inverse efficiency scores improved immediately post-exercise, compared with the rested control trial (p = 0.016). Qualitatively, families from all socioeconomic backgrounds agreed that tag rugby is an inclusive, enjoyable mode of physical activity that families want to participate in together, which can be adapted to overcome the barriers associated with the cost of and access to local facilities. Conclusions: An acute bout of tag rugby improved postprandial insulin concentrations in parents and cognitive function in children and their parents. Tag rugby was deemed an appropriate exercise modality for families from a range of socioeconomic backgrounds. Full article

The kiln head temperature of a rotary kiln is a core process parameter in cement clinker production, and its accurate prediction coupled with uncertainty quantification is crucial for process optimization, energy consumption control, and safe operation. To tackle the prediction challenges arising from strong multi-variable coupling and nonlinear time series characteristics, this paper proposes a prediction approach integrating feature selection, heterogeneous model ensemble, and probabilistic interval estimation. Firstly, the Relief algorithm is adopted to select key features and construct a time series feature set with high discriminability. Then, a hierarchical architecture encompassing deep feature extraction, heterogeneous model fusion, and probabilistic interval quantification is devised. CNN is utilized to extract spatial correlation features among multiple variables, while BiLSTM is employed to bidirectionally capture the long-term and short-term temporal dependencies of the temperature sequence, thereby forming a deep temporal–spatial feature representation. Subsequently, RF is introduced to establish a heterogeneous model ensemble mechanism, and dynamic weight allocation is implemented based on the Mean Absolute Error of the validation set to enhance the modeling capability for nonlinear coupling relationships. Finally, Gaussian probabilistic regression is leveraged to generate multi-confidence prediction intervals for quantifying prediction uncertainty. Experiments on the real rotary kiln dataset demonstrate that the R² of the proposed model is improved by up to 15.5% compared with single CNN, BiLSTM and RF models, and the Mean Absolute Error is reduced by up to 27.7%, which indicates that the model exhibits strong robustness to the dynamic operating conditions of the rotary kiln and provides both accuracy guarantee and risk quantification basis for process decision-making. This method offers a new paradigm integrating feature selection, adaptive heterogeneous model collaboration, and uncertainty quantification for industrial multi-variable nonlinear time series prediction, and its hierarchical modeling concept is valuable for the intelligent perception of complex process industrial parameters. Full article