Search Results (1,498)

Existing studies of street-related emotional perception mainly rely on static scene evaluations, which cannot capture the cumulative effects of environmental exposure during continuous walking. To address this limitation, this study proposes a method for predicting pedestrian physiological responses in sequential audiovisual street environments. Four real-world walking routes were selected, with outbound and return directions treated as independent paths, yielding eight paths and 32 valid samples. EEG, ECG, sound pressure level, first-person video, and GPS data were synchronously collected to construct a 1 s multimodal time-series dataset. Pearson correlation, Kendall correlation, and mutual information analyses were used to examine linear, monotonic, and nonlinear relationships between environmental variables and physiological indicators, and the resulting weights were incorporated into a Long Short-Term Memory (LSTM) model for multi-step prediction. Visual elements and noise exposure were the main factors influencing physiological responses. Among the models, the mutual-information-weighted LSTM performed best, achieving an R² of 0.77 for heart rate variability (RMSSD), whereas prediction of the EEG ratio (β/α and θ/β) remained limited. An additional independent street sample outside the training set was then used to generate a dual-dimensional EEG-ECG physiological response map, demonstrating the model’s potential for identifying emotional risk segments and supporting street-level micro-renewal. Full article

In response to global climate change and China’s “Dual Carbon” goals, the transition to low-carbon manufacturing has become a crucial step for achieving high-quality regional development. This study focuses on the Chengdu–Chongqing Economic Circle and constructs a comprehensive evaluation system spanning economic, technological, energy, carbon emission, and environmental dimensions. By applying a dynamic Set Pair Analysis (SPA) model coupled with a state transition matrix, we assess the low-carbon manufacturing performance of eight core cities from 2016 to 2023. The results indicate the following: (1) Strong path dependence characterizes regional low-carbon development, revealing a “Matthew effect” in which leading cities continue to advance while lagging ones face persistent barriers. (2) Cities are evolving into distinct equilibrium patterns: Chongqing is progressing toward full optimization, and Chengdu and Mianyang remain in a high-level equilibrium, whereas Suining and Zigong show signs of long-term low-level lock-in. (3) A three-tier regional structure emerges: Chongqing and Chengdu represent a high-level steady state; Deyang, Mianyang, Yibin, and Luzhou form an intermediate fluctuating tier; and Suining and Zigong constitute a low-level locked tier. (4) The low-carbon transition of manufacturing within the region remains markedly unstable, with cities such as Deyang and Yibin yet to establish steady low-carbon trajectories and remaining susceptible to regression. These findings provide a robust, evidence-based foundation for policymaking aimed at fostering coordinated and sustainable low-carbon development in the Chengdu–Chongqing region’s manufacturing sector. Full article

Under the dual carbon goals, green innovation acts as a key driver for the transformation and upgrading of China’s energy-intensive enterprises, and stands as an inevitable choice to propel China’s shift from a major energy-consuming nation to a powerful scientific and technological nation. To explore the driving mechanism of digital transformation on the green innovation efficiency of energy-intensive enterprises, this paper takes such enterprises from 2011 to 2022 as the research subject, designs three distinct scenarios (environmental protection, enterprise innovation and scientific research innovation), simulates heterogeneous development paths, and forecasts trends to 2030. The results show that green innovation efficiency rises steadily across all scenarios, with the strongest improvement under the scientific research scenario. By 2030, efficiency under this scenario is expected to reach 1.77 times that of the baseline scenario, while the other two scenarios also perform significantly better. These findings confirm the positive role of multi-dimensional policy interventions in strengthening digital-enabled green innovation efficiency. Accordingly, practical recommendations are put forward regarding how digital transformation can effectively drive the improvement of green innovation efficiency of energy-intensive enterprises from the aspects of policy formulation, enterprise management and innovation investment. Full article

Understanding the spatiotemporal formation mechanisms of built cultural heritage is essential to interpreting regional cultural landscapes and informing differentiated conservation strategies. Using Fujian Province, China, as a representative mountain–sea transitional region, this study constructs a province-scale, multi-category, and dynamically oriented analytical framework to investigate the temporal evolution, spatial structure, and driving mechanisms of immovable cultural relics. Based on a georeferenced dataset of 940 immovable cultural relics, textual historical records were standardized into continuous temporal variables and integrated with GIS-based kernel density estimation, spatial autocorrelation analysis, distance-to-coast modeling, and category co-occurrence analysis. The results reveal a pronounced temporal concentration in the Ming–Qing and modern periods, with a primary formation peak during the Qing Dynasty and a secondary peak in the early 20th century driven by modern heritage. Spatially, relics exhibit significant positive spatial autocorrelation (Global Moran’s I = 0.375, p < 0.001) and form a structured dual-core pattern, consisting of a persistent coastal heritage belt and a distinct inland modern core centered in western Fujian. More than 75% of relics are located within 110 km of the coastline, confirming strong maritime orientation, while regression analysis reveals that this inland shift is primarily driven by the Modern Era rather than representing a continuous long-term trend. Category-level correlation analysis further demonstrates a clear spatial decoupling between traditional heritage and modern sites, indicating fundamentally different locational logics. Synthesizing these findings, this study proposes a dual-core driven model under a mountain–sea geographical framework, in which a path-dependent, economically reinforced coastal core coexists with a historically contingent, politically driven inland core. The results advance quantitative understanding of how multiple cultural logics, operating across different temporal scales, jointly shape complex regional heritage systems and provide a transferable framework for heritage analysis and spatially differentiated conservation planning. Full article

As AI-generated influencers increasingly dominate social media landscapes, their psychological impact on human users necessitates rigorous empirical investigation. Grounded in Self-Determination Theory, this study examines how AI influencers influence the satisfaction and frustration of users’ basic psychological needs—autonomy, competence, and relatedness. Utilizing a netnographic approach, the research identifies three pivotal psychological mechanisms. The findings reveal a fundamental paradox characterized by a dual-path process; while AI influencers can meaningfully fulfill psychological needs through consistent presence and customizable narratives, they simultaneously risk undermining these needs when perceived as instruments of algorithmic surveillance, commercial orchestration, or emotional inauthenticity. This duality underscores the complexity of AI-mediated engagement, where the same technological affordances can lead to either psychological flourishing or digital alienation. These insights emphasize the urgency for responsible AI design that prioritizes user well-being over mere commercial conversion, offering critical implications for developers, marketers, and policymakers in the evolving era of AI-driven social interaction. Full article

Retrieval-Augmented Generation (RAG) has become an important paradigm for knowledge-intensive natural language processing, as it enables Large Language Models (LLMs) to access external evidence beyond their parametric memory. However, existing RAG pipelines often rely on static user queries and predominantly semantic matching, which makes them less effective in data-intensive scenarios that require structured knowledge and multi-hop evidence aggregation. To address these limitations, we propose DMAR, a dynamic multi-anchor retrieval framework for retrieval refinement in knowledge-augmented generation. DMAR first identifies high-confidence anchor documents from an initial candidate pool through a dual-path evaluator that combines semantic relevance with knowledge-graph-based structural association. The selected anchors are then used to guide generative query reformulation, producing an enriched query for second-stage retrieval, followed by fidelity-controlled reranking to preserve alignment with the user’s original intent. We further model structural relevance using Subgraph Shapley Values and a learnable Siamese GNN-based similarity module. Experiments on five knowledge-intensive benchmarks, covering open-domain question answering, multi-hop reasoning, and fact verification, show that DMAR consistently improves retrieval and downstream answer quality over strong baselines. For example, DMAR achieves an F1 score of 62.5% on HotpotQA and 79.0% on TriviaQA. These results demonstrate that dynamically integrating semantic retrieval, structural knowledge, and query reformulation is an effective approach for robust knowledge-augmented NLP systems. Full article

The construction sector’s transition to carbon neutrality requires innovative strategies to address the performance and cost challenges of advanced building designs, such as passive-oriented nearly zero-energy buildings. This study proposes an artificial intelligence-based multi-objective optimization framework to reduce both energy consumption and construction costs for residential building envelopes in Chengdu’s hot summer and cold winter climate. The framework uses the NSGA-II genetic algorithm within DesignBuilder to explore trade-offs between energy efficiency and economic cost. Key design parameters (wall insulation thickness, roof insulation thickness, and window glazing type) are optimized to obtain a Pareto-optimal front. A subsequent global incremental cost analysis of the non-dominated solutions identifies the optimal balance where significant energy savings are achieved before diminishing returns set in. The research results show that by combining the NSGA-II algorithm with the global incremental cost method in the Chengdu area, the parameters of the enclosure structure can be systematically optimized, and the optimal balance point between energy conservation and cost can be effectively identified. Based on this, an “energy-saving optimal—trade-off optimal—cost optimal” template set design path based on dual objectives of energy consumption and cost can be obtained, which is applicable to different demand-oriented engineering scenarios. This research provides a quantifiable decision-making basis for the design of buildings with passive design strategies that achieve near-zero energy consumption in hot summer and cold winter regions, helping to achieve the coordinated optimization of energy efficiency goals and economic feasibility, and promoting the reliable promotion and application of near-zero energy buildings. Full article

Path planning and formation control in leader–follower robotic systems are active areas of research, as both are highly relevant to the proper execution of the assigned task. In this work, a dual approach to the A* algorithm is applied to generate consistent trajectories for a multi-agent robotic system with a leader–follower scheme. The conventional A* algorithm aims to minimize the cost of finding the best path by minimizing distances. In this case, a modified A* algorithm is used because, although decision-making also involves choosing among eight options or cells, the goal is not to minimize distance; instead, the focus is on analyzing the direction of acceleration. The proposed algorithm is robust regarding the initial and relative pose of the leader with respect to the followers. The leader is tracked using a digital accelerometer. The algorithm is tested by simulating various patterns and implemented in two experimental test scenarios: the first with differential mobile robots, and the second with an Ackerman-type mobile robot. In both scenarios, the trajectories were achieved with deviations in x and y between the follower’s path and the leader’s path of less than 0.03, and the leader’s pose independence was maintained. Full article

Background: Accurate identification of cancer driver genes is essential for understanding tumorigenesis and developing targeted therapies. Although graph neural networks (GNNs) have advanced multi-omics integration, existing methods often simply concatenate omics features and underutilize the topological information of biological networks. Methods: We propose M-GNN, a multi-modal GNN framework for cancer driver gene prediction. It employs separate Graph Convolutional Network (GCN) encoders to process four types of omics data (mutation, expression, methylation, copy number variation (CNV)), each represented as a 16-dimensional vector. We incorporate knowledge distillation by using soft labels from a pre-trained teacher model to enhance feature representation. An attention mechanism adaptively fuses the encoded omics features, and a dual-path classifier combining a GCN and a Multilayer Perceptron (MLP) preserves both intrinsic gene properties and network topology. Results: Experiments on three public protein–protein interaction (PPI) networks show that M-GNN consistently achieves the highest or second-highest AUPRC compared to five state-of-the-art methods. Ablation studies confirm the contribution of each module, and biological interpretability analysis—including analysis of GO enrichment and drug sensitivity—validates the reliability of the predicted genes. Conclusions: M-GNN provides a robust and interpretable computational tool for systematic cancer driver gene identification, effectively integrating multi-omics and network data. Full article

While vision-language models (VLMs) have been widely applied in zero-shot anomaly detection (ZSAD), their performance remains limited by the inability to distinguish fine-grained normal and abnormal textures, coupled with inadequate capabilities in detecting complex morphological anomalies. To address these limitations, this paper proposes BAG-CLIP (Bifurcated Attention Graph-Enhanced CLIP), a dual-path graph-enhanced zero-shot anomaly detection method. This approach employs a Bifurcated Self-Attention (BSA) module to decouple visual features, processing global semantics and spatial details separately to mitigate the inherent conflict between abstract semantic representation and precise spatial localization. A Self-Attention Graph (SAG) module is designed to model the topological structure of complex morphological anomalies. This module dynamically constructs visual features’ topological relationships and utilizes graph convolutions to aggregate neighborhood information, thereby enhancing the model’s representational capacity for diverse and complex morphological anomalies. Extensive experiments are conducted on five diverse industrial datasets, featuring complex transmission line backgrounds alongside general industrial scenarios. The proposed method is comprehensively evaluated against 11 state-of-the-art (SOTA) methods. On the EPED (Electrical Power Equipment Dataset) and MPDD datasets, BAG-CLIP outperforms the second-best methods in image-level AUROC (Area Under the Receiver Operating Characteristic Curve) by 3.7% and 2.8%, respectively. BAG-CLIP achieves superior performance in both zero-shot anomaly detection and segmentation. Full article

Artificial intelligence (AI) is reshaping electronic retailing, yet many firms struggle to translate AI adoption into a sustainable competitive advantage, and research still lacks an integrative explanation of how digital maturity, AI implementation, AI-enabled benefits, customer experience, and competitive outcomes are linked in this context. This study develops and tests a capability-to-advantage framework proposing that digital maturity is associated with AI implementation, that AI implementation is associated with qualitative and quantitative AI benefits, and that these benefit streams are linked to digitally mediated customer experience and to differentiation and cost-based competitive advantage. Using survey data from retail employees and managers, we estimated the model with PLS-SEM and applied cIPMA to identify actionable priorities by combining importance-performance evidence with necessity-oriented insights. We triangulated the proposed mechanisms through NVivo-based sentiment and thematic analysis of open-ended comments. Results support all hypothesized relationships. Digital maturity strongly predicts AI implementation, which increases both benefit streams and directly improves the customer experience. Customer experience was the strongest downstream driver of both competitive advantage dimensions and partially mediated the effects of AI-enabled benefits. cIPMA identified customer experience and AI implementation as the primary improvement priorities; qualitative evidence was predominantly positive and highlights efficiency/cost gains and decision support alongside the capability constraints. The study integrates capability-based and customer-experience perspectives to offer a theory-guided explanation of how digital maturity and AI implementation are associated with competitive outcomes in electronic retailing while also offering guidance for managers seeking AI-driven advantage. Full article

Electromagnetic vortex waves carrying orbital angular momentum (OAM) provide an additional modal dimension for electromagnetic scattering analysis, but the resulting OAM mode-purity spectra are highly nonlinear and expensive to characterize through repeated full-parameter simulations. To address this issue, this work proposes a dual-path data-driven surrogate framework for the simulation-level prediction of OAM mode-purity spectra in metallic-target vortex-wave scattering. High-frequency datasets were generated within a prescribed workflow that combined an angular-spectrum formulation of Bessel vortex beams with a facet-based physical-optics method. Five representative metallic targets were considered, namely, Plate, Spiral, Spite, Missile, and Dihedral. In the first surrogate path, a numerical-parameter-based regression model was developed to predict the mode-purity spectrum from physical scattering variables for canonical targets. In the second surrogate path, a phase-map-based regression model was introduced to predict the spectrum directly from scattered-field phase maps without explicit geometric parameterization. The results show that the parameter-based surrogate achieves low prediction errors for canonical targets, while the proposed ConvNeXt + GAM model provides strong regression performance across multiple target categories in the phase-map-based setting. Overall, the proposed framework offers an efficient surrogate approximation of the nonlinear mapping between the scattering conditions and OAM mode-purity spectra under simulated conditions. This study is positioned as a simulation-level surrogate modeling investigation, and extension to experimental measurements or real-scene applications remains as future work. Full article

In this article, I will use the ecological approach to explore the recent videos of Pinar Öğrenci. I will focus on two works: Agit (2022) and Cemetery of the Nameless (2025). In the latter work, there is a complex examination of the interplay between the precarious paths taken by refugees and the climate change crisis. She also explores the multiple layers of history and memorialization in sites that have been scarred by genocide. In Cemetery of the Nameless (2025), Pinar establishes an analogy between missing bodies and the contamination of the water of Lake Van. However, this connection is not linear and there is no direct cause and effect; Lake Van was meant to be a transit zone for the refugees, not a cemetery. I will argue that the function of analogy is in its suggestion of comparisons, rather than the establishment of equivalence. Öğrenci thereby puts the analogy to work in a dual manner—it both amplifies and concentrates our attention. We listen to the narratives of migration while looking at the scenes caused by climate change. The image broadens the horizon of the narrative, and the voice sucks the gaze into a dark hole. In this manner, Öğrenci’s art of witnessing, which both combines and separates voice and image, amplifies and concentrates the transfer of information. I will also frame this commentary on the artworks with a broader discussion on the politics of care and memorialization. Full article

This paper investigates the mechanical transfer mechanism of bimetallic composite pipes used in highly sour gas fields located in high-steep mountainous areas. It systematically analyzes the mechanical response behavior of these pipes under the coupled effects of complex geological conditions and operational loads. By establishing and validating a finite element model that accounts for material nonlinearity and pipe–soil interaction, the study examines the influence of key factors—including internal pressure, landslide displacement, and base pipe wall thickness—on the stress distribution and transfer mechanism within the pipeline. The results demonstrate that increased internal pressure significantly elevates both circumferential and axial stresses: when internal pressure increases from 7 MPa to 9 MPa, the liner hoop stress increases by 35.5% and the base pipe axial stress increases by 27.5%. When landslide displacement exceeds a critical threshold of 3 m, the stress in the base pipe rises sharply, with axial stress increasing by 39.7% when displacement increases from 3 m to 5 m; conversely, increasing the base pipe wall thickness from 12 mm to 15 mm effectively reduces the overall stress level, decreasing base pipe axial stress by 40.4% and liner axial stress by 86.9%. Stress transfer exhibits a dual-path characteristic, which can be described as “bidirectional transfer induced by internal pressure” and “progressive transfer caused by landslide”. These quantitative findings provide a theoretical basis for the safe design and operation of bimetallic composite pipes in high-steep mountainous regions. Full article

Battery-balancing circuits are essential for improving the performance, safety, and service life of lithium-ion battery packs in electric vehicles and energy storage systems. This paper proposes a modified multi-port half-bridge DC–DC circuit with a reconfigurable port network and its control method for battery balancing and multi-level DC voltage output. The circuit evolves from traditional inductor-based balancing units, while a new sequential turn-off switching strategy is introduced so that only one switch is turned off at any moment, achieving precise voltage distribution by adjusting the duty cycle. To improve control accuracy, a dual closed-loop voltage-current control strategy with adaptive gain scheduling and nonlinear compensation is employed. Furthermore, a predictive voltage control strategy based on Mamba-Multilayer Perceptron optimized by the Crested Porcupine Optimizer (CPO-Mamba-MLP-PVC) is proposed. This data-driven approach predicts a target voltage that considers battery and circuit losses, thereby optimizing the balancing path. Experimental results obtained from a hardware prototype verify both battery equalization and multi-level DC output functions. Compared with conventional methods, the proposed CPO-Mamba-MLP-PVC strategy reduces the balancing time by 18.03% and increases the energy utilization rate to 90.7%. Full article