Search Results (268)

Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous multi-source information, capturing global semantic relationships, and adapting to dynamic network evolution. This paper proposes a novel unsupervised multimodal community detection algorithm (UMM) based on fractal iteration. The core idea is to design a dual-channel encoder that comprehensively considers node semantic features and network topological structures. Initially, node representation vectors are derived from structural information (using feature vectors when available, or singular value decomposition to obtain feature vectors for nodes without attributes). Subsequently, a parameter-free graph convolutional encoder (PFGC) is developed based on fractal iteration principles to extract high-order semantic representations from structural encodings without requiring any training process. Furthermore, a semantic–structural dual-channel encoder (DC-SSE) is designed, which integrates semantic encodings—reduced in dimensionality via UMAP—with structural features extracted by PFGC to obtain the final node embeddings. These embeddings are then clustered using the K-means algorithm to achieve community partitioning. Experimental results demonstrate that the UMM outperforms existing methods on multiple real-world network datasets. Full article

This study estimates a willingness-to-pay (WTP) space mixed logit model to evaluate user valuations of travel time, safety, and comfort attributes associated with common access modes in Bangkok, including walking, motorcycle taxis, and localized minibuses. The model accounts for preference heterogeneity by specifying random parameters for travel time. Results indicate that users—exhibiting substantial variation in preferences—place higher value on reducing motorcycle taxi travel time, particularly in time-constrained contexts such as peak-hour commuting, whereas walking is more acceptable in less pressured settings. Safety and comfort attributes—such as helmet availability, smooth pavement, and seating—significantly influence access mode choice. Notably, the WTP for helmet availability is estimated at THB 8.04 per trip, equivalent to approximately 40% of the typical fare for station access, underscoring the importance of safety provision. Women exhibit stronger preferences for motorized access modes, reflecting heightened sensitivity to environmental and social conditions. This study represents one of the first applications of WTP-space modeling for valuing informal station access transport in Southeast Asia, offering context-specific and segment-level estimates. These findings support targeted interventions—including differentiated pricing, safety regulations, and service quality enhancements—to strengthen first-/last-mile connectivity. The results provide policy-relevant evidence to advance equitable and sustainable transport, particularly in rapidly urbanizing contexts aligned with SDG 11.2. Full article

In the context of promoting sustainable mobility, walking and cycling have been widely recognized for their environmental and health benefits. However, a notable gap often exists between residents’ motivation to engage in these modes and their actual behavior. This study focuses on this motivation–behavior discrepancy and explores how heterogeneous information interventions—within the constraints of the existing built environment—can effectively influence residents’ travel psychology and behavior. Drawing on Protection Motivation Theory, this study aims to uncover the psychological mechanisms behind travel-mode choices and quantify the relative impacts of different types of information interventions. A travel survey was conducted in Yangzhou, China, collecting data from 1052 residents. Cluster analysis was performed using travel psychology data to categorize travel motivations and examine their alignment with actual travel behavior. A random forest model was then employed to assess the effects of individual attributes, travel characteristics, and information intervention attributes on the choice of walking and cycling. The results reveal a significant motivation–behavior gap: while 76% of surveyed residents expressed motivation to walk or cycle, only 30% actually adopted these modes. Based on this, further research shows that informational attributes exhibit a stronger effect in terms of promoting walking and cycling behavior compared to individual attributes and travel characteristics. Among these, health-related information demonstrates the maximum efficacy in areas with well-developed infrastructure. Specifically, health-related information has a greater impact on cycling (21.4%), while environmental information exerts a stronger influence on walking (7.31%). These findings suggest that leveraging information to promote walking and cycling should be more targeted. Full article

Heterogeneous information network (HIN) embedding transforms multi-type nodes into low-dimensional vectors to preserve structural and semantic information for downstream tasks. However, it struggles with multiplex networks where nodes connect via diverse semantic paths (metapaths). Information fusion mainly improves the quality of node embedding by fully exploiting the structure and hidden information within the network. Current metapath-based methods ignore information from intermediate nodes along paths, depend on manually defined metapaths, and overlook implicit relationships between nodes sharing similar attributes. Our objective is to develop an adaptive framework that overcomes limitations in existing metapath-based embedding (incomplete information aggregation, manual path dependency, and ignorance of latent semantics) to learn more discriminative embeddings. We propose an adaptive multi-hop neighbor information fusion model for heterogeneous network embedding (ANHNE), which: (1) autonomously extracts composite metapaths (weighted combinations of relations) via a multipath aggregation matrix to mine hierarchical semantics of varying lengths for task-specific scenarios; (2) projects heterogeneous nodes into a unified space and employs hierarchical attention to selectively fuse neighborhood features across metapath hierarchies; and (3) enhances semantics by identifying potential node correlations via cosine similarity to construct implicit connections, enriching network structure with latent information. Extensive experimental results on multiple datasets show that ANHNE achieves more precise embeddings than comparable baseline models. Full article

Background/Objectives: Congenital microcephaly is a clinical manifestation with a heterogeneous etiology, and its epidemiological surveillance relies on the systematic identification of cases and investigation of their underlying causes to inform preventive strategies and improve prognostic assessments. In Latin America, despite the existence of congenital anomaly reporting programs since 1967, the surveillance of microcephaly only gained substantial attention following the Zika virus (ZIKV) epidemic in 2015. Since then, efforts have predominantly concentrated on cases of infectious origin, often at the expense of recognizing endogenous etiologies, particularly those of genetic nature. This review aims to examine the role of genetic alterations in microcephaly pathogenesis and evaluates the limitations of current surveillance systems. Methods: A literature review centered on syndromic and non-syndromic genetic etiologies, alongside an analysis of Latin American surveillance frameworks (ECLAMC, RyVEMCE, ICBDSR, ReLAMC) was performed. Results: The findings reveal improved case detection and increased reported prevalence; however, the proportion of genetically attributed cases has remained stable. No systematic studies were found identifying the most common genetic causes; instead, genetic investigations were limited to isolated cases with a family history. Conclusions: While epidemiological surveillance systems in Latin America have advanced in the reporting of congenital microcephaly cases, substantial gaps remain in case ascertainment and etiological investigation, particularly concerning genetic contributions Full article

This study investigates rural settlement patterns and land suitability in Rwanda’s Western Highlands, a mountainous region highly vulnerable to geohazards like landslides and flooding. Its primary aim is to inform sustainable, climate-resilient development planning in this fragile landscape. We employed high-resolution satellite imagery, a Digital Elevation Model (DEM), and comprehensive geospatial datasets to analyze settlement distribution, using Thiessen polygons for influence zones and Kernel Density Estimation (KDE) for spatial clustering. The Analytic Hierarchy Process (AHP) was integrated with the GeoDetector model to objectively weight criteria and analyze settlement pattern drivers, using population density as a proxy for human pressure. The analysis revealed significant spatial heterogeneity in settlement distribution, with both clustered and dispersed forms exhibiting distinct exposure levels to environmental hazards. Natural factors, particularly slope gradient and proximity to rivers, emerged as dominant determinants. Furthermore, significant synergistic interactions were observed between environmental attributes and infrastructure accessibility (roads and urban centers), collectively shaping settlement resilience. This integrative geospatial approach enhances understanding of complex rural settlement dynamics in ecologically sensitive mountainous regions. The empirically grounded insights offer a robust decision-support framework for climate adaptation and disaster risk reduction, contributing to more resilient rural planning strategies in Rwanda and similar Central African highland regions. Full article

Background/Objectives: In Nepal, cancer, among non-communicable diseases, has a high mortality rate. The disease significantly affects patients’ quality of life (QoL). This study aims to identify key attributes of QoL and explore patients’ preferences regarding these attributes. Methods: We implement a discrete choice experiment (DCE) survey to understand cancer patients’ preferences for different attributes of QoL, their willingness to pay for improved QoL, and their preference heterogeneity. This study innovatively uses the EuroQol measure in a DCE setting to elicit the patients’ preferences and their willingness to pay. Results: Using a random parameter logit model, we find that cancer patients prefer lower levels of pain and higher levels of performing usual activities. Overall, we find that cancer patients are willing to pay a total amount of about NRS 2.6 million [about USD 26,000] for improved quality of life. Our analysis also shows that preference heterogeneity exists among cancer patients, and the presence of uncertainty in the preferences of patients does not affect the results. Conclusions: This study sheds light on the preferences and willingness to pay for improved quality of life among cancer patients in Nepal. Understanding these preferences can inform healthcare policy and resource allocation decisions aimed at improving the QoL of cancer patients in the region. Full article

Valuation of ecosystem services (ESs) is crucial for understanding the benefits provided by ecosystems and informing sustainable management and policy decisions related to ecosystem protection. This study explores the disagreements in ecosystem service value (ESV) at the county level across China in 2020 by comparing ten land cover datasets of varying resolutions from 500 to 10 m, using the equivalent factor method. Significant disagreements in ESV estimates are identified, revealing spatial heterogeneity and large inconsistencies among estimates from different datasets, even with high spatial resolution (10 m). Across all counties, the typical discrepancy in ESV estimates between any two datasets reaches 3503 CNY/ha, and the ESV estimates for each county show an average coefficient of variation (CV) of 0.186 across the ten datasets, indicating considerable inconsistency attributable to dataset selection. The results highlight that ESV evaluations based on the CLCD, Globeland30, and GLC-FCS30 datasets demonstrate higher consistency and reliability, making them suitable for regional ecosystem service valuation. Both the landscape configurations and the area disparities of different land types have significant impacts on ESV disagreement. This study provides valuable insights into the applicability of different datasets for ESV evaluation, thereby enhancing the reliability of ESV assessments and supporting informed decision making in ecosystem management. Full article

The proliferation of malicious social bots poses severe threats to cybersecurity and social media information ecosystems. Existing detection methods often overlook the semantic value and emotional cues conveyed by emojis in user-generated tweets. To address this gap, we propose ESA-BotRGCN, an emoji-driven multi-modal detection framework that integrates semantic enhancement, sentiment analysis, and multi-dimensional feature modeling. Specifically, we first establish emoji–text mapping relationships using the Emoji Library, leverage GPT-4 to improve textual coherence, and generate tweet embeddings via RoBERTa. Subsequently, seven sentiment-based features are extracted to quantify statistical disparities in emotional expression patterns between bot and human accounts. An attention gating mechanism is further designed to dynamically fuse these sentiment features with user description, tweet content, numerical attributes, and categorical features. Finally, a Relational Graph Convolutional Network (RGCN) is employed to model heterogeneous social topology for robust bot detection. Experimental results on the TwiBot-20 benchmark dataset demonstrate that our method achieves a superior accuracy of 87.46%, significantly outperforming baseline models and validating the effectiveness of emoji-driven semantic and sentiment enhancement strategies. Full article

This study examines whether China’s rapid spread of internet and mobile information technologies has translated into greater household support for government air-quality programs. Using nationally representative data from the Chinese General Social Survey (2018), this study estimates the causal impact of digital media use on residents’ willing to pay (WTP) each month for one additional “good-air” day. Ordinary least squares shows that individuals who rely primarily on the internet or mobile push services are willing to contribute CNY 1.9–2.7 more—about 43 percent above the sample mean of CNY 4.41. To address potential endogeneity, we instrumented digital media adoption using provincial computer penetration; two-stage least squares yielded roughly CNY 10.5, confirming a causal effect. Mechanism tests showed that digital access lowers complacency about local air quality, strengthens anthropogenic attribution of pollution, and heightens the moral norm that economic sacrifice is legitimate, jointly mediating the rise in WTP. Heterogeneity analyses revealed stronger effects among high-income households and renters, while extended tests showed that (i) the impact intensifies when the promised environmental gain rises from one to three or five clean-air days, (ii) attention to international news can crowd out local WTP, and (iii) digital media raise not only the likelihood of paying but also the amount paid among existing contributors. The findings suggest that targeted digital outreach—especially messages with concrete, locally salient goals—can substantially enlarge the fiscal base for air-quality initiatives, helping China advance its ecological-civilization and dual-carbon objectives. Full article

Despite the economic importance of Protected Designation of Origin (PDO) cheeses in Italy, little research has examined how label attributes affect price premiums. For Italian cheese producers, especially those investing in PDO certification, understanding which attributes generate premiums is crucial for sustainable business strategies. This study examined attributes displayed on 420 validated cheese labels collected across Italy in 2022, focusing on hard cheese, fresh soft cheese, and string cheese. A content analysis was conducted to identify and categorize the attributes displayed on cheese labels. Following this, the hedonic pricing method, supported by multiple linear regression analysis, was used to assess the impact of these attributes—along with brand and distribution channel—on product pricing. Our findings reveal that sustainability attributes show particularly strong effects on price premiums. PDO certification generates significant premiums prominently for hard and fresh soft cheeses, cow breed information for string cheese, while specialized retail channels create higher prices for fresh soft and string cheeses. While brand–price relationships are heterogeneous, the study provides evidence of their impact. These insights enable cheese producers, marketers, and retailers to strategically prioritize product attributes, optimize distribution channels, and make informed decisions about brand positioning to maximize value in competitive cheese markets. Full article

Deep learning greatly improves the detection efficiency of abnormal traffic through autonomous learning and effective extraction of data feature information. Among them, Graph Neural Networks (GNN) effectively fit the data features of abnormal traffic by aggregating the features and structural information of network nodes. However, the performance of GNN in the field of industrial Internet of Things (IIoT) is still insufficient. Since the asymmetry of GNN traffic data is greater than that of the traditional Internet, it is necessary to propose a detection method with high detection rate. At present, many algorithms overly emphasize the optimization of graph neural network models, while ignoring the heterogeneity of resources caused by the diversity of devices in IIoT networks, and the different traffic characteristics caused by multi type protocols. Therefore, universal GNN may not be fully applicable. Therefore, a novel intrusion detection framework incorporating graph neural networks is developed for Industrial Internet of Things systems. Design mini-batch sampling to support data parallelism and accelerate the training process in response to the distributed characteristics of the IIoT. Due to the strong real-time characteristics of the industrial IIoT, data packets in concentrated time periods contain a large number of feature attributes, and the high redundancy of features due to the correlation between features. This paper establishes a model temporal correlation and designs a new model. The performance of the proposed GIDS model is evaluated on several benchmark datasets such as BoT-IoT, ACI-IoT-2023 and OPCUA. The results marked that the method performs well on both binary classification task and multiclass classification task. The accuracy on binary classification task is 93.63%, 97.34% and 100% with F1 values of 94.34%, 97.68% and 100.00% respectively. The accuracy on multiclass classification task is 92.34%, 93.68% and 99.99% with F1 values of 94.55%, 94.12% and 99.99% respectively. Through experimental measurements, the model effectively utilizes the natural distribution characteristics of network traffic in both temporal and spatial dimensions, achieving better detection results. Full article

The increasing adoption of DNNs in critical domains such as healthcare, finance, and autonomous systems underscores the growing importance of explainable artificial intelligence (XAI). In these high-stakes applications, understanding the decision-making processes of models is essential for ensuring trust and safety. However, traditional DNNs often function as “black boxes,” delivering accurate predictions without providing insight into the factors driving their outputs. Expected gradients (EG) is a prominent method for making such explanations by calculating the contribution of each input feature to the final decision. Despite its effectiveness, conventional baselines used in state-of-the-art implementations of EG often lack a clear definition of what constitutes “missing” information. This study proposes DeepPrior-EG, a deep prior-guided EG framework for leveraging prior knowledge to more accurately align with the concept of missingness and enhance interpretive fidelity. It resolves the baseline misalignment by initiating gradient path integration from learned prior baselines, which are derived from the deep features of CNN layers. This approach not only mitigates feature absence artifacts but also amplifies critical feature contributions through adaptive gradient aggregation. This study further introduces two probabilistic prior modeling strategies: a multivariate Gaussian model (MGM) to capture high-dimensional feature interdependencies and a Bayesian nonparametric Gaussian mixture model (BGMM) that autonomously infers mixture complexity for heterogeneous feature distributions. An explanation-driven model retraining paradigm is also implemented to validate the robustness of the proposed framework. Comprehensive evaluations across various qualitative and quantitative metrics demonstrate its superior interpretability. The BGMM variant achieves competitive performance in attribution quality and faithfulness against existing methods. DeepPrior-EG advances the interpretability of complex models within the XAI landscape and unlocks their potential in safety-critical applications. Full article

Unmanned aerial vehicles (UAVs), owing to their flexible coverage expansion and dynamic adjustment capabilities, hold significant application potential across various fields. With the emergence of urban low-altitude air traffic dominated by UAVs, the integrated aviation information network combining UAVs and manned aircraft has evolved into a complex space–air–ground integrated Internet of Things (IoT) system. The application of 5G/6G network technologies, such as cloud computing, network function virtualization (NFV), and edge computing, has enhanced the flexibility of air traffic services based on service function chains (SFCs), while simultaneously expanding the network attack surface. Compared to traditional networks, the aviation information network integrating UAVs exhibits greater heterogeneity and demands higher service reliability. To address the failure issues of SFCs under attack, this study proposes an efficient SFC recovery method for recovery rate optimization (ERRRO) based on virtual network functions (VNFs) migration technology. The method first determines the recovery order of failed SFCs according to their recovery costs, prioritizing the restoration of SFCs with the lowest costs. Next, the migration priorities of the failed VNFs are ranked based on their neighborhood certainty, with the VNFs exhibiting the highest neighborhood certainty being migrated first. Finally, the destination nodes for migrating the failed VNFs are determined by comprehensively considering attributes such as the instantiated SFC paths, delay of physical platforms, and residual resources. Experiments demonstrate that the ERRRO performs well under networks with varying resource redundancy and different types of attacks. Compared to methods reported in the literature, the ERRRO achieves superior performance in terms of the SFC recovery rate and delay. Full article

At the intersection of nanotechnology and cancer biology, gold nanoparticles (AuNPs) have emerged as more than passive carriers—they are active agents capable of reshaping cellular fate. Among their most promising attributes is the potential to modulate apoptosis and autophagy, two intricately linked pathways that determine tumor response to stress, damage, and treatment. Apoptosis serves as the principal mechanism of programmed cell death, while autophagy offers a dualistic role—preserving survival under transient stress or contributing to cell death under sustained insult. Thus, understanding how these mechanisms interact—and how AuNPs influence this crosstalk—may be key to unlocking more effective oncologic therapies. This review explores the molecular interplay between apoptosis and autophagy in cancer and evaluates how AuNPs impact these pathways. By enhancing radiosensitization in radiation therapy and improving drug delivery and chemotherapeutic precision, AuNPs offer a unique strategy to circumvent resistance in aggressive or refractory tumors towards shaping their biological behavior and cellular pathways and, therefore, forming a patient-centered personalized therapeutic potential. Yet, clinical translation remains challenging. The dynamic physicochemical nature of AuNPs makes their biological behavior highly context-dependent. Combined with the complexity of apoptotic and autophagic signaling and tumor heterogeneity, this creates a triad of profound intricacy. However, within this complexity lies therapeutic opportunity. Framing AuNPs, apoptosis, and autophagy as a synergistic axis may enable mechanism-informed, adaptable, and patient-specific cancer therapies. This paradigm shift invites a more strategic integration of nanotechnology with molecular oncology, advancing the frontier of precision medicine. Full article