Search Results (1,343)

In complex urban environments, GNSS satellite signals suffer from severe multipath errors caused by building occlusion and reflection, which significantly degrades the accuracy of precise point positioning (PPP). This paper proposes a deep-learning-based multipath hemispherical grid correction model (DL-MHM) that integrates combined filtering and satellite embedding mechanisms. The model adopts the multi-system interoperable MHM framework to achieve effective multipath error correction. First, pseudorange and carrier phase observation residuals are calculated using the ionosphere-free combination for PPP. Then, a joint median and Kalman filtering scheme is applied to suppress noise in multi-day continuous residual sequences. A transformer-based time-series learning model is constructed, which introduces satellite-specific embedding vectors to characterize the differences between individual satellites and deeply fuse temporal features. This enables the model to adaptively fit the residual variation patterns of different satellites and accurately extract multipath errors. Finally, the multipath components predicted by the deep learning model are incorporated into the multi-system interoperable MHM model to generate the final multipath corrections. Test results show that in heavily obstructed urban scenarios, the root mean square (RMS) values of the east (E), north (N), and up (U) coordinate residuals are improved by 49.27%, 1.80%, and 3.35%, respectively, after DL-MHM correction compared to the uncorrected data. In open-sky environments, the corresponding improvements are 7.70%, 5.48%, and 34.28%. In all experimental scenarios, the proposed method outperforms both the conventional multipath hemispherical map (MHM) model and the convolutional neural network-long short-term memory (CNN-LSTM)-based MHM model in terms of overall multipath correction performance. The experimental results demonstrate that the proposed DL-MHM model can effectively mitigate multipath errors in complex urban scenarios and significantly improve the accuracy of GNSS precise positioning. Full article

Mung bean (Vigna radiata (L.) Wilczek) seed coat (MBSC) is an underutilized by-product rich in vitexin and isovitexin, but its potential as a source of spray-dried functional powders has not been systematically evaluated. This study investigated the spray drying of MBSC extract using three structurally distinct polysaccharide-based carriers—maltodextrin, trehalose, and inulin—to compare their effects on process yield, powder quality, and the content of phenolic compounds, flavonoids, and antioxidant activity. Response surface methodology (RSM) with a Box–Behnken design was employed to examine the influence of inlet temperature (130–160 °C) and carrier concentration. Maltodextrin provided the highest process yield (84.85%), while trehalose and inulin formulations exhibited stronger antioxidant activity, with the lowest DPPH IC₅₀ values of 0.096 mg/mL and 0.100 mg/mL, respectively (expressed per mg of spray-dried powder). Trehalose yielded the highest total phenolic content (TPC = 28.12 mg GAE/g extract) and acceptable flowability (Carr’s index = 20.72%). Inulin gave the highest total flavonoid content (TFC = 126.8 mg QE/g extract) but showed greater variability, attributed to its polymeric network and higher hygroscopicity. The RSM models showed high predictive accuracy for TPC (R² > 0.98) and DPPH antioxidant activity (R² ≈ 1.00). These findings offer a multi-objective optimization framework that links carrier structure to powder performance, providing practical guidance for selecting polysaccharide carriers in the development of spray-dried nutraceutical and functional food ingredients. However, direct measurement of encapsulation efficiency, particle morphology, and storage stability was beyond the scope of this study and warrants further investigation. Full article

Drawing on signaling theory, this study investigates the role of lead founders’ human capital signals in the success outcomes of equity crowdfunding (ECF) campaigns. While prior research emphasizes entrepreneurial teams or broadly defined founder characteristics, the role of dominant entrepreneurial actors remains underexplored. We focus on the lead founder, defined as the individual combining founder status, CEO authority, and ownership concentration, as the primary signal carrier in ECF contexts. Using a multi-platform dataset of 1067 campaigns from Republic Europe, Crowdcube, Mamacrowd, and Invesdor (2012–2024), we examine how lead founders’ education and experience shape investor decisions. Our results indicate that industry-related education is the strongest predictor of the number of investors. Furthermore, while industry experience alone can positively predict investor engagement, its role disappears once education is accounted for, suggesting that education in industry-related fields can outweigh industry experience in shaping investor perceptions. Additionally, our findings suggest that entrepreneurial experience and attendance at a top-ranked university do not contribute meaningfully to explaining investor participation. Accordingly, the study contributes to the human capital signaling literature by showing that investors evaluate the incremental informational value of human capital signals rather than assessing each signal independently, and highlights the centrality of the lead founder in decision-making under highly uncertain crowdfunding environments. Full article

With the emergence of mobile sixth-generation (6G) integrated sensing and communication (ISAC) scenarios, conventional multicarrier waveforms face challenges in maintaining reliable communication and robust physical-layer security. In this paper, we propose a multi-layer encryption multiple-input multiple-output (MIMO) affine frequency division multiplexing (AFDM) with index modulation (IM) scheme, which exploits the inherent flexibility of the AFDM modulation parameter $c_2$ and subcarrier IM to construct a multi-dimensional physical-layer security mechanism. To enable sensing and exploit MIMO spatial diversity, a unified downlink MIMO configuration is adopted, where sensing and communication share the same transmit waveform, receive array, and physical propagation environment. The proposed configuration enables multi-dimensional parameter estimation, including delay, Doppler, and angle. The obtained sensing information further assists beamforming design, channel reconstruction, and signal equalization. Furthermore, the base station and user equipment share synchronized secret keys, and a unified detection framework is developed to balance computational complexity and detection accuracy while remaining compatible with the multi-dimensional encryption structure of the MIMO-AFDM-IM system. Simulation results verify the effectiveness of the proposed scheme in mobile scenarios, demonstrating enhanced multi-dimensional sensing accuracy, improved resistance to eavesdropping, and superior communication reliability and energy efficiency (EE). Full article

Green hydrogen is increasingly discussed as an energy carrier that can link electricity, gas, heat, and transport sectors. However, many existing reviews address this topic from separate viewpoints, such as hydrogen production technologies, Artificial Intelligence (AI) applications, or system integration, with less attention to how policy and market conditions affect deployment. This review brings these related aspects together in one structured discussion. The paper first reviews the hydrogen supply chain, including production, storage, transport, and utilization. It then discusses an integrated multi-energy architecture in which hydrogen interacts with electricity, natural gas, heat, and cooling networks. Policy instruments in five major economies, including the European Union, the United States, China, Japan, and India, are compared. The review also summarizes the main barriers to large-scale deployment, including high production costs, limited infrastructure, technological challenges, regulatory uncertainty, and supply-chain constraints. In addition, the current market structure and selected large-scale hydrogen projects planned in the United States are reviewed. The paper also examines the role of artificial intelligence in green hydrogen systems. AI applications are grouped into four main stages of the hydrogen value chain: forecasting renewable energy generation, improving electrolyzer design and operation, optimizing storage and distribution, and supporting system-level techno-economic assessment. Recent Machine Learning (ML) studies are compared based on their methods and their contributions to operation and planning. Overall, this review highlights the role of AI in enabling green hydrogen integration within multi-energy systems. Full article

The sortie generation rate (SGR), a key metric of carrier-based aircraft operations, is severely degraded by multi-source coupled faults across the human–equipment–environment triad. Existing models oversimplify these dynamics by employing static failure probabilities and treating contributing factors in isolation, thereby underestimating systemic risk. To address this, we propose a mechanism-driven, hybrid simulation framework that dynamically captures fault coupling and cascading effects within the phased-mission system (PMS) of flight deck operations. First, 22 basic fault events are identified via fuzzy fault tree analysis (FFTA) and translated into a Bayesian network (BN) to establish a probabilistic baseline. A multi-source coupled fault model is then constructed, integrating human reliability, time-varying equipment degradation, and fault stress propagation to describe spatiotemporal coupling. A protocol is designed to robustly simulate heterogeneous fault dynamics within a discrete-continuous hybrid engine. Simulation experiments demonstrate that: (1) the baseline replicates real-world exercise data, validating framework credibility; (2) the model reveals a nonlinear SGR degradation with a sharp decline beyond a critical maintenance-pressure threshold, a behavior missed by static models; and (3) a comprehensive maintenance strategy improves long-term SGR by 73.13% over a reactive baseline. This framework provides a scalable testbed for evaluating operational resilience and informing maintenance strategies for next-generation aircraft carriers. Full article

Traditional villages are important carriers of rural cultural heritage, yet their spatio-temporal distribution and underlying mechanisms remain insufficiently understood, particularly regarding the interaction between environmental and socio-cultural drivers over long historical periods. Focusing on 131 nationally recognized traditional villages in southern Hebei, China, this study integrates GIS-based spatial analysis with historical interpretation to examine their spatial patterns, temporal evolution, and driving factors from the pre-Sui period to the Qing Dynasty and post-Qing period. The results show that traditional villages exhibit a highly clustered and uneven distribution, primarily concentrated in mountain-front zones in the western and southwestern parts of the region. Spatial analysis reveals a multi-core clustering structure, and spatial autocorrelation confirms that this pattern is statistically significant. Temporally, village formation follows a non-linear process of concentration, expansion, and stabilization, with the Ming Dynasty representing a key peak period. The findings further indicate that dominant driving mechanisms shifted over time: early settlement was mainly constrained by environmental conditions, whereas later development increasingly depended on socio-cultural processes such as migration, defense, clan organization, and regional exchange. In the contemporary context, economic development and accessibility introduce complex and non-linear effects. These results suggest that traditional villages should be understood as dynamic cultural landscapes shaped by long-term human–environment interactions. This study provides an integrated framework for understanding rural settlement dynamics and offers insights relevant to rural heritage conservation and sustainable development in transitional regions. Full article

Background/Objectives: The gene solute carrier family 52 member 3 (SLC52A3) encodes riboflavin transporter-3, a transmembrane protein essential for riboflavin absorption. Emerging evidence suggests that metabolic transporters may play a role in tumor biology. This study aimed to investigate the expression patterns, prognostic significance, genetic alterations, and functional associations of SLC52A3 in gynecological cancers. Methods: A comprehensive bioinformatic analysis was conducted using multi-omics datasets from The Cancer Genome Atlas (TCGA). Gene expression and survival analyses were performed via GEPIA3. Genetic alterations, including mutations and copy number variations, were assessed using cBioPortal. Immune infiltration correlations were analyzed through TIMER3. Protein–protein interactions and gene enrichment analyses were performed using STRING and GEPIA2, followed by Gene Ontology (GO) and KEGG pathway analyses. Results: SLC52A3 expression was significantly upregulated in ovarian, cervical, and endometrial cancers. Reduced expression of SLC52A3 was associated with poorer overall survival and shorter progression-free interval specifically in endometrial cancer. Genetic alterations in SLC52A3 were not significantly associated with survival outcomes (OS, DFS, and PFS). Functional enrichment analysis indicated that SLC52A3 is involved in biological processes such as cell junction organization and protein localization to the plasma membrane. Additionally, SLC52A3 expression showed positive correlations with genes implicated in tumor progression and metastasis, including NECTIN4, PROM2, TACSTD2, PKP3, SEMA4B, and CD46. Conclusions: These findings suggest that SLC52A3 may serve as a potential prognostic biomarker in endometrial cancer and could play a role in tumor progression pathways. Its functional associations highlight its potential relevance as a therapeutic target, warranting further experimental validation. Full article

Background: Interest in green hydrogen (GH) is increasing, as it can act both as an energy carrier and as an industrial feedstock to decarbonise applications that currently rely on fossil-based (grey) hydrogen. Hard-to-abate industries, such as steelmaking, face complex and multi-dimensional uncertainties when assessing conversion to GH and the associated supply chain redesign. Materials and Methods: We propose an enterprise-oriented decision-modelling framework that structures conversion drivers into six decision-relevant dimensions (socio-economic, infrastructure, technology, market, supply chain, and enterprise). The framework is refined through a two-round expert elicitation process and operationalised through a scenario planning workflow based on discrete key-factor projections and an elicited interdependency network. Building on this dependency structure, we propose a transparent consistency-based reduction approach that integrates pairwise projection compatibility and graph-guided screening to identify internally coherent and decision-relevant scenarios. The procedure is further demonstrated through an illustrative steelmaking conversion case. Results: The expert-supported workflow identifies 14 external key factors and their decision-relevant projections, together with an elicited interdependency structure among them. The illustrative application shows how an initial scenario space of 6561 configurations, based on eight selected key factors, can be screened to 1335 internally admissible configurations and consolidated into four representative scenarios. These scenarios capture distinct decision contexts, including coordinated acceleration, demand-led but infrastructure-constrained transition, technology and policy push with limited market pull, and fragmented, delayed transition. Conclusions: The approach enhances methodological transparency in scenario-based decision support and offers hard-to-abate industries a structured basis for evaluating green hydrogen conversion under systemic interdependencies and deep uncertainty. The illustrative application further demonstrates how the framework can transform combinatorial uncertainty into a compact and interpretable set of scenarios supporting stakeholder discussion and strategic decision-making. Full article

Against the backdrop of climate change, the volatility of energy supply and demand in integrated energy systems (IESs) has intensified, resulting in heightened scheduling challenges. Electric–hydrogen coupling has emerged as a pivotal approach to fostering multi-energy complementarity while enhancing the flexibility and stability of IES. Rational planning of an electric–hydrogen coupled integrated energy system (EH-IES) can further strengthen energy interconnection and mutual support. First, the architecture and diverse coupling modes of the EH-IES are outlined based on key technologies and coupling mechanisms. Accurate modeling serves as the “cornerstone” of planning, with electric power and hydrogen energy equipment acting as the foundational “carriers” and electric–hydrogen coupling devices as the critical “link.” By examining application scenarios across the transportation, building, and industrial sectors, the study analyzes EH-IES planning scenarios, objectives, and modeling methodologies. Sector-specific planning primarily focuses on equipment configuration and layout, evaluated from economic and/or environmental perspectives. Finally, future research directions for EH-IES planning are proposed, addressing multiple uncertainties, energy demand dynamics, and market mechanisms. These insights aim to provide a reference for subsequent studies. Full article

Addressing the issues of forest resource fragmentation and difficulties in value realization caused by traditional development models, China has explored the Natural Resource Asset Portfolio Supply Model (PSM), offering a new pathway for realizing Forest Ecosystem Service Value (FESV). However, existing studies are mostly descriptive case summaries and have yet to reveal the process mechanisms through which PSM drives forest value enhancement. Accordingly, this study selects five typical cases released by the Ministry of Natural Resources and employs multi-case research and grounded theory to deeply analyze their evolutionary pathways. The findings show that PSM promotes forest value enhancement through a gradient evolutionary pathway of “asset aggregation, functional coupling, and property rights conversion”. Asset aggregation addresses fragmentation through resource integration; functional coupling generates synergies through element combination; and property rights conversion transforms ecosystem services into transferable value carriers through institutional innovation, completing the transition from physical assets to capital. The study further identifies two roles of forest resources in composite asset packages, namely dominant resources and background resources, along with their distinct value enhancement pathways, and reveals how institutional innovation in property rights releases ecosystem services from physical constraints into transferable value carriers. The gradient evolutionary pathway constructed in this paper provides a novel process explanation for theoretical research on ecosystem service value realization, and its cross-context applicability offers a theoretical reference for natural resource management in similar global contexts. Practically, it provides managers with actionable value enhancement pathway choices and institutional design references, while also offering a viable analytical tool for policy optimization of PSM. Full article

Against the backdrop of China’s vigorous promotion of rural revitalization, traditional villages have become important carriers of rural tourism; however, their tourism development potential varies significantly. Using 182 traditional villages in Shexian County, Anhui Province, as the study area, this paper integrates multi-source data, including remote sensing, socio-economic, and online data. It constructs an evaluation index system from three dimensions: resource endowment, socio-economic conditions, and natural environment. Three machine learning models, namely, Random Forest (RF), XGBoost, and LightGBM, are employed to measure tourism development potential, and the optimal model is selected through comparative analysis. On this basis, the SHAP method is introduced to interpret the influencing factors and reveal the direction and mechanisms of their effects. The results show that (1) the LightGBM model performs best and is more suitable for evaluating tourism development potential of traditional villages; (2) service facilities, land resources, and transportation conditions are the most important influencing factors, while cultural resources and online attention also play significant roles; (3) the effects of different factors exhibit obvious nonlinear characteristics with interaction effects; and (4) the spatial pattern of tourism development potential presents a structure of “core agglomeration–transitional distribution–peripheral dispersion”. From the perspective of multi-source data and explainable machine learning, this study provides a systematic analysis of tourism development potential in traditional villages and offers a scientific reference for their differentiated development and conservation. Full article

Traditional villages in the Chaoshan region serve as living repositories of local cultural heritage. Their concentrated and coordinated conservation and utilization can transcend administrative boundaries, enabling the integrated allocation of regional resources and the enhancement of cultural synergy. Currently, conservation practices for traditional villages are largely limited to piecemeal rescue efforts focused on individual villages. There is a lack of systematic understanding from a regional perspective and an explanation of the mechanisms underlying the formation of local landscapes, which hinders the realization of economies of scale in conservation and the development of cultural synergy. To explore effective approaches for the cluster-based conservation of traditional villages in China’s Lingnan coastal region, as well as the characteristics of human–land relationships and their adaptive mechanisms, this study focuses on 115 national and provincial-level traditional villages in the Chaoshan region. By introducing methods of single-factor and multi-factor cluster identification, the study innovatively constructs a four-dimensional cluster identification framework comprising “spatial proximity, geomorphological similarity, cultural convergence, and residential isomorphism,” and, utilizing the ArcGIS platform for coupled analysis, kernel density analysis, cluster identification, and field surveys, systematically analyzed the diverse typologies and landscape-specific characteristics of traditional village clusters in the Chaoshan region. The results indicate the following: (1) The identification of Chaozhou–Shantou traditional village clusters reveals three diverse types—comprehensive, distinctive, and potential—reflecting the richness and diversity of these clusters in the region. (2) Spatially proximate clusters exhibit a single-core, multi-point distribution, topographically similar clusters show differentiated distributions across plains and river valleys, culturally convergent clusters are significantly correlated with cultural carriers such as postal routes, water transport, and trade, and residential distributions are significantly correlated with topography and landforms, collectively constituting the unique character of Chaozhou–Shantou traditional village clusters. (3) Traditional villages in Chaoshan exhibit significant coupling with the natural environment, forming diverse spatial siting patterns in relation to mountains, water, forests, fields, and the sea, reflecting differentiated adaptation to and ingenious utilization of the natural environment. (4) The adaptive mechanism of the landscape of traditional Chaozhou–Shantou villages can be distilled into a three-tiered structure, natural adaptation as the foundation, social adaptation as the framework, and cultural adaptation as the soul, revealing the spatial planning wisdom of the Chaozhou–Shantou people in complex mountain and coastal environments. This study not only deepens our understanding of the human–land relationship in traditional villages of the Chaoshan region but also provides scientific evidence and theoretical support for the holistic preservation of cultural heritage and regional coordinated development. It holds significant practical value for promoting the protection and sustainable development of rural cultural heritage in the Lingnan coastal region. Full article

Rural low-altitude tourism serves as an important carrier for the deep integration of general aviation technology and agricultural culture and tourism, driven by the comprehensive promotion of the rural revitalization strategy and the accelerated rise of the low-altitude economy. However, systematic sustainability assessment tools suitable for complex rural scenes remain lacking. This study aimed to fill this gap and constructed a multi-dimensional evaluation framework. The framework included five main dimensions: the integration of low-altitude general technology and digital infrastructure, the digital protection and activation of rural cultural heritage, the economic and social benefits of agricultural culture and tourism integration, ecological coordination and community inclusiveness, and airspace governance and policy support. Twenty-one secondary indicators supplemented these dimensions. The triangular fuzzy number-TOPSIS group decision method determined the indicator weights and reduced subjective uncertainty in expert evaluation. The TOPSIS method quantitatively evaluated and ranked five typical villages: Anji in Zhejiang, Yangshuo in Guangxi, Yuanjiajie in Hunan, Nantai in Gansu, and Lingshui in Hainan. The results show that Zhejiang Anji leads in comprehensive sustainability, followed by Hunan Yuanjiajie and Guangxi Yangshuo. Sensitivity analysis confirms the robustness of the ranking results. The innovation of this research lies in the integration of frontier elements such as airspace synergy efficiency into the evaluation framework. The application of triangular fuzzy number TOPSIS enhances the methodological rigor and robustness of the evaluation. This study provides practical insights for optimizing rural low-altitude tourism resource allocation, strengthening cultural heritage transmission, and promoting green transformation. Full article

Synthetic polyinosinic:polycytidylic acid (poly(I:C)) offers an attractive cancer therapeutic by operating on two fronts at once, combining direct tumor cell killing with immunostimulatory activity. Yet, these dual functions can only be efficiently harnessed when intracellular delivery is sufficiently effective to enable poly(I:C) to reach and activate its intracellular receptors. We addressed this delivery challenge by developing pH-responsive formulations using lipoamino fatty acid xenopeptide (LAF-XP) carriers, composed of polar cationizable succinoyl tetraethylene pentamine (Stp) and apolar cationizable LAF building blocks in defined architectures. In particular, poly(I:C)-lipid nanoparticles (LNPs) formulated with bundle LAF₄-Stp₁ XP carriers displayed increased anti-tumoral activity at decreased dosage across multiple cancer cell models, compared to control formulations. In parallel, LAF-XP LNP-delivered poly(I:C) activated immune responses, including CXCL10 production by tumor cells, and activation of peripheral blood mononuclear cells (PBMCs), characterized by increased phenotypic markers (CD69 and LAMP-1/CD107a) and functional molecules (e.g., IFN-γ and granzyme B). Conditioned supernatant of pre-stimulated PBMCs with poly(I:C) reduced cancer cell viability, highlighting the contribution of PBMC-released factors to cancer cell death. Of particular novelty is the combination of poly(I:C) with siRNA-mediated survivin knockdown to increase apoptosis in cancer cells using the bundle LAF-XP LNP. Collectively, our findings establish efficient LAF-XP LNPs as a versatile platform that supports multi-layered therapeutic strategies. Full article