Search Results (13,980)

Accurate prediction of shipping freight indices, represented by the Baltic Dry Index (BDI), is crucial for operational decision-making and risk management in the shipping industry. Existing models mainly rely on historical time-series data and often overlook the influence of unstructured information such as market sentiment. To address this limitation, this study proposes a dynamic freight rate prediction framework integrating a shipping text sentiment index. First, a shipping news sentiment index is constructed using a RoBERTa-based pre-trained model to quantify the impact of market sentiment on freight rate fluctuations. Second, the BDI series is decomposed and reconstructed through Variational Mode Decomposition (VMD) and Fuzzy C-Means (FCM) clustering to extract multiscale features. Finally, a deep learning based multi-step prediction model is developed by incorporating the sentiment index into the forecasting process. Empirical results show that the proposed model significantly outperforms benchmark models without sentiment information in terms of MAE, RMSE, and R², and demonstrates greater robustness under extreme market conditions. These findings provide a novel methodological framework for improving freight rate forecasting accuracy and offer practical decision support for shipping enterprises. Full article

Predicting the future impact of technical economic documents at release time is challenging due to delayed supervision signals, long-tailed label distributions, and time- and domain-dependent shifts in language and topics. Moreover, similarity graphs derived from text embeddings can be noisy due to boilerplate and evolve under temporal drift, making robustness and leakage-free evaluation essential. We formulate grant-time patent impact prediction as a node classification and within-domain ranking problem on a large-scale semantic similarity document graph built from patent text embeddings, avoiding any future citation leakage. The document graph is constructed via ANN Top-K retrieval and similarity thresholding, enabling scalable and reproducible sparsification on hundreds of thousands of nodes. We propose GraphGPT-Patent, which adapts a reversible graph-to-sequence foundation backbone to local subgraphs extracted from the similarity network. The model incorporates time- and domain-conditioned edge reliability to suppress drift-induced and template-driven pseudo-similarity, and optimizes a joint objective coupling high-impact classification with ranking consistency within comparable groups. Experiments on USPTO granted patents (2000–2022) across three high-volume CPC domains and three evaluation horizons show consistent gains over text-only and GNN baselines, achieving up to 0.94 recall for the positive class and improved macro-average recall across nine settings. Temporal shift analyses further quantify the effect of training-data freshness, while explanation subgraphs provide auditable structural evidence of model decisions. The proposed framework offers an effective graph-based learning pipeline for scalable impact prediction and downstream triage under strict information constraints. Full article

Rapid access to building intelligence is critical for emergency response, yet paper fire safety instructions (FSi) often provide limited utility under stress. This structured narrative review addresses the “information gap” between unit arrival and decision-making by analyzing the legal admissibility, technological requirements, and security risks of digital FSi across Poland, Germany, France, Belgium, and Hungary. While no explicit prohibition of digital forms was identified, enforcement practices prioritize paper as the evidentiary master. Consequently, we propose a hybrid model: a paper master for compliance and redundancy, supplemented by a digital operational overlay accessed via “zero-install” offline-first progressive web apps (PWA). The review defines a minimum operational dataset (MOD)—prioritizing critical data like utility shut-offs and hazards over full documentation—and addresses cybersecurity threats, specifically QR-phishing (“quishing”). We conclude that the hybrid model minimizes legal and operational risks while significantly reducing time-to-information, provided that strict content identity and change management protocols are maintained. Full article

To address high uncertainty, dynamic evolution, and limited information in emergency decision-making for major sudden disasters, this paper proposes a sliding-window game-theoretic method with four reference points for emergency response selection. Firstly, interval-valued T-spherical fuzzy sets are adopted to capture decision-makers’ uncertain and hesitant evaluations in interval form. Subsequently, a four-reference-point framework, including the external, internal, average development speed, and ideal proximity reference points, is established to reflect stage-dependent psychological baselines. Furthermore, criterion weights are updated by a sliding-window game-theoretic combination weighting scheme that integrates entropy, anti-entropy, criteria importance through intercriteria correlation, and the coefficient of variation, and performs rolling updates across stages. Prospect values are then computed relative to the four reference points and aggregated to rank alternatives at each stage. Finally, a case study of the 2024 Huludao extreme rainfall event applies the proposed method to evaluate four candidate schemes across six criteria over three decision stages. Results show that rescue cost has the highest weight in all stages, while the importance of rescue speed decreases and social impact increases as the response progresses. The proposed method identifies a comprehensive flood relief scheme led by the People’s Liberation Army and the People’s Armed Police Force as the best option in all stages, because it achieves the highest comprehensive prospect values among all alternatives. Comparative analyses indicate more consistent identification of the optimal scheme than existing approaches, supporting sustainable and resource-efficient disaster management. Full article

Digital platforms have been increasingly adopted to support sustainable climate-resilient planning by implementing nature-based solutions (NbSs) as an effective short-term strategy. Although existing studies have deepened the operational performance of digital platforms, less attention has been paid to their role as knowledge infrastructure for shaping sustainability-relevant planning practices. This paper examines the informative structure of the Louisiana Watershed Initiative (LWI) platform. This is intended as a relevant case study to investigate how digital platforms organize data, information, and knowledge to support NbS-oriented climate resilience at the watershed scale. The study adopts a mixed-method case-study approach, combining an interpretative analysis of the platform’s digital and informational architecture with targeted tests of NbS-oriented decision-support interfaces. The results highlight the operational and cognitive conditions in shaping NbS prioritization processes—notably, those related to scaling, informational structuring, and governance alignment. While the platform effectively supports digital decision-making processes at regional and watershed levels, limitations emerge regarding how ecological knowledge is produced, interpreted, and operationalized within planning frameworks, with implications for the long-term sustainability and robustness of planning decisions. The lesson learnt by the analysis of the LWI identifies the conditions under which the analytical approach can be replicated and highlights insights relevant to both the design and evaluation of digital decision-support platforms in NbS-oriented planning contexts. Full article

Background/Objectives: Biopsy examination remains the gold standard for cancer diagnosis, relying on histopathological assessment of tissue samples to identify malignant changes. However, manual interpretation of histopathological slides is time-consuming, subjective, and susceptible to inter-observer variability. The digitization of histopathological images enables automated analysis and offers opportunities to support clinicians with more consistent and objective diagnostic tools. This study aims to enhance cancer diagnosis by proposing a hybrid framework that integrates deep-learning-based histopathological image analysis with Whispering Gallery Mode (WGM) optical sensing for complementary tissue characterization. Methods: The proposed framework combines automated tumor classification from histopathological images with biochemical signal analysis obtained from WGM optical sensors. Deep learning models, including EfficientNet-B0, InceptionV3, and Vision Transformer (ViT), were employed for binary and multi-class tumor classification using the BreakHis dataset. To address class imbalance, a Deep Convolutional Generative Adversarial Network (DCGAN) was utilized to generate synthetic histopathological images alongside conventional data augmentation techniques. In parallel, WGM optical sensors were incorporated to capture subtle tissue-specific signatures, with machine learning algorithms enabling automated feature extraction and classification of the acquired signals. Results: In multi-class classification, InceptionV3 combined with DCGAN-based augmentation achieved an accuracy of 94.45%, while binary classification reached 96.49%. Fine-tuned Vision Transformer models achieved a higher classification accuracy of 98% on the BreakHis dataset. The integration of WGM optical sensing provided additional biochemical information, offering complementary insights to image-based analysis and supporting more robust diagnostic decision-making. Conclusions: The proposed hybrid framework demonstrates the potential of combining deep-learning-based histopathological image analysis with WGM optical sensing to improve the accuracy and reliability of cancer classification. By integrating morphological and biochemical information, the framework offers a promising approach for enhanced, objective, and supportive cancer diagnostic systems. Full article

The revitalization of vitality in historic cultural districts can enhance a city’s cultural attractiveness and promote the upgrading of the urban cultural industry and sustainable development. Revealing the threshold and synergistic effects of different districts’ scene elements on district vitality helps to identify the distribution patterns of district vitality and provides a basis for managerial decision-making. This study first uses a geographic information system (ArcGIS) to overlay Baidu heatmaps with the street-network distribution in order to depict the spatiotemporal heterogeneity of district vitality and to compute vitality values by partitions at the district scale. Subsequently, based on an explanatory framework that integrates the physical space and subjective cognition, multi-source data such as street-view panoramas and points of interest (POIs) are quantified to obtain scene-element values for each unit area. Then, the scene-element values and vitality values are integrated into a consolidated database. Additionally, the LightGBM model and the SHAP method are employed to evaluate each element’s marginal contribution and relative importance to district vitality, thereby screening out the key scene elements. Finally, by means of SHAP dependence plots and interaction-effect analysis, the threshold intervals of the key elements and their synergistic relationships are identified, revealing the nonlinear threshold effects and synergies by which scene elements influence spatial vitality. The results show that during rest days, district vitality exhibits stronger diffusion, and the synergistic effect between Leisure-Facility Attractiveness and Street-Network Accessibility is the most prominent in enhancing vitality. High Exhibition-Facility Attractiveness is difficult to sustain crowds on its own; only when Leisure-Facility Attractiveness is likewise high does its effectiveness increase significantly. When Transport Accessibility is within the 0.20–0.40 interval, the positive effect of Leisure-Facility Attractiveness is significantly amplified. An excessive Traditional–Modern Facility Mix readily leads to homogenization of districts; therefore, when introducing modern business formats, local cultural characteristics must be retained. Overall, the generation of district vitality relies more on the synergy between material factors and subjective cognition than on improvements to any single element. The findings of this study provide suggestions for the planning of scene elements and the enhancement of vitality in historic cultural districts. Full article

As the manufacturing industry continues to advance its digital transformation, intelligent sensing technology has become a key support for achieving precise, efficient and automated quality control. However, current production line monitoring systems predominantly rely on fixed and costly monitoring equipment and sensors, lacking flexible and interactive first-person perspective perception approaches centered on on-site operators. Meanwhile, factory process monitoring often depends solely on visual expression rather than balancing the capabilities of the simulation model and visual state detection, leading to delayed responses to abnormal systems and hindering the adjustment strategy feedback. To address these limitations, this study provides wearable sensing for key workers, enriching the state perception capabilities in industrial scenarios. Furthermore, to achieve dynamic model and real-time visual representation of production line operations, a multi-source information-enhanced Petri nets model is proposed in terms of engineering and user-friendliness. With the solid mathematical basics of the Petri nets and the enriched human–machine data from the product line, this method provides an intuitive, dynamic and accurate reflection of the production system’s real-time operational status, offering a scientific and reliable basis for operational decision-making. The proposed approach has been implemented in a real-world production system for reinforced concrete civil defense doors, and this engineering application can also be extended to many other scenarios. Full article

Global agricultural production is severely threatened by the intensification of crop diseases and pests. Traditional pesticide application methods, characterized by inefficiency and frequent phytotoxicity, necessitate the urgent development of smart plant protection technologies that feature precision, dosage reduction, and high efficiency. This study focuses on the core component of intelligent decision-making, systematically delineating the technological trajectory of the field through a three-tier analytical framework: “model evolution–system integration–application form.” Analysis reveals that decision-making models have transitioned from rule-driven and data-driven approaches to fusion-driven paradigms. This evolution marks a shift from the codification of empirical experience to data learning, culminating in the synergistic integration of multi-source information and domain knowledge. At the system application level, the core technical architecture—comprising multi-dimensional information sensing, real-time edge computing, and precise control execution—has facilitated the translation of intelligent pesticide application from laboratory settings to field deployment. Future decision-making systems are projected to evolve towards causal understanding, cluster collaboration, and ubiquitous service, providing critical technical support for the green transformation and sustainable development of agriculture. Full article

Integrating cover crops (CCs) into crop rotations has gained interest in the Southeastern United States due to the benefits that CCs offer, which improve soil health for agricultural production. However, more information is needed on how CCs may affect the development of soil water repellency (SWR), which can negatively impact soil hydrology. The development of SWR threatens crop yields, food security, and farmer livelihoods. To address this knowledge gap, a field experiment measured the water repellency (WR) of four common CC species and a fallow treatment. CC samples were oven-dried, ground, and analyzed for WR using the water drop penetration time (WDPT) test. The mean WDPTs of the CC residues collected at termination and four weeks post-termination ranged from 49 to 4174 and 8 to 2627 s, respectively. Large WDPTs (>5 s) indicate that CC residues can potentially influence the development of SWR. All CC residues exhibited WR. The results suggest that farmers may need to consider alternative CC species depending on when they plant their cash crops in relation to CC termination. Considering the effects of CCs on SWR will enable farmers to make informed management decisions to mitigate SWR development and maintain soil health in a changing climate. Full article

Ecological resettlement (ER), or conservation-led displacement, is widely implemented to safeguard biodiversity but often produces complex socio-ecological outcomes. This study assessed the environmental justice (both social and ecological) impacts of ER in Nepal’s Terai Arc Landscape (TAL) using an enhanced (including social, ecological, and environmental aspects) environmental justice (EJ) framework. Data were collected from 240 households across all resettled villages within the Chitwan and Parsa National Parks (NPs) of Nepal through household interviews, key informant interviews, focus groups, and field observations, supplemented by policy reviews, reports, and unpublished documents. Household demographics indicated an average family size of 5.5, gender parity (664 females, 658 males), and diverse caste/ethnic composition (ethnic: 146 households; higher caste: 64; lower caste: 6). Wealth distribution and literacy were uneven, with disparities in land ownership, assets, and social positions. Social and ecological justice outcomes were analysed using chi-square and McNemar tests. We observed a significant difference (p < 0.05) in substantive justice (food, shelter, clothing, and security) attributes before and after the resettlements. Similarly, significant improvements post-resettlement were observed in procedural and recognition justice: participation in decision-making increased from 43% to 62% (χ² = 12.34, p < 0.05). However, recognition of Indigenous knowledge and FPIC rights remained low, with 93% of households reporting inadequate acknowledgment (χ² = 198.5, p < 0.05). Distributive justice indicators, including access to compensation and forest resources, showed mixed outcomes, with 52% reporting fair compensation and 48% citing inequities (p < 0.05). Ecological outcomes also shifted significantly: forest cover decreased in 65% of surveyed areas post-resettlement, while grassland extent increased in 28% (χ² = 27.4, p < 0.05). Water source accessibility declined for 48% of households (χ² = 21.6, p < 0.05), and bushfire incidence decreased by 15% (χ² = 9.8, p < 0.05). Composite scoring revealed strong linkages between social justice deficits and ecological downturn in the resettled areas, suggesting that inadequate participation, recognition, inequitable compensation, and ecological degradation shift the issues from parks to the outside and exacerbate environmental vulnerability. These findings demonstrate that ER can achieve partial ecological objectives inside the parks but often perpetuates social inequities and ecological downturn in the resettled areas, undermining the long-term sustainability of the socio-ecological landscape. The study highlights the critical need to integrate social justice, participatory governance, and ecological monitoring into resettlement planning. Future policies should be grounded in the understanding that conservation effectiveness and social equity are mutually reinforcing, and that ignoring justice dimensions risks undermining both biodiversity outcomes and human wellbeing. Full article

With the rapid development of integrated energy systems (IES) towards integration and marketization, the collaborative planning of multi-energy entities has become a research hotspot. However, in real-world market environments, various energy entities often face information asymmetry and competitive interests, posing significant challenges to the optimal scheduling of the system. To address the incomplete information and competitive constraints among multiple energy hubs (EH) within IES, this paper constructs a multi-entity game planning model that accounts for environmental costs and carbon trading mechanisms. The model employs Bayesian game methods to handle the incomplete information among EH and analyzes the dynamic interactive behaviors of market entities under different strategies through multilateral incomplete information evolutionary game theory. Meanwhile, this paper incorporates carbon trading mechanisms along with the coupling technologies of power-to-gas (P2G) and carbon capture systems (CCS) to balance the economic efficiency and environmental protection. Additionally, in response to investment uncertainty, the real options theory is utilized for evaluation, and then a multi-entity incomplete information planning model is constructed, which is solved by using a nested algorithm proposed in this paper. This approach balances the interests of various entities and enhances the comprehensive long-term investment returns considering options. Simulation results demonstrate that the model effectively reflects the game behaviors among multi-energy entities under incomplete information, yielding optimized scheduling solutions that closely align with real-world scenarios. It improves economic benefits while reducing environmental pollution, providing theoretical foundations and methodological support for the planning of integrated energy systems involving multiple entities in electricity market environments. Full article

Healthcare visualization has become a crucial approach for interpreting complex medical data, supporting informed clinical decision-making, and enhancing public health management. However, existing reviews tend to focus on specific technologies or application scenarios, offering limited insight into the field’s overall knowledge structure, developmental trajectory, and interdisciplinary integration. To address this gap, this study systematically reviews 1121 publications from 1994 to 2025 indexed in the Web of Science Core Collection. By combining bibliometric analysis with qualitative assessment, it maps the field’s evolution and underlying research paradigms. The findings reveal a clear shift from early innovation in technical tools toward the realization of clinical value, giving rise to an integrated research system that connects technology, data, clinical practice, and public health. Recent research has progressed beyond initial explorations of medical imaging, standalone devices, and isolated techniques, moving instead toward core domains such as immersive medical visualization, medical data visualization and analytics, health information systems and decision support, AI-assisted epidemic prediction and diagnosis, and integrated IoT-based healthcare frameworks. Looking ahead, an assessment of future trends suggests that, among other directions, the deep integration of explainable artificial intelligence (XAI) with visualization analysis, the development of IoT-driven real-time interactive systems, and the extension of visualization-enabled services from clinical applications toward inclusive population-level health coverage represent core driving forces for the future development of this field. These insights offer strategic guidance for future research, inform the design principles of next-generation visualization systems, and provide new models of interdisciplinary collaboration. The results also offer evidence-based support for health resource planning, technological innovation, and policy formulation. Full article

Objectives: To report the outcomes of a population of fetuses and neonates with life-limiting (LL) or life-threatening (LT) diagnoses leading to adverse prognoses cared for by a service of perinatal palliative care (PPC), the Percorso Giacomo (PG). Study design: This is a single center retrospective cohort study of all fetuses and neonates prenatally or postnatally diagnosed with LL or LT conditions whose families opted to continue the pregnancy at IRCCS Policlinico di Sant’Orsola in Bologna, Italy, from 2013 to 2025. Results: There were 83 fetuses and/or neonates including 64 diagnosed prenatally and 19 postnatally with annual significant increments in number. All families encountered the PG team. Overall, the cohort demonstrated a very high cumulative rate of comfort care plan (90%) with high rate of redirection of goals of care from intensive to palliative. Conclusions: PG showed a significant growth over 13 years suggesting the strong need of a service of PPC. The continuity of care provided by PG facilitated parental decision-making process towards redirection of goals of care. The outcomes observed provided valuable insights related to the wide range of prognoses for each diagnosis that will enable more informed counseling in the future. Full article

This study explores the interconnection between the variables of ChatGPT usage, energy consumption, water consumption, and carbon dioxide CO₂ emissions. A new integer and fractional order model using the Caputo derivative is proposed to comprehend the long-term dependencies of these variables. Boundedness, and global and local stability are examined for the fractional order model. The equilibrium points of these variables are shown to determine the stability of the model. The Runge–Kutta 7 numerical method is employed for the integer order model, whereas the semi-implicit linear interpolation (L1) method is used for the fractional order model. The parameter sensitivity is conducted on the system’s parameters to understand the variables’ impact by varying the relevant parameters for the system. To increase the efficacy of our analysis, we used machine learning approaches to model and predict the dynamics of CO₂ emissions, energy and water consumption, and ChatGPT usage. The Prophet ML model stood out among the other methods because it is adept at identifying long-term growth trends, seasonal changes, and the impact of outside variables in intricate time-series data. It is extremely beneficial for research centered on sustainability, where accurate projections are essential for wellinformed decision-making, because it can produce robust, interpretable forecasts against missing values and outliers. Using the Prophet ML model, our research guarantees precise and expandable predictions and provides valuable information that can direct tactics to balance environmental sustainability and technological progress. Full article