Search Results (4,987)

Temporal Knowledge Graph Inference (TKGI) is a cornerstone for intelligent decision-making in dynamic scenarios, but existing models face critical bottlenecks, including inadequate complex-context modeling, a lack of entity importance quantification, insufficient novel-event reasoning accuracy, and weak domain adaptability. To address these issues, this study proposes a semantics-enhanced model (LLM-DSaR) integrating Large Language Models (LLMs), temporal attention networks, and optimized contrastive learning. Specifically, a two-stage LLM semantic enhancement (LLM1 + LLM2) framework first generates structured semantic analysis reports via adaptive prompt engineering, and then extracts domain-specific semantic embeddings from the last-layer hidden states through pooling and linear projection, which are further fused with TransE-based structural embeddings; meanwhile, LLM2 mitigates data sparsity in novel-event reasoning; a dynamic weight fusion (DWF) framework adaptively assigns feature weights to achieve deep feature synergy; an LLM-enhanced contrastive-learning module strengthens event clustering and discrimination. Experiments on five public datasets and a self-constructed Robotics Temporal Knowledge Graph (RTKG) show LLM-DSaR outperforms 16 baselines: on RTKG, its MRR is 10.35 percentage points higher than GCR, and Hits@10 reaches 88.87%. Ablation experiments validate core modules’ effectiveness, confirming LLM-DSaR adapts to professional scenarios like robot maintenance prediction, providing a novel technical paradigm for complex-domain TKG reasoning. Full article

High renewable energy penetration creates significant operational challenges for power systems, especially during extreme weather that disrupts supply–demand balance. This study introduces a framework that integrates extreme scenario identification, data augmentation, and power balance optimization. It defines extreme wind speed events, such as sudden drops, surges, and persistent anomalies, and uses a sliding-window algorithm to extract these events from historical meteorological data. To address the scarcity of extreme samples, a new data augmentation method combines the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and iterative distribution shifting. This approach focuses the generated data on distribution tails while preserving diversity and temporal consistency. An optimization model, which includes various generation resources, energy storage, and load shedding, is developed to assess system flexibility under extreme conditions. Case studies on the projected 2030 Northeast China Power Grid show that the augmentation method expands extreme scenario datasets from 150 to 1000 samples, maintains extremity and temporal consistency, and reveals that wind curtailment rises sharply above 70% renewable share, with storage systems providing key flexibility in high-output scenarios. Full article

Antimicrobial resistance represents one of the most formidable global health crises of the 21st century, driven by the diminishing efficacy of conventional antibiotics due to bacterial adaptation and biofilm formation. In response, antimicrobial nanoformulations have emerged as a transformative therapeutic paradigm, offering multifaceted and innovative mechanisms to combat resistant pathogens. This comprehensive review delineates the broad scope and distinct novelty of nano-enabled antimicrobial strategies, moving beyond the single-target limitations of traditional drugs. We systematically explore the diverse architectural classes of nanoformulations—including metallic, polymeric, and self-assembling nanostructures—and elucidate their unique mechanistic actions. These encompass (1) physical disruption of microbial membranes via electrostatic interactions; (2) catalytic generation of reactive oxygen and nitrogen species to induce an ‘oxidative storm’; (3) intracellular sabotage of essential metabolic pathways; (4) the ‘Trojan horse’ strategy for enhanced drug delivery and bioavailability; (5) efflux pump bypass to counteract a major resistance mechanism; (6) penetration and eradication of resilient biofilms; and (7) disarming pathogens through quorum sensing and virulence inhibition. Furthermore, this review highlights the immunomodulatory potential of nanoformulations; their activity beyond bacteria against fungi, viruses, and parasites; and the critical role of the nano-bio interface defined by surface physicochemistry. We also address the translational pathway, considering challenges in nanotoxicology, scalability, and regulatory approval, alongside the ecological impact and economic horizon of these technologies. This sector is projected to reach USD 5.4 to 8.96 billion by 2033 to 2034, with compound annual growth rates of 11 to 21% across antimicrobial nanomaterials, nanocoatings, and nanomedicine applications. By integrating insights from computational modeling and in silico design, this review underscores how nanoformulations leverage synergistic, multi-target approaches to overcome resistance, enhance therapeutic efficacy, and represent a significant leap forward in the future of infectious disease management. The novelty lies in the holistic and mechanistic synthesis of how nanotechnology is redefining antimicrobial warfare, offering a promising arsenal to avert a post-antibiotic era. Full article

Neoliberal privatization models, emphasizing economic advancement over universal fairness, present considerable challenges to the urban regeneration process in post-conflict environments. The Solidere project in Beirut shows how architectural development in the Central District establishes social obstacles through its transformation of 1.8 million m² of war-destroyed territory. This research applies UNESCO’s Historic Urban Landscape (HUL) framework to distinguish regeneration from gentrification systematically and to assess the impact of privatized governance. By employing rigorous case study methodologies to assess master plans, legal statutes, corporate reports, and academic publications, four evaluation criteria for the HUL: historical layering, social participation, spatial connectivity, and physical integrity, were developed. The results show that while Solidere’s physical reconstruction was successful; it did not incorporate HUL principles fully. This resulted in the forced relocation of between 40,000 and 60,000 individuals, the commercialization of heritage through façadism, with 24% of the original buildings being preserved and 76% being destroyed. Sarajevo serves as a point of comparison, revealing the vulnerabilities of profit-driven approaches. The study shows that market-driven reconstruction efforts lacking public engagement will foster exclusionary gentrification, resulting in the erosion of urban identity and ownership, challenging neoliberal urban theories. Full article

In road planning and landscape design, data collection emphasizes existing site conditions, particularly in projects involving modifications rather than new construction, as such data directly inform subsequent planning decisions. Beyond conventional surveying techniques, large-scale street-region digital elevation models can be generated using aerial imagery acquired from unmanned aerial vehicles. The point clouds derived from these aerial photographs provide a basis for constructing spatial models applicable to street landscape and road planning. In this study, aerial data were processed using Pix4D software 4.8.4 to generate the initial spatial model, which was subsequently integrated into a building information modeling-based design framework in Autodesk Revit 2022. This approach enabled rapid and precise design outputs, while the resulting BIM model was further applied to mapping applications to establish a foundational database for regional public works. Full article

Human activities and climate change are influencing the survival and distribution of species, threatening the current distribution pattern of biodiversity and potentially leading to the “sixth mass extinction.” The forest musk deer (Moschus berezovskii) is among the most numerous and widely distributed musk deer species in China. However, its habitat is severely threatened by human activities and climate change. Due to the lack of field surveys and research data, it is difficult to assess the threats posed by human activities and climate change effectively. In this study, we integrate the new records of forest musk deer with climate and human activity data, and apply the MaxEnt species distribution model to evaluate the impact of human activities and climate change on the forest musk deer under current conditions and future scenarios (SSP1-2.6 and SSP5-8.5 for the 2030s, 2050s, and 2070s). Our results showed that the forest musk deer prefer areas with high vegetation cover (NDVI > 0.7), low GDP, and low levels of human activity disturbance. The areas of high-suitability habitats are 90.10 × 10⁴ km², 72.85 × 10⁴ km², and 30.43 × 10⁴ km², respectively. The optimal climatic conditions are an annual precipitation (BIO12) of 750–1500 mm and a seasonal temperature variation (BIO4) of 500–600. Their occurrence probability is highest at elevations between 1500 and 3000 m. Under the current climate conditions, the area of high-suitability habitats is estimated at 5.54 × 10⁴ km², primarily distributed across central–northern Sichuan, northwestern Guangxi, and southern Gansu. Under the future climate scenarios, low and medium-suitability habitats are projected to shrink to varying degrees, whereas the high-suitability area is expected to expand, particularly under the SSP5-8.5-2030s scenario where it is projected to increase by 2.88 × 10⁴ km². The centroid of suitable habitat is projected to shift toward higher-elevation areas in northwestern China, with regional hotspots emerging in southwestern regions such as central–northern Sichuan and northwestern Guangxi. These elevational and distributional shifts highlight the vulnerability of current habitats and the importance of adaptive conservation strategies to strengthen species protection, including continuously advancing forest protection programs, mitigating the impact of human activities in high-altitude areas, and strengthening the protection of key areas in the southwestern region. Full article

Indoor exposure to particulate matter (PM) is increasingly recognized as a major contributor to respiratory and cardiovascular risk, yet the relative contributions of outdoor pollution, building characteristics, and occupant behavior remain poorly resolved. PM₁ (aerodynamic diameter < 1 μm) warrants focus due to its higher alveolar deposition. “Evidence driven indoor air quality improvement” (EDIAQI) project aims to enhance indoor air quality guidelines and increase awareness by providing accessible data on exposure, pollution sources, and related risk factors. As part of the Zagreb pilot within the project, 103 paired indoor/outdoor PM₁ samples were analyzed. Seasonal analysis revealed substantial wintertime outdoor PM₁ spikes, while indoor medians remained stable. Chemometric analysis identified factors such as dwelling size, outdoor pollution, resuspension, building age/heating type, and urban context. Among the tested models, the validated gradient-boosted regressor (GBR) achieved the strongest performance, explaining ~65% variance in indoor PM₁ (test R² ≈ 0.65). Explainable machine learning analysis (SHAP) identified outdoor PM₁ levels, infiltration, and resuspension as the most influential predictors. Findings underscore wintertime outdoor emissions (e.g., residential heating and traffic) and dwelling-related and behavioral factors as key drivers, with the machine learning–environmental data integration enabling targeted residential IAQ management: optimized ventilation protocols, resuspension mitigation via behavior, and infiltration reduction through retrofits. Full article

In the 21st century, rapid urban development during global urbanization has led to high-density environments. These settings have become a significant cause of stress-related health problems for residents. Healing street design plays an important role in helping address mental health challenges caused by this process. Current research often focuses on healing elements and methods from only a single field. As a result, it lacks the integration of multidisciplinary and multi-stakeholder perspectives. To address this gap, this paper formed a Delphi expert panel with multidisciplinary scholars, urban managers, and practicing designers. The panel developed a quantitative evaluation model. This model covers four core dimensions: Safety (0.3210), Attractiveness (0.1080), Friendliness (0.2155), and Comfort (0.3553). It also includes eleven healing elements, such as Pedestrian Right-of-Way (0.4131), Night Lighting (0.3209), Visual Landscape (0.759), Street Furniture (0.4000), and Street Scale (0.3274). Using this model, the healing potential of Jingliu Road in Zhengzhou was assessed. The analysis identified the overall healing potential, core healing dimensions, and shortcomings of the street. This finding provides a clear direction for future healing-oriented street design. This paper builds a healing system for pedestrian spaces in high-density urban streets in China. It thus offers an evidence-based scientific tool for environmental design. Healing environments have expanded from less accessible spaces, such as squares and parks, to interactive and accessible streets. This transition enhances urban spaces’ capacity to address residents’ mental health concerns and promotes public health. Additionally, this paper offers specific recommendations for planners and policymakers to prioritize healing elements in urban renewal projects. Full article

Land use change has direct human impacts and profoundly alters the structure and function of terrestrial ecosystems. Numerous studies have explored land use change dynamics in the context of socio-economics, often overlooking the influence of climate change on the potential suitability of land use. To address this gap, we propose an integrated framework combining CLUE-S and MaxEnt models to analyze how land use in Tai’an City, Shandong Province, China, responds to future socio-economic and climate change scenarios. The CLUE-S model, based on land demand, and the MaxEnt model, based on suitability assessment, can effectively explore the trends of land change under the influence of human activities and global warming. This study maps the spatial distributions of land use under socio-economic development and four climate change pathways. Overall, the AUC values of the CLUE-S model were all greater than 0.7, and those of the MaxEnt model were all greater than 0.5, indicating that the results of both are relatively reliable. Our study reveals that, within the baseline development (BL) scenario, cultivated land, forest land, grassland, and unused land are projected to decrease between 2020 and 2040. Conversely, the expansion of water bodies and built land will keep growing. In addition, climate change is expected to enhance the suitability of cultivated land between 2020 and 2040, while reducing that of forest land, grassland, unused land, and built land, with only minimal effects on water bodies. Finally, our framework projected that the most widespread priority areas are cultivated land, followed by forest, grassland, water, built land, and unused land. These priority areas are largely determined by human activities, while the influence of climate change is relatively small. Our research framework has broad applicability to the other regions. Considering the MaxEnt model within the framework is beneficial for excluding unsuitable distribution areas of land use types in the CLUE-S model, which will provide new insights for the sustainable use of land resources. Full article

Accurate energy forecasting is essential for maintaining power system reliability, integrating renewable generation, and ensuring market stability. Although machine learning has improved forecasting accuracy, its operational deployment depends on Machine Learning Operations (MLOps) platforms that automate and scale the entire lifecycle of energy data pipelines. However, the capabilities of existing MLOps platforms for energy forecasting have not been systematically compared. This study adopts a PRISMA-informed review process to identify relevant end-to-end MLOps platforms for energy forecasting and then maps their documented capabilities using an established energy forecasting pipeline lifecycle as the reference structure. A total of 256 records were screened across vendor documentation, open-source repositories, and academic literature, of which 13 MLOps platforms were selected for comparative capability analysis. Platform capabilities are organised and presented across an end-to-end lifecycle covering project setup and governance, data ingestion and management, model development and experimentation, deployment and serving, and monitoring and feedback. Commercial platforms such as Amazon SageMaker and Google Vertex AI generally provide stronger end-to-end integration and production readiness, while open-source platforms such as Kubeflow and ClearML offer modular flexibility that typically requires additional integration effort to achieve end-to-end operation. The mapping identifies four priority areas where platform support remains limited, namely (i) governance workflow automation, (ii) automated data quality validation, (iii) feature management, and (iv) deployment and monitoring support under nonstationary conditions. These findings indicate that platform selection for energy forecasting should be treated as a lifecycle capability decision, balancing end-to-end integration, operational assurance, and long-term flexibility. Full article

Historical small towns (HSTs) embody irreplaceable cultural heritage and territorial identity, facing depopulation, economic marginalization, and infrastructure decay. Improving their liveability and attractiveness is essential to reverse these trends and boost sustainable development. In this context, HSTs are potential drivers of circular and sustainable socio-technical systems, where the circular economy (CE) offers a framework for local sustainability. However, HSTs lack adequate sustainable CE implementation tools. This study, the culmination of the H-SMA-CE project, develops a Decision Support System (DSS) to assist local policymakers in planning CE transitions in Italian HSTs. The DSS integrates three building blocks: context analysis (metabolic flows, stakeholder networks), an intervention library with cost–benefit data, and a composite Municipal Circular Economy Index (MCEI). The tool enables users to assess baseline circularity, simulate scenarios, and identify optimal investment portfolios through multi-objective optimization. This approach allows for the simultaneous evaluation of the benefits of each sustainability aspect, i.e., environmental, economic and social. Tested on the municipality of Taurasi (Italy), an HST with a wine-based economy, the results show that balanced intervention strategies yield greater circularity improvements than single-objective approaches. The paper contributes to the discourse on digital tools for sustainability transitions, offering a replicable model for evidence-based CE governance in heritage-rich territorial contexts. Full article

To meet the requirements of quantitative elemental analysis in the ultraviolet (UV) spectrum, a UV single-photon imaging system was developed, integrating a digital micromirror device (DMD) and a single photon-counting imaging detector, enabling high sensitivity, high resolution, and a wide dynamic range. However, intrinsic geometric distortion poses a significant challenge to accurate spectral calibration. A hybrid correction framework is proposed, cascading polynomial coarse correction with multilayer perceptron (MLP) fine regression, improving calibration accuracy. The method utilizes a full-field dot-array mask projected by the DMD to acquire distortion-reference image pairs. The polynomial model rapidly captures the dominant high-order distortion, while a lightweight MLP performs non-parametric fine regression of residual displacements, achieving a mean error of 0.84 pixels. This approach reduces the root mean square (RMS) error to 1.01 pixels, outperforming traditional direct linear transformation (5.35 pixels) and pure polynomial models (1.33 pixels), while the nonlinearity index decreases from 0.35° to 0.05°. In addition, the method demonstrates stable performance across multi-scale checkerboard patterns ranging from 128 to 280 pixels, with RMS errors remaining around the 1-pixel level. These results validate the high-precision distortion suppression and robust cross-scale performance of the proposed framework. By leveraging DMD-generated patterns for self-calibration, this method eliminates the need for external targets, offering a scalable solution for high-end spectrometer calibration. Full article

Peatlands cover approximately 10% (640,000 ha) of Latvia’s territory, of which about 51,000 ha is officially classified as degraded due to peat extraction and related activities. This study assesses the current status of peat extraction site recultivation in Latvia and evaluates future after-use requirements under contrasting policy pathways using a review of scientific literature, project reports, national statistics, and updated peat extraction licence records. A simple allocation model was applied to estimate recultivation trajectories for the nationally defined degraded peatland area under two scenarios: (i) a licence-expiry baseline scenario and (ii) an accelerated immediate-stop-peat-mining scenario. The results show that full recultivation would require average annual efforts of approximately 1500 ha yr⁻¹ under the baseline scenario and around 2000 ha yr⁻¹ under the accelerated scenario. Although European Union-funded projects and corporate initiatives have demonstrated the potential of rewetting, paludiculture, and renewable energy integration, only a limited number of sites have been officially recognised as fully recultivated or restored. Because ecological recovery of peatland functions may take decades, administrative closure alone does not guarantee climate or biodiversity benefits. A phased recultivation strategy linked to licence expiry and prioritising degraded and self-regenerating sites emerges as the most pragmatic pathway for Latvia, balancing European Union climate objectives, institutional capacity, and socio-economic constraints. Full article

Manufacturing enterprises face increasing complexity in managing the complete life cycle of production systems, requiring integration of information from diverse sources to support timely maintenance, diagnostics, and operational decisions. This paper presents a comprehensive service-oriented architecture (SOA) for decision support in industrial life-cycle management, integrating real-time monitoring, predictive maintenance, and collaborative problem-solving across extended manufacturing enterprises. The architecture implements a three-layer service model comprising eight core collaborative services, three application services, and six life-cycle management services, orchestrated through a risk assessment module that monitors life-cycle parameters and triggers appropriate maintenance, diagnostics, or hazard prevention actions. The system was developed in the context of a European research project and validated in two industrial settings: automotive assembly lines at a German SME and air conditioning manufacturing at a Portuguese company. Results demonstrated substantial operational improvements, including reduced problem resolution time, lower diagnostic travel requirements, reduced spare-parts consumption, and increased structured problem registration. The original SOAP-based web-services implementation is further contextualized within the contemporary Industry 4.0 landscape through comparison with microservices architectures and discussion of integration paths involving OPC UA, Asset Administration Shells, and digital twins. The paper contributes a validated reference architecture for service-based industrial life-cycle management and clarifies its relevance as an early precursor of contemporary smart manufacturing approaches. Full article

Since At-Turaif’s inscription as a World Heritage Site in 2010, Al-Diriyah and its peripheries have witnessed massive urban development. With the recently proposed Wadi Safar project, the expansion of Al-Diriyah has taken another turn, as it is conceptualized as a luxury driven mixed-use district, integrating cultural experiences that are rooted in the past. This research examines the urban development of Al-Diriyah through the lens of the Experience Economy Model (1998), in which value is derived not just from objects or spaces but from the memorable and immersive experiences they tend to incorporate. This study employs a qualitative-case study methodology structured through a five-phase analytical framework that spans from 2010 to 2025/2030. Utilizing a deductive qualitative approach, the analysis demonstrates a differentiated application of the four experiential realms of the Experience Economy Model across the study sites. While At-Turaif predominantly engages two experiential dimensions and the broader regeneration of Al-Diriyah incorporates three, the planned development of Wadi Safar is designed to encompass all four dimensions of the Experience Economy. This configuration produces a balanced spectrum of active and passive participation as well as absorption and immersion, positioning Wadi Safar within Al-Diriyah’s broader transformation into the world’s largest heritage-led urban development. The findings contribute to the theme of a thriving economy of KSA Vision 2030 by advancing heritage-oriented experience as a pathway towards economic diversification. Full article