Search Results (3,808)

Injection well placement and rate allocation are among the most decisive factors in determining the efficiency and bankability of CCS projects. However, optimizing these parameters is notoriously complex: even a small number of injection wells leads to a virtually infinite set of injection scenarios, while traditional optimization techniques typically require thousands of high-fidelity reservoir simulations. For project developers, this computational burden can stall critical Final Investment Decisions (FID). The approach proposed here addresses this bottleneck by using a Design of Experiments (DoE) framework combined with nonlinear surrogate modeling, which efficiently maps the relationship between injection rates and storage performance, to identify near-optimal solutions with a minimal number of simulations. We show that our method achieves up to 97% of the initially targeted CO₂ sequestration with as few as 15 simulations, demonstrating a step-change reduction in time and cost. From a business standpoint, CCS operators can de-risk projects earlier, accelerate FID timelines, and evaluate multiple site configurations in parallel while minimizing computational overhead. Rather than waiting weeks or months for exhaustive optimization, decision-makers can gain timely, reliable insights that directly support capacity commitments, regulatory submissions, and ultimately revenue realization. Full article

By 2050, the global population is expected to reach approximately 10 billion, leading to a projected 50% increase in food demand relative to 2013 levels. If not adequately anticipated, this growing demand will place significant strain on agri-food systems worldwide, with disproportionate impacts on low- and middle-income countries. Moreover, current projections may underestimate the accelerating effects of climate change, political instability, and civil unrest, which continue to disrupt food production and distribution systems. In this context, technological advancements offer a promising pathway to enhance efficiency, improve transparency, and mitigate risks related to food safety, adulteration, and counterfeiting. Emerging innovations can decouple food production from environmental degradation while strengthening monitoring, verification, and accountability across supply chains. This review examines state-of-the-art technologies developed to support traceability and anti-counterfeiting in agri-food supply chains, considering their application across the full spectrum of stakeholders. To provide a system-level perspective, the review adopts a five-layer socio-technical traceability and anti-counterfeiting framework, comprising identity, sensing, intelligence, integrity, and interaction layers, which is used to map enabling technologies and reinterpret the evolution of traceability systems (TS 1.0–TS 4.0) as a progression of functional capabilities rather than isolated technological upgrades. Using this framework, the review analyzes the advantages and limitations of current solutions and clarifies how traceability and anti-counterfeiting functions emerge through technology integration. It further identifies gaps that hinder large-scale and equitable adoption. Finally, future research directions are outlined to address current technical, economic, and governance challenges and to guide the development of more resilient, trustworthy, and sustainable agri-food traceability systems. Full article

Growing electricity demand and decarbonisation requirements pose significant challenges for coal-dependent transition economies. This study examines whether nuclear deployment can support low-carbon economic transformation using Poland’s national nuclear programme as a case study. We conduct a structured document analysis that integrates a systematic search and screening of peer-reviewed literature with an analysis of national policy and planning materials and a synthesis of publicly available project documentation for the Lubiatowo-Kopalino nuclear power plant, the Pątnów project, and the planned small modular reactor (SMR) deployments. Impacts on employment, infrastructure, technical education, technology transfer, and local supply chain participation are assessed and mapped to the sustainable development goals and the EU climate policy criteria. The analysis indicates that, if accompanied by early workforce development and supplier prequalification, nuclear investments can stimulate industrial upgrading, strengthen energy security, and deliver regional co-benefits beyond electricity generation. At the same time, scheduling slippage, governance uncertainty, and gaps in domestic capabilities in nuclear-specific components can limit these benefits. The article concludes with recommendations for national and local authorities on stakeholder engagement, local content strategy, and risk management that can be transferred to Central European economies with similar starting conditions. Full article

Gastrointestinal cancers account for roughly a quarter of global cancer incidence, and early detection through endoscopy has proven effective in reducing mortality. Multi-class endoscopic disease detection, however, faces three persistent challenges: feature redundancy from non-pathological content, severe illumination inconsistency across imaging modalities, and extreme scale variability with blurry boundaries. This paper introduces Endo-DET, a domain-specific detection framework addressing these challenges through three synergistic components. The Adaptive Lesion-Discriminative Filtering (ALDF) module achieves lesion-focused attention via sparse simplex projection, reducing complexity from $O\left(N^2\right)$ to $O\left(\alpha N^2\right)$. The Global–Local Illumination Modulation Neck (GLIM-Neck) enables illumination-aware multi-scale fusion through four cooperative mechanisms, maintaining stable performance across white-light endoscopy, narrow-band imaging, and chromoendoscopy. The Lesion-aware Unified Calibration and Illumination-robust Discrimination (LUCID) module uses dual-stream reciprocal modulation to integrate boundary-sensitive textures with global semantics while suppressing instrument artifacts. Experiments on EDD2020, Kvasir-SEG, PolypGen2021, and CVC-ClinicDB show that Endo-DET improves mAP50-95 over the DEIM baseline by 5.8, 10.8, 4.1, and 10.1 percentage points respectively, with mAP75 gains of 6.1, 10.3, 6.8, and 9.3 points, and Recall50-95 improvements of 10.9, 12.1, 11.1, and 11.5 points. Running at 330 FPS with TensorRT FP16 optimization, Endo-DET achieves consistent cross-dataset improvements while maintaining real-time capability, providing a methodological foundation for clinical computer-aided diagnosis. Full article

Reliability and maintenance serve as pivotal factors in safeguarding safety, enhancing efficiency, optimizing costs, and fostering sustainable development. They permeate all facets of industry, daily life, and society, thereby constituting a crucial foundation for achieving long-term, stable development. The rapid evolution of data-driven methods and artificial intelligence (AI) has revolutionized reliability and maintenance practices, driving a shift from reactive to predictive maintenance (PdM) and ultimately intelligent maintenance strategies. Unlike existing reviews that focus on single technologies or tasks, this paper adopts a system-level integration perspective to construct a closed-loop framework connecting data-driven reliability analysis, maintenance optimization, and intelligent decision-making. It further elucidates the integrated logic between prediction and decision-making through formalized mechanisms. This article systematically reviews the research progress and practical applications of data-driven methods and AI in reliability and maintenance. First, it classifies and summarizes data-driven reliability analysis methods based on existing literature. Second, a reliability-oriented maintenance optimization framework is proposed, comprehensively integrating economic, reliability, resource efficiency, and multi-objective collaboration considerations, while analyzing the characteristics of diverse maintenance systems. Furthermore, the innovative applications and performance advantages of AI algorithms in complex system maintenance are synthesized, and a comparative analysis of the applicability of different methods across various operational scenarios is conducted. And conducted a multidimensional comparison of the applicability scenarios for different methods from an engineering selection perspective. In addition, this review examines the current status and challenges of applying data-driven and AI technologies across multiple real industrial settings and identifies common obstacles encountered during project implementation. We further elucidate the research positioning of this work and provide a comparative discussion with existing review articles. Finally, the article conducts a bibliometric analysis to map the research landscape, provides quantitative support for the development trends in the field. Limitations in this field are also discussed. Full article

Safe train positioning is a key technology to make rail transportation more efficient and cost-effective. Within the EGNSS MATE project, the project partners SBB, DLR, and IABG researched the use of European Global Satellite Navigation Systems for this application. The main contributions are the development of a novel map-based sensor fusion algorithm, the development of a test catalogue for jamming and spoofing cyberthreats, and the collection of a large and rich dataset for testing and validation. The dataset includes over 200 h of sensor data and ground truth data, covering most of the Swiss normal gauge network. In addition, tests were conducted to assess the impact of jamming and spoofing attacks. Results show promising performance of the algorithms on most of the lines, excluding some long tunnels and sections with heavy multipath. The findings of the project results will help to introduce safe train positioning into ETCS by boosting development and standardisation efforts. Full article

This paper studies constrained model reference adaptive control (MRAC) for a planar unmanned ground vehicle (UGV) subject to actuator limits and safety requirements. First, we establish a double-integrator model by applying dynamic feedback linearization to a nonholonomic kinematic model with acceleration input, while simultaneously accounting for external disturbances. A constrained MRAC scheme is developed that enforces constraints at two levels: (i) actuator constraints are guaranteed by saturating the physical inputs after mapping the adaptive virtual control through the inverse kinematic transformation, and (ii) safety constraints are enforced via componentwise control barrier function (CBF) on the tracking error, which induces explicit bounds on the plant state. A projection-based adaptive law is introduced to keep parameter estimates bounded and to ensure well-posedness under saturation-induced mismatch. Moreover, we propose a sufficient feasibility condition that explicitly relates safety margins, disturbance bounds, and available actuator authority, thereby forming a guideline for feasible region design. Simulation studies demonstrate that the proposed method achieves constraint-satisfying tracking under bounded disturbances while respecting physical actuator constraints. Full article

The thermal consequences of industrial land transformation remain underexplored in rapidly urbanizing regions of Bangladesh. This study presents a novel approach of how extensive industrial expansion in Narayanganj, a major manufacturing hub dominated by textile, knitwear and dyeing industries, has altered land surface temperature (LST) dynamics over the past three decades, including its variation across classes, relationships with biophysical indices and future patterns. Landsat 5 TM and Landsat 8 OLI imagery from 1991, 2007, and 2023 were utilized to map LULC using winter-season images through supervised classification, while multi-seasonal thermal bands were used to derive LST. LST variations were further evaluated using cross-sectional profiles across different land cover types, and correlations were examined with indices including the greenness index (NDVI), moisture index (NDMI), built-up index (NDBI), and barrenness index (NDBAI). Additionally, a future LST map for 2039 was generated using the cellular automata–artificial neural network (CA-ANN) model. Results show that between 1991 and 2023, built-up area and bare land expanded by 16.72% and 14.15%, while vegetation area and water bodies decreased by 26.62% and 4.25%. Average LST increased from 25.94 °C in 1991 to 28.68 °C in 2023, with projections indicating an additional 2 °C rise by 2039. Cross-sectional analysis found that built-up areas consistently showed the maximum surface temperatures, followed by bare land, vegetation and water bodies. In addition, correlation analysis revealed that LST showed an inverse relation with NDVI and NDMI, while showing a positive relationship with NDBI and NDBAI. These findings show the necessity of sustainable urban planning and green infrastructure to reduce surface heating in rapidly urbanizing areas. Full article

Sea-level rise (SLR) and coastal flooding are among the most pressing climate-related challenges facing coastal regions worldwide, and their impacts are further intensified by rapid urbanization. These processes pose serious socioeconomic and environmental risks, including increased flood exposure, threats to public health, and damage to critical infrastructure. In Saudi Arabia, more than 3100 km² of coastal land lies at elevations of 1 m or lower; however, reliable assessments of future sea-level rise and its potential impacts remain limited, creating significant uncertainty for long-term planning. This study addresses this knowledge gap by identifying areas vulnerable to sea-level rise and coastal flooding through the development of inundation maps for the Dammam Metropolitan Area (DMA) as a case study, while also outlining potential adaptation measures. Using satellite imagery and geospatial datasets, changes in the DMA shoreline between 2014 and 2024 were analyzed, and sea-level rise scenarios were simulated based on projections from the Intergovernmental Panel on Climate Change (IPCC). The results indicate that under a 0.6 m sea-level rise scenario, flooding would be limited to a small area of approximately 0.2 km² in the Half-Moon residential district. In contrast, a 1.1 m sea-level rise scenario reveals a substantial increase in risk, with nearly 83 km² of the DMA potentially exposed to coastal flooding. Based on these findings, targeted disaster management and adaptation strategies are recommended for areas most vulnerable to sea-level rise. The study highlights the need for policies regulating coastal reclamation and other climate-sensitive developments to minimize future flood risks. It supports the United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action) by enhancing urban flood risk assessment and improving understanding of climate-driven sea-level rise impacts. Full article

Vision-based unmanned aerial vehicles (UAVs) have been widely studied and applied in aerial monitoring tasks; however, detecting small objects in UAV imagery remains challenging due to limited visual features, significant scale variations, dense distributions, and complex background interference. In real-world UAV scenarios, small objects often occupy only a few pixels and are easily obscured by cluttered backgrounds, which complicates stable and accurate detection. To address these issues, this study proposes MSCM-YOLO, a UAV-oriented lightweight detection framework based on YOLOv11. The framework integrates four key innovations: (1) a dedicated P2 detection head to preserve high-resolution features for extremely small and dense targets; (2) a lightweight backbone enhanced with Mobile Bottleneck Convolution (MBConv) to improve feature extraction for visually weak objects; (3) a Scale-Adaptive Attention Fusion (SAF) mechanism with a Channel-Adaptive Projection (CAP) module to effectively integrate multi-scale spatial and semantic features under large object-size variations; and (4) a Multi-Domain Feature Attention Fusion (MDFAF) module to enhance target–background discrimination in complex UAV scenes. Experiments on the VisDrone2019 dataset show that MSCM-YOLO achieves mAP50 and mAP50:95 scores of 44.41% and 27.13%, respectively, outperforming the YOLOv11 baseline by 10.77 and 7.22 percentage points. Notably, the proposed framework achieves this significant performance improvement while maintaining a balanced computational profile suitable for UAV deployment. Additional validation on the UAVDT, DIOR, and AI-TOD datasets confirms consistent improvements in mAP50, demonstrating the robustness and generalization ability of the proposed method. Overall, MSCM-YOLO provides an effective and practical solution for accurate small object detection in aerial monitoring applications. Full article

The damage status of shield machine disc cutters directly impacts the safety and efficiency of tunnelling projects. Current manual inspection methods involve high risks and low efficiency, while existing detection methods suffer from low accuracy and poor real-time performance in complex environments, often lacking quantitative analysis capabilities. To address these issues, this paper proposes an intelligent identification and quantitative assessment method for disc cutter damage based on the Deep Forest (DF) model. First, an eddy current sensor calibration platform was established, and a mapping relationship between output voltage and actual wear was developed through piecewise fitting to achieve precise wear quantification. In the data preprocessing stage, signal quality was improved via filtering, and typical damage features such as edge chipping, cracks, and eccentric wear were extracted using pulse edge detection. These feature segments were then resampled to construct the model training dataset. The DF model utilizes a hierarchical ensemble structure to mine data correlations, enabling accurate identification of four states: normal, edge chipping, eccentric wear, and cracks. Simultaneously, a DF regression model was employed to provide continuous quantitative predictions of damage size. Experimental results show that the classification model achieved accuracies of 98%, 96%, and 96% on the training, validation, and test sets, respectively, with weighted average F1-scores exceeding 0.96. The regression model achieved a coefficient of determination (R²) of 0.9940 and a root mean square error (RMSE) of 0.4051 on the test set. Both models demonstrate excellent performance and generalization, achieving full coverage from “qualitative state identification” to “quantitative wear assessment,” thereby providing reliable decision support for cutter maintenance and replacement. Full article

Drones have long been explored by commercial and military users for supply. While several systems offering small payloads in drone delivery have seen operational use, large-scale supply drones have yet to be adopted. A range of setbacks cause this, including technological and operational challenges that hinder their adoption. Here, we evaluate these challenges from a conceptual modelling perspective and forecast their applicability once these barriers are overcome. This study uses technology trend modelling and bibliometric activity mapping methodologies to predict the applicability of specific technologies that are currently identified as operational challenges. Specifically for supply drones, we model trends in technological improvements of battery technology and aircraft control, and project its focus on landing zone autonomy and powertrain. The prediction also focuses on the current state of hybrid power and higher levels of automation required for landing zone operations. These models are validated through several published case studies of small delivery drones and then applied to assess the feasibility and constraints of larger supply drones. A case study involving the conceptual design of a supply drone large enough to move a shipping container is presented to illustrate the critical technologies required to transition large supply drones from concept to operational reality. Key technologies required for large-scale supply drones have yet to build up a critical mass of research activity, particularly on landing zone autonomy and powertrain. Moreover, additional constraints beyond technological and operational challenges could include limitations in autonomy, certification hurdles, regulatory complexity, and the need for greater social trust and acceptance. Full article

The expansion of wind energy projects in Indigenous territories has intensified debates about the social legitimacy of corporate practices. In the Isthmus of Tehuantepec, Oaxaca, the main wind corridor in Mexico, wind farms coexist with deeply rooted Zapotec governance systems, creating a complex interface between corporate responsibility and community well-being. Based on a survey of 184 workers employed by wind companies in the region, this study examines the relationship between perceived Corporate Social Responsibility (CSR), in its ethical, legal, and philanthropic dimensions, and social and economic well-being. Using partial least squares structural equation modeling (PLS-SEM) and Importance–Performance Map Analysis (IPMA), we found that legal and philanthropic CSR significantly enhance both types of well-being, whereas ethical CSR only affects social well-being. These findings reflect the perspective of workers as hybrid actors, simultaneously employees and members of Zapotec communities, and should be interpreted in light of the study’s limitations: its focus on employed individuals, cross-sectional design, and reliance on self-reported perceptions. The results contribute to global debates on symbolic versus substantive CSR, distributive justice, and the risk of “green colonialism” in energy transitions. Full article

Optical inspection of highly reflective cylindrical components—such as stainless-steel vessels featuring both planar and curvilinear surfaces—presents significant challenges due to complex geometric distortions in single-pass imaging. This study proposes a line-scan imaging framework that integrates synchronized kinematic control with geometry-aware distortion correction. The system addresses shape deformations through three coordinated modules: (1) parametric synchronization between rotational motion and image acquisition ensures full-surface coverage; (2) scanline-specific 1D projective transformations correct perspective distortions on toroidal sidewalls; and (3) adaptive polar coordinate remapping restores radial symmetry on circular bases. Experimental results demonstrate subpixel-level geometric correction accuracy, validating the proposed framework’s effectiveness in eliminating geometric aberrations with low computational complexity and without reliance on data-driven training, while maintaining compatibility with defect detection and quantitative surface analysis of specular cylindrical specimens. Full article

Loop Closure Detection (LCD) is a key component of Simultaneous Localization and Mapping (SLAM) systems, responsible for correcting odometric drift and maintaining global consistency in localization and mapping. However, single-modality LCD methods suffer from inherent limitations: LiDAR-based approaches are affected by point cloud sparsity, limiting feature representation in unstructured environments, while vision-based methods are sensitive to illumination and weather variations, reducing robustness. To address these issues, this paper presents a LiDAR–vision multimodal fusion LCD algorithm. Spatiotemporal alignment between LiDAR point clouds and images is achieved through extrinsic calibration and timestamp interpolation to ensure cross-modal consistency. Harris corner detection and BRIEF descriptors are employed to extract visual features, and a LiDAR-projected sparse depth map is used to complete depth information, mapping 2D features into 3D space. A hybrid feature representation is then constructed by fusing LiDAR geometric triangle descriptors with visual BRIEF descriptors, enabling efficient loop candidate retrieval via hash indexing. Finally, an improved RANSAC algorithm performs geometric verification to enhance the robustness of relative pose estimation. Experiments on the KITTI and NCLT datasets show that the proposed method achieves average F1 scores of 85.28% and 77.63%, respectively, outperforming both unimodal and existing multimodal approaches. When integrated into a SLAM framework, it reduces the Absolute Error (ATE) RMSE by 11.2–16.4% compared with LiDAR-only methods, demonstrating improved loop detection accuracy and overall system robustness in complex environments. Full article