The risk of catastrophic flooding from sequential dam breaches in cascade reservoir systems has become increasingly critical under the influence of complex climate change and extreme geological events. In this study, a two-dimensional hydrodynamic dam-break model was developed to analyse flood propagation and inundation dynamics for the $RE1$, $RE2$, and $RE3$ cascade reservoirs in the lower Southwest China River Basin, considering various instantaneous full and partial collapse scenarios. Four distinct scenarios were simulated to evaluate breach characteristics and inundation impacts. Notably, Scenario 3-involving the simultaneous instantaneous full collapse of all three reservoirs-produced peak flow rates of 341,200 m$^3$/s, 1,157,900 m$^3$/s, and 340,100 m$^3$/s at $RE1$, $RE2$, and $RE3$, respectively. Under this worst-case scenario, maximum inundation depths at representative sites A, B, C, and D reached 69.51 m, 79.87 m, 77.16 m, and 48.38 m, with high-severity flooding areas extending over 0.95 km$^2$, 1.10 km$^2$, 1.21 km$^2$, and 1.73 km$^2$, respectively. In comparison, Scenarios 1 and 2 generated lower peak flow rates, smaller inundation areas, and less severe flooding, while Scenario 4-representing overtopping without structural breach-resulted in a substantial reduction of high-risk zones. The findings highlight the pronounced escalation of flood risk under simultaneous multi-reservoir collapse conditions and underscore the necessity for enhanced coordinated flood management and emergency response strategies in cascade reservoir systems. This study offers valuable insights into dam failure risk assessment, contributing to improved flood mitigation policies and emergency preparedness in regions vulnerable to extreme hydrological events.

Robusta coffee cultivation in Malinau Regency has been increasingly associated with forest land conversion, thereby intensifying the need for sustainable management practices that align with both environmental conservation and rural livelihood enhancement. To evaluate the sustainability of Robusta coffee farming systems, the sustainable livelihood framework was applied, focusing on five key livelihood capitals: natural, human, social, physical, and financial. A mixed-methods approach involving Multidimensional Scaling (MDS) and thematic analysis was employed to quantify sustainability levels and identify leverage points for strategic intervention. Results indicated that most capitals were classified as either “unsustainable” or “less sustainable.” Social capital demonstrated the lowest performance, with an index of 15.10, while financial capital followed at 20.88; both were categorized as “unsustainable.” Natural capital (26.13) and human capital (26.09) were deemed “less sustainable,” whereas physical capital showed relatively higher resilience with an index of 46.61, though still within the “less sustainable” threshold. Key constraints included insecure land tenure, underdeveloped infrastructure, limited social cohesion, and economic dependence on non-coffee income sources. Strategic interventions were proposed, including the certification of land ownership for 70% of coffee farmers within three years, the revitalization of farmer cooperatives to improve social capital, and the enhancement of rural infrastructure, particularly targeting 85% electricity coverage in coffee-producing areas by the second year. The integration of Geographical Indication (GI) certification with agroforestry-based production systems was identified as a pivotal strategy to reconcile ecological integrity with market competitiveness. By year four, price premiums of up to 40% in domestic markets and 60% in international markets were targeted through value addition and branding. These integrated measures are expected to reinforce livelihood resilience while promoting long-term socio-ecological sustainability in Malinau’s coffee landscapes.

A comprehensive bibliometric analysis was conducted to evaluate the evolution, thematic structure, and emerging trends in autonomous vehicle (AV) research. Scientific literature published up to 3 January 2025 was retrieved from the Web of Science (WoS), resulting in a corpus of 11,069 publications spanning 60 countries. Using VOSviewer software, a detailed examination was performed to map the intellectual structure of the field, including co-authorship patterns, citation networks, keyword co-occurrence, and institutional contributions. The findings revealed a marked increase in the volume of AV-related publications over time, indicating growing scholarly interest and investment in the domain. A total of 157 distinct scientific disciplines were identified, underscoring the inherently multidisciplinary nature of AV research, which encompasses fields such as computer science, robotics, transportation engineering, artificial intelligence, and socio-economic policy. The most prolific countries, institutions, and authors were visualised through citation and collaboration networks, revealing key contributors and international linkages. Particular emphasis was placed on the use of reinforcement learning and other machine learning methodologies in AV development, as reflected by keyword trends and thematic clustering. Additionally, attention was given to the broader socio-economic and managerial dimensions of AV adoption, including market dynamics, regulatory frameworks, and public acceptance. This analysis provides a rigorous and systematic overview of the current state of AV research and highlights potential avenues for future exploration. By synthesising large-scale bibliometric data, this study offers valuable insights for academics, policymakers, and industry stakeholders engaged in the evolving landscape of autonomous transportation systems.

Accurate detection of road surface potholes remains a persistent challenge due to environmental variability, inconsistent illumination, noise interference, and the complexity of road textures. Conventional detection methods frequently suffer from reduced performance when exposed to low-quality or noisy imagery, resulting in unreliable or delayed identification. To address these limitations, a robust and optimized image processing framework has been developed for real-time pothole detection under uncertain environmental conditions. The proposed approach employs a combination of advanced contrast enhancement techniques and adaptive convolutional processing to strengthen feature discrimination across heterogeneous road surfaces. To further improve detection reliability, a self-adaptive fuzzy refinement mechanism has been introduced, effectively delineating ambiguous or degraded regions often overlooked by deterministic methods. An energy-based functional is applied to model spatial and intensity gradients, enabling more precise localization of structural discontinuities indicative of pothole boundaries. The framework also incorporates computational optimization strategies to enhance processing speed without compromising accuracy, rendering it suitable for deployment in real-time autonomous or semi-autonomous road inspection systems. Thresholding and mask extraction operations have been systematically integrated to achieve accurate segmentation of pothole regions, even in the presence of substantial visual noise or occlusions. Experimental validations on benchmark datasets and real-world road imagery have demonstrated that the proposed method consistently outperforms existing state-of-the-art techniques with regard to detection accuracy, robustness to environmental disturbances, and computational efficiency. This approach presents a scalable and practical solution for intelligent transportation systems and automated infrastructure monitoring, contributing to improved road safety, timely maintenance, and cost-effective asset management.

The effectiveness of single-axis solar tracking in enhancing the performance of flat-plate solar collectors (FPSCs) has been widely acknowledged, particularly under clear-sky conditions. However, the precision of solar tracking systems—governed by the electro-mechanical transmission's discrete rotation step size—has a critical impact on energy yield. In this study, the influence of varying rotation step sizes on the incident solar irradiance received by flat-plate collectors with single-axis tracking (SAT) has been numerically investigated using the EnergyPlus simulation environment. Eight discrete step sizes—1°, 2°, 5°, 10°, 15°, 30°, 45°, and 90°—were examined under clear-sky conditions on July 26, using meteorological data specific to Kragujevac, Serbia. The tracking system was configured to follow the solar trajectory along the east–west (E–W) direction, rotating around a north–south (N–S) inclined axis. Results demonstrated that incident solar irradiance was significantly enhanced—by over 35%—when rotation step sizes ranged between 1° and 15°, compared to fixed (non-tracking) collectors. Slight reductions in performance were observed for step sizes of 30° (34.26% improvement) and 45° (32.95%), with the lowest gain (23.04%) associated with the coarsest resolution of 90°. Although dual-axis tracking (DAT) systems provide superior irradiance capture, single-axis systems offer substantial advantages in residential and small-scale applications due to their lower capital investment, simpler design, reduced maintenance requirements, and greater architectural integration potential. These findings underscore the importance of optimizing rotation step size in the design and deployment of cost-effective, energy-efficient solar tracking systems. In light of increasingly stringent energy performance directives within the European Union, the deployment of optimally configured SAT systems is expected to expand across the residential sector.

Image segmentation remains a foundational task in computer vision, remote sensing, medical imaging, and object detection, serving as a critical step in delineating object boundaries and extracting meaningful regions from complex visual data. However, conventional segmentation methods often exhibit limited robustness in the presence of noise, intensity inhomogeneity, and intricate region geometries. To address these challenges, a novel segmentation framework was developed, integrating fuzzy logic with geometric principles. Uncertainty and overlapping intensity distributions within regions were modeled through fuzzy membership functions, allowing for more flexible and resilient region characterization. Simultaneously, geometric principles—specifically image gradients and curvature—were incorporated to guide boundary evolution, thereby improving delineation precision. A fuzzy energy functional was constructed to jointly optimize region homogeneity, edge preservation, and boundary smoothness. This functional was minimized through an iterative level-set evolution process, allowing dynamic adaptation to varying image characteristics while maintaining computational efficiency. The proposed model demonstrated robust performance across diverse image modalities, including those with high noise levels and complex regional structures, outperforming traditional methods in terms of segmentation accuracy and stability. Its applicability to tasks demanding high-precision region-based analysis highlights its potential for widespread deployment in advanced imaging applications.