Search Results (360)

In climate adaptation plans, national sustainability strategies, and agency-level resilience frameworks, planetary limits are routinely acknowledged, yet proposed responses continue to center on expansion, replication, and scalability. This paper argues that this tension is not merely political or technical but grammatical. It reflects the dominance of the grammar of scale—a patterned way of organizing, evaluating, and legitimizing sustainability action through expansion, metrics, piloting, and exit. While indispensable in many contexts, scale increasingly struggles to secure durable ecological responsibility amid irreversibility, uneven exposure, and intergenerational harm. The paper advances a framework of plural sustainability grammars to diagnose this mismatch. In addition to scale, it identifies six alternative grammars—attachment, settlement, sufficiency, inheritance, exposure, and refusal—that already circulate, often implicitly, within sustainability discourse. Each grammar foregrounds dimensions of responsibility that scalability tends to background, including permanence, restraint, cumulative consequence, and ethical limits. The paper traces these grammars through climate adaptation planning frameworks across governance levels, showing how plural grammars are prominent in problem framing and diagnosis but are progressively narrowed as plans move toward implementation, monitoring, and accountability, where scale becomes dominant. The paper concludes by reflecting on the implications of this grammatical narrowing for practitioners, policymakers, and scholars concerned with adaptation, justice, and the governance of sustainability under planetary limits. Full article

To address challenges in complex group decision-making (GDM)—specifically preference fuzziness, intricate subgroup segmentation, and non-cooperative behavior—this study proposes an adaptive consensus model based on probabilistic linguistic term sets (PLTSs). By integrating fuzzy C-means (FCM) clustering with a Gaussian mixture model (GMM), a fuzzy Gaussian mixture model (FGMM) is constructed to achieve soft segmentation of expert preference distributions. On this basis, an adaptive consensus feedback mechanism is developed, which dynamically integrates interactive and automated adjustment strategies via multi-level consensus thresholds, thereby balancing decision efficiency and quality. To identify and control non-cooperative behaviors, a cooperation index and a three-tier management strategy, which incorporates intra-group negotiation, weight penalties and an exit-delegation mechanism, were introduced. In the case of strategic decision-making of new energy vehicles (NEV), after four rounds of feedback iterations, the group consensus level increased from the initial 0.316 to 0.804, reaching the preset threshold and verifying the effectiveness of the consensus mechanism. Compared with the existing literature methods, the framework in this paper achieves more comprehensive integration and innovation in four aspects: preference expression, clustering mechanism, consensus feedback and behavior management. Full article

Reliable steering regulation under varying road curvature and actuator constraints remains a central challenge in autonomous electric vehicles (AEVs). Many exiting approaches rely on reactive error correction or treat preview information solely as a reference adjustment, limiting anticipation and physical consistency. This study proposes a prediction–preview steering control (PSC) framework in which future curvature information within state propagation and constraint handling enables forward-looking steering decisions while respecting dynamic and actuator limits. The method is evaluated using a lateral-heading vehicle model with real-road geometric variation. Experimental results indicate significant improvement in tracking performance, reducing lateral RMSE from 0.1747 m to 0.0074 m with a maximum deviation of 0.0889 m and limiting heading RMSE to 0.0867° (maximum 1.2046°). Steering angle commands remain bounded within ±8.7°, while steering angle rate is maintained within 40–60°/s, ensuring smooth and dynamically admissible operation. The proposed strategy offers a computationally efficient solution for embedded AEV steering systems and demonstrates improved robustness under practical curvature transitions. Full article

Monocular 3D reconstruction remains a persistent challenge for autonomous driving systems in Degraded Visual Environments (DVEs) with extreme glare and low illumination, such as highway tunnels, due to the lack of reliable texture cues. This paper proposes a physics-aware deep learning framework that overcomes these limitations by fusing polarization sensing with conventional intensity imaging. Unlike traditional end-to-end data-driven fusion strategies, we propose a Modality-Aligned Parameter Injectionstrategy. By remapping the weight space of the input layer, this strategy achieves a smooth transfer of the pre-trained Vision Transformer (i.e., MiDaS) to multi-modal inputs. Its core advantage lies in the seamless integration of four-channel polarization geometric information while fully preserving the pre-trained semantic representation capabilities of the backbone network, thereby avoiding the overfitting risk associated with training from scratch on small-sample data. Furthermore, we design a Reliability-Aware Gating mechanism that dynamically re-weights appearance and geometric cues based on intensity saturation and the physical validity of polarization signals as measured by the Degree of Linear Polarization (DoLP). We validate the proposed method on our self-constructed POLAR-GLV benchmark, a real-world dataset collected specifically for high dynamic range tunnel scenarios. Extensive experiments demonstrate that our method consistently outperforms intensity-only baselines, reducing geometric reconstruction error by 24.2% in high-glare tunnel exit zones and 10.0% at tunnel entrances. Crucially, compared to multi-stream fusion architectures, these performance gains come with negligible additional computational cost, making the framework highly suitable for resource-constrained onboard inference environments. Full article

Reliable positioning and mapping in roadway tunnels are crucial for vehicle-based monitoring and inspection, especially considering the challenging environmental conditions such as rapidly changing illumination, low-texture environments, and repetitive structural elements. While general LiDAR-inertial odometry (LIO) frameworks and loop-closure detection methods are effective in general scenarios, they often suffer from severe drift or incorrect loop constraints under these specific conditions. These challenges are further exacerbated by the inherent uncertainties associated with low-cost sensors. This paper introduces a narrow field-of-view LiDAR-centric RTK-visual-inertial SLAM system enhanced by three key modules: semantic-assisted loop detection and matching, two-stage RTK quality control, and adaptive factor graph optimization (FGO). In the first module, the proposed semantic loop descriptor (SLD) matching is used to determine the potential loop closure locations and then integrates the corresponding constraint as graph nodes. The quality control module addresses RTK outlier rejection during tunnel entry and exit, employing an event-driven stochastic model to characterize the uncertainty between RTK and the other sensors, effectively suppressing RTK-induced errors. FGO module performs optimization by incorporating LIO, RTK, and loop closure factors, employing a keyframe-based strategy to produce globally optimized poses while continuously updating the map. The proposed Tunnel-SLAM was evaluated against state-of-the-art SLAM algorithms in four extended roadway tunnels, ranging in traveling distance approximately from 5 to 10 km. Experimental results demonstrate that the proposed SLAM achieved a final drift of less than 2 m with loop closure, demonstrating significantly reducing the drift, while other existing SLAM frameworks fail catastrophically or have large drift. Full article

DUR (Deep Underground Railways) stations, such as Suseo Station in Korea, present unique evacuation challenges stemming from multi-level spatial depth, long vertical circulation paths, and rapid smoke spread dynamics. Conventional design guidelines often fail to capture these complexities, underscoring the need for advanced, simulation-driven safety evaluation frameworks. This study proposes a comprehensive Digital Twin-based methodology that integrates spatial topology modeling, agent-based evacuation simulation, and dynamic hazard-aware routing. A multi-layer map topology was constructed from high-fidelity architectural geometry, decomposing the station into functional regions and encoding connectivity across platforms, concourses, corridors, and vertical circulation elements. Real-time hazard conditions were reflected through dynamic adjustments to edge weights, allowing evacuation paths to adapt to blocked exits, fire shutter operations, and smoke-infiltrated domains. Ten evacuation scenarios were developed to assess sensitivity to fire origin, exit availability, vertical circulation failures, and onboard passenger loads. Simulation results reveal that evacuation performance is primarily constrained by vertical circulation bottlenecks, with emergency stairways (E1 and E2) serving as critical choke points under high-density conditions. Cases involving exit closures or fire-compartment failures produced significant delays, frequently exceeding NFPA 130 and KRCODE performance criteria. Conversely, guided evacuation strategies demonstrated marked improvements, reducing congestion and enabling compliance with platform evacuation thresholds even in full-load scenarios. These findings highlight the necessity of transitioning from static design evaluations toward Digital Twin-enabled, predictive safety management. The proposed framework enables real-time visualization, intervention testing, and operator decision support, offering a scalable foundation for next-generation evacuation planning in extreme-depth railway infrastructures. Full article

This paper focuses on alternatives to the CAFC-NEV credits policy in the automotive industry of China. It considers a dual-channel supply chain consisting of a manufacturer and a retailer that can simultaneously produce and sell new energy vehicles (NEVs) and internal combustion engine vehicles (ICEVs). Differential game theory is employed to explore dynamic optimal decisions under CAFC-NEV credits and carbon credit policies. The results suggest that the strategies combining CAFC-NEV credits and carbon credit policies are equivalent to a single CAFC-NEV credits policy. Therefore, implementing the carbon credit policy on the basis of the CAFC-NEV credits policy does not affect the increase in NEV range. If the NEV credit score is below a certain threshold, the carbon credit policy will result in a higher range increase and brand goodwill of NEV. In the transition process of implementing the carbon credit policy based on CAFC-NEV credits and subsequently canceling the CAFC-NEV credit policy, the profits of supply chain members change slightly. The findings provide a theoretical basis for the timely exit of the CAFC-NEV credits policy. Full article

Cultural diversity holds an irreplaceable ecological value in biodiversity conservation. Jingxi is a county-level city in Baise City, Guangxi Zhuang Autonomous Region. In Jingxi, where the Zhuang ethnic group accounts for 99.4% of the population, a symbiotic relationship has developed between its unique ethnic culture and ecological environment. According to the 2017 census of old trees (OTs) in Jingxi, a total of 1361 OTs were recorded, of which 63.3% (865 trees) were concentrated in human settlements, including village entrances or exits, and cultivated lands, demonstrating significant spatial differentiation. This distinctive distribution pattern raises two core research questions: (1) What are the spatial distribution patterns of OTs within human settlements? (2) Do cultural factors play a significant role in OTs conservation? Therefore, an ethnobotanical study of OTs in Jingxi is necessary. The objectives of this study are to: (1) conduct a comprehensive ethnobotanical investigation of the OTs among the Zhuang people in the region; (2) summarize the environmental spaces of OTs based on their geographical locations; (3) analyze the symbolic cultural meaning associated with OTs across different environmental spaces. This study also aims to reveal conservation strategies for OTs from a cultural perspective and to integrate cultural values into biodiversity conservation, thereby providing significant insights into the mechanisms underlying cultural–ecological synergy. Full article

Shore power (SP) is a critical solution for decarbonizing maritime transport, yet its adoption is hindered by the “high investment, low utilization” paradox, driven by high initial costs and misaligned incentives between ports and ships. While government subsidies are essential, traditional static policy designs often fail to adapt to the complex, non-linear dynamics of technology diffusion. To address this, the study proposes a dynamic evaluation framework combining System Dynamics (SD) with Evolutionary Game Theory (EGT), embedding a Rolling Horizon Optimization algorithm. Using Shanghai Port as a case study, simulation results demonstrate that optimal subsidies are highly state-dependent. Specifically, effective promotion requires prioritizing ship-side incentives during the early start-up phase, followed by facilities subsidies supporting the coordinated evolution of both ships and berths, and finally a market-driven exit. Furthermore, the proposed dynamic strategy demonstrates superior robustness against oil price volatility and demand shocks compared to static policies, while strictly complying with fiscal budget caps. This framework provides a foundation for the adaptive management of green port infrastructure, facilitating the advancement of energy-saving and environmental protection initiatives within the maritime industry. Additionally, it contributes to the forecasting and evaluation of the policy outcomes of green technology adoption. Full article

Azithromycin remains an important agent in gonorrhea treatment, yet resistance is a growing global threat. To comprehensively define its genetic basis, we performed a large-scale genome-wide association study of 14,727 Neisseria gonorrhoeae genomes with linked azithromycin MICs from 66 countries. We identified 113 genetic variants significantly associated with elevated MICs. Beyond well-known mutations in 23S rRNA (A2059G, C2611T) and mtrCDE operon, we uncovered a broad repertoire of potential resistance determinants, including multiple amino acid substitutions in 16 ribosomal proteins (e.g., L2, L4, L13, L23) forming the nascent peptide exit tunnel (NPET), and porin PorB alterations (G120K, A121D/N). Systematic pairwise analysis revealed extensive synergistic interactions, particularly between variants affecting drug influx/efflux (PorB, MtrCDE) and ribosomal target affinity. Phylogenetic analysis identified successful, globally circulating lineages employing distinct resistance strategies: NPET-dominated, 23S rRNA-associated, and porin/efflux-mediated. Our findings demonstrate that azithromycin resistance is a polygenic trait shaped by functional complementarity and epistasis between target modification, membrane permeability, and efflux. This integrated model is essential for accurate resistance prediction from genomic data and highlights key lineages for focused surveillance. Full article

Early and accurate identification of causative microorganisms is essential for improving outcomes in peritoneal dialysis (PD)-associated peritonitis. However, nontuberculous mycobacterial (NTM) peritonitis remains difficult to diagnose and manage, often resulting in delayed treatment and unfavorable clinical outcomes. We conducted a scoping review to summarize the clinical features, microbiological profiles, treatment strategies, and outcomes of PD-associated NTM peritonitis. A total of 107 patients from 81 published reports were identified, including one patient treated at our institution. The mean age was 50.1 years, with a slight male predominance. Diabetes mellitus was the most common underlying cause of end-stage renal disease. Abdominal pain, fever, and cloudy dialysate were the most frequently reported symptoms, and exit-site infection was present in 55% of cases. Rapid-growing NTM species predominated, with Mycobacterium fortuitum being the most frequently identified organism. A substantial delay was observed between symptom onset and initiation of appropriate therapy. The mean duration of antimicrobial treatment was six months. PD catheters were removed in 90% of patients, and 69% were permanently transitioned to hemodialysis. The overall mortality rate during treatment was 18%. These findings suggest that NTM infection should be considered in cases of culture-negative peritonitis unresponsive to standard antibiotics. Early catheter removal combined with prolonged multidrug antimicrobial therapy for at least six months may be beneficial. In pediatric patients, temporary conversion to hemodialysis followed by PD catheter reinsertion or renal transplantation may represent a reasonable management option after successful infection control. Full article

This study investigates the machinability of Continuous Fiber-Reinforced Thermoplastic Composite (CFRTP) produced via Material Extrusion (MEX) additive manufacturing, focusing on drilling as a critical post-processing step in hybrid manufacturing. CFRTP components, fabricated from 3K carbon fibers and a PLA matrix, were subjected to systematic drilling tests under varying cutting speeds (50–110 m/min) and feed rates (0.06–0.24 mm/rev). Thrust force (Fz) and torque (Mz) were recorded using a high-precision dynamometer to evaluate the influence of cutting parameters on mechanical loads and damage mechanisms. Results indicate that increasing the feed rate significantly increases Fz and Mz, promoting fiber pull-out, delamination, and edge deformation, particularly at hole entry and exit regions. Conversely, higher cutting speeds reduce Fz and Mz due to thermal softening of the PLA matrix, enabling more controlled fiber–matrix interaction. Microscopic analyses revealed that damage severity correlates strongly with mechanical load levels. While high feed rates caused pronounced surface irregularities and matrix smearing, low feed rates combined with high cutting speeds yielded smoother hole morphology and preserved fiber–matrix integrity. The study concludes that optimal drilling conditions for CFRTP materials involve low feed rates and high cutting speeds, minimizing mechanical loads and suppressing damage formation. These findings provide a scientific basis for precision finishing strategies in hybrid manufacturing, enhancing dimensional accuracy and structural reliability of CFRTP components for advanced engineering applications. Full article

In urban environments, autonomous vehicles face critical challenges in localization and perception under extreme lighting conditions, including rapid illumination changes, high contrast, and nighttime low-light scenarios. To address the performance degradation of traditional LiDAR-inertial odometry systems under such conditions, this study proposes a high-precision FAST-LIO2-EC algorithm that fuses event cameras into the FAST-LIO2 framework. Event cameras, with their microsecond temporal resolution and 140 dB dynamic range, provide asynchronous edge information that complements LiDAR point clouds and IMU measurements. We validate the proposed system through real-world road tests conducted on public roads and closed test tracks, covering three typical extreme lighting scenarios: tunnel entrance/exit transitions, high-contrast shadow boundaries, and nighttime sparse-lighting conditions. The experimental platform is equipped with a 32-beam LiDAR, a 6-axis IMU, a DVS event camera, and an RTK-GNSS system for ground truth trajectory acquisition. Real-world results demonstrate that the FAST-LIO2-EC system achieves significant improvements in localization accuracy and robustness. In illumination change scenarios, the Absolute Trajectory Error (ATE) is reduced by 32.5% compared to the baseline FAST-LIO2 system, with zero tracking loss events. The point cloud quality is substantially enhanced, with more uniform distribution and clearer obstacle boundaries. In high-contrast scenarios, both systems maintain comparable performance with ATE below 0.15 m. However, in nighttime scenarios, the fusion system shows moderate improvement (15.3% ATE reduction) but reveals sensitivity to event camera noise, indicating the need for adaptive thresholding strategies. Supplementary simulation experiments validate the system’s robustness under varying speeds and sensor noise levels. This work provides a practical solution for autonomous vehicle deployment in complex urban lighting environments, with a comprehensive analysis of real-world performance boundaries and deployment considerations. Full article

Smart suspensions have significantly improved passenger comfort and vehicle stability compared to their passive counterparts. This manuscript explores the integration of artificial intelligence (AI) into hybrid suspension control systems to enhance vehicle stability and ride comfort under conditions where suspended mass changes. A one-quarter-vehicle model is employed to simulate and evaluate the performance of a hybrid control strategy, which combines skyhook and groundhook methods using a dynamic weighting factor ($\alpha$). This investigation considers an everyday situation where the sprung mass of a vehicle changes considerably when passengers enter or exit the automobile, impacting the suspension performance. Reinforcement learning techniques are utilized to optimize $\alpha$, achieving an acceptable balance between passenger comfort and vehicle stability. Simulation results show significant improvements in the dynamic response of the sprung mass compared to traditional passive systems, while keeping vehicle stability. Although improvements in road holding are incremental, simulation effort validates the AI-based hybrid system’s potential for refinement and practical application. Validation in MATLAB-Simulink demonstrates the system’s adaptability to varying road conditions and load distributions. The findings highlight the transformative role of AI in suspension control, paving the way for real-time implementation, advanced algorithms, and integration into full-vehicle models. This study contributes to the ongoing development of intelligent suspension systems toward vehicle performance advancement by improving passenger comfort and road holding. Full article

Extreme natural disasters raise a fundamental question for biologically rigid food production systems: does post-disaster productivity recovery stem from technological change or from adaptive reorganization within existing constraints? In inland aquaculture, where biological processes, fixed production cycles, and capital requirements severely limit short-run technological upgrading, this distinction is particularly critical. Using two post-earthquake time points (2023 and 2025), the analysis documents productivity and efficiency patterns rather than causal recovery trajectories. Accordingly, the analysis is explicitly descriptive and does not attempt to identify causal recovery mechanisms or long-run productivity dynamics. Adaptive efficiency is not directly measured in this study; rather, the term is used as an interpretative construct to describe efficiency changes that are consistent with adaptive behavior under post-disaster constraints. This study examines productivity patterns observed during the post-earthquake period in inland trout aquaculture following the 6 February 2023 earthquake in Eastern Türkiye, with a particular focus on adaptive efficiency as a recovery-consistent mechanism. Using a balanced panel of 290 inland trout farms observed during the immediate post-earthquake adjustment period (2023) and a subsequent recovery phase (2025), the analysis integrates bias-corrected Data Envelopment Analysis, Malmquist productivity decomposition, and resilience-oriented truncated regression. Recovery dynamics are examined conditional on farm survival, allowing within-farm adaptive adjustment to be distinguished from exit-driven selection effects. The results indicate that productivity recovery was driven predominantly by improvements in technical efficiency, while technological change remained close to unity across provinces, suggesting short-run production frontier stability. This pattern is consistent with delayed or constrained investment behavior under heightened uncertainty rather than with technological stagnation. This interpretation is not unique and should be read as one plausible mechanism among several, rather than as a definitive explanation of observed frontier stability. Farms primarily restored performance through operational reorganization, input coordination, and scale adjustment within existing biological and technological constraints, rather than through innovation. Second-stage results further show that the coefficient on access to liquidity is positive, while higher mortality rates and greater distance to markets are systematically associated with weaker post-disaster adjustment. Overall, the findings indicate that short- to medium-term productivity patterns in biologically rigid inland aquaculture systems are governed primarily by efficiency changes consistent with adaptive efficiency rather than technological change. From a policy perspective, post-disaster aquaculture recovery strategies should prioritize liquidity support, biological continuity, and operational stability over premature technology-push interventions. The analysis is based on two post-disaster observation points (2023 and 2025), which allows identification of short- to medium-term recovery-consistent patterns but does not permit causal or long-run inference. Full article