Search Results (6,432)

Ammonia (NH₃) is a promising carbon-free energy carrier for low-carbon power generation. However, in turbulent ammonia–methane (NH₃-CH₄) premixed swirling flames, operating at lean conditions to limit NO_X, emissions can trigger strong thermoacoustic oscillations. This study investigates thermoacoustic oscillatory instability in an NH₃-CH₄ swirl-stabilized combustor using the chemiluminescence of CH*, OH*, and NH* over a wide range of ammonia fuel fraction (X_NH3). Combined spectral measurements and 2D chemiluminescence imaging are employed to obtain the global emission characteristics and spatial distributions of OH* and NH* in the UV band and CH* in the visible band. A custom-designed intensified CMOS (ICMOS) camera based on a high-gain UV–visible image intensifier with direct coupling is developed to enable sensitive OH* and NH* imaging (gain > 10⁴). Frequency analysis of continuous CH* imaging, together with morphology-based principal component analysis and k-means clustering of 46 image features, shows that oscillatory combustion occurs for X_NH3 < 0.40, whereas X_NH3 ≥ 0.40 leads to multimode, stable combustion. As X_NH3 increases, OH* and NH* fields progressively decouple from CH*, becoming more elongated and shifting downstream. These results demonstrate that UV radical chemiluminescence provides indispensable information on NH₃ reaction zones and should be combined with CH* diagnostics for reliable thermoacoustic analysis and control in practical NH₃-fueled combustion systems. Full article

This study proposes a multi-objective, multi-class explainable modeling framework to explain country performance profiles in PISA Mathematics (PISAM), Reading (PISAR), and Science (PISAS). Instead of treating PISA as a simple ranking, the study models each country’s Low/Medium/High-achieving class and asks which structural signals the model relies on when assigning a country to this class. To this end, the study combines governance quality (e.g., accountability, control of corruption, and political stability, etc.), economic and administrative capacity, and regional/institutional location in a single prediction pipeline and explains the resulting classifications with SHAP contributions conditional on class. While the findings do not point to a single, universal determinant, in mathematics, high-level profiles cluster around political stability, economic scale barriers, and regional location, along with governance indicators; in reading, economic capacity is explicitly integrated into this institutional core; and in science, in addition to these two dimensions, the shared institutional dynamics of regional blocs come into play. Furthermore, the study not only produces explanations but also quantitatively reports their reliability. The fit with the model output (Fidelity) and the traceability of the decision logic (Faithfulness) are 0.95/0.85 for PISAM, 0.89/0.92 for PISAR, and 0.89/0.89 for PISAS, which demonstrates high internal consistency and traceability of the decision process. Overall, the study reframes the PISA results not as isolated test scores but as structural profiles generated by the combination of governance, capacity, and region, revealing the policy-relevant levers behind “high performance” as a transparent and reproducible decision-making pipeline. This provides policymakers with an important roadmap for creating a sustainable education policy. Full article

This study proposes a stage-aware governance framework for large language models (LLMs) that structures human oversight and accountability across different decision stages in AI-assisted literature review systems. Large language models (LLMs) are increasingly embedded in systematic review workflows, yet how human oversight and accountability should be structured across different decision stages remains unclear. This study evaluates three LLMs in a controlled two-stage literature review workflow—title-and-abstract screening and eligibility assessment—using identical evidence inputs and fixed inclusion criteria, with outputs benchmarked against expert consensus under fully reproducible conditions with standardized prompts and comprehensive logging. While LLMs closely matched expert decisions during screening (precision 0.83–0.91; F1 up to 0.89; Cohen’s κ 0.65–0.85), performance degraded substantially at the eligibility stage (F1 0.58–0.65; κ 0.52–0.62), indicating increased epistemic uncertainty when fine-grained criteria must be inferred from abstract-level information. Importantly, disagreements clustered in borderline cases rather than random error, supporting a stage-aware governance approach in which LLMs automate high-throughput screening while inter-model disagreement is operationalized as an actionable uncertainty signal that triggers human oversight in more consequential decision stages. These findings highlight the need for explicit oversight thresholds, responsibility allocation, and auditability in the responsible deployment of AI-assisted decision systems for evidence synthesis. Full article

The Lanuoma sediment-hosted lead–zinc (Pb–Zn) deposit, situated in the central part of the Sanjiang base metal metallogenic belt (SMB) within the Changdu Basin, is hosted by Triassic Bolila Formation limestone. The source of metals and sulfur (S), as well as the ore-forming processes for the deposits in this belt, are contentious. To constrain the metal and sulfur sources and to define the ore-forming mechanism, we analyzed Zn, Pb, and S isotopes of sphalerite and robinsonite, as well as Zn isotopes of the host limestone and the metamorphic basement. Sphalerite shows homogeneous δ⁶⁶Zn values (−0.31‰ to −0.12‰; mean = −0.20‰). The calculated δ⁶⁶Zn of the ore-forming fluid (~0.00‰) matches that of the Triassic limestone, indicating a sedimentary Zn source (δ⁶⁶Zn = −0.11‰ to −0.09‰; average 0.00‰). Robinsonite displays a wider δ⁶⁶Zn range (−0.22‰ to 0.44‰), reflecting a mixture of sedimentary and metamorphic sources (δ⁶⁶Zn = 0.12‰ to 0.42‰; average 0.22‰). Lead isotopes of sphalerite are uniform (²⁰⁶Pb/²⁰⁴Pb = 19.041–19.079) and indicative of a sedimentary rock source, whereas robinsonite shows wide variation (²⁰⁶Pb/²⁰⁴Pb = 19.070–19.156) and linear trends between low- and high-radiogenic end-members, indicating mixed Pb sources from sedimentary rocks and metamorphic basement. Sulfur isotopic compositions of sulfides (δ³⁴S = −1.4‰ to 2.6‰; mean = −0.1‰) cluster near 0‰, consistent with a deep magmatic origin. A strong linear correlation between ²⁰⁶Pb/²⁰⁴Pb and δ⁶⁶Zn, coupled with a lack of correlation between both ²⁰⁶Pb/²⁰⁴Pb and δ³⁴S and δ⁶⁶Zn and δ³⁴S in the sulfides, indicates that Pb and Zn were derived from a common metal source, whereas sulfur originated from a distinct reservoir. Combined with previously published fluid inclusion, rare earth element, and multi-isotopic constraints, these results suggest that Pb–Zn mineralization at Lanuoma was controlled by a mixing between metal-rich basinal brines and sulfur-rich deep-sourced fluids, leading to sulfide precipitation dominated by open-space filling. This study provides new insights into the genesis and mineralization process of sediment-hosted Pb–Zn deposits in the Sanjiang metallogenic belt. Full article

Road transport across Asia is undergoing rapid motorisation and exemplifies growing road safety challenges, with rising accident rates closely linked to driver behaviour. Recent reports indicate that Vietnamese drivers often perceive risk as manageable and enforcement as inconsistent, contributing to habitual violations such as speeding, signal ignoring, and risky manoeuvres, particularly when traffic is light. Evidence shows that riders, especially young adults, feel confident controlling their vehicles and frequently disregard safety warnings. This study investigates traffic safety awareness among motorcyclists and car drivers in Hanoi, based on a questionnaire survey of 393 respondents. Principal Component Analysis (PCA) was used to group 11 attitudinal statements into key components influencing road safety perceptions, identifying five: non-compliance with traffic regulations (Component 1), aggressive driving behaviour (Component 2), traffic signal issues (Component 3), road quality and infrastructure (Component 4), and preventive measures (Component 5). Multiple Correspondence Analysis (MCA) and two-step cluster analysis (TCA) were then applied to determine user clusters by socio-demographic characteristics, producing three groups: young adults in employment riding motorcycles (Cluster 1), young adults in education riding motorcycles (Cluster 2), and mature adults in employment driving cars (Cluster 3). Finally, Multinomial Logistic Regression (MLR) was applied to assess variations in road safety perceptions across the different groups (clusters). Mature adults driving cars (Cluster 3) identified the first four components as significant, with Components 1 and 2 showing negative associations and Components 3 and 4 positive associations. Full article

Hybrid AC/DC microgrids (HMGs) are increasingly recognized as a solution for the transition toward future energy systems because they can combine the efficiency of DC networks with an AC system. Despite these advantages, HMGs still have challenges in protection, cybersecurity, and reliability. Conventional protection schemes often fail due to reduced fault currents and the dominance of power electronic converters in islanded or dynamically reconfigured topologies. At the same time, IEC 61850 protocols remain vulnerable to advanced cyberattacks such as Denial of Service (DoS), false data injection (FDIA), and man-in-the-middle (MITM), posing serious threats to the stability and operational security of intelligent power networks. Previous surveys have typically examined these challenges in isolation; however, this paper provides the first integrated review of HMG protection across three complementary dimensions: traditional protection schemes, cybersecurity threats, and artificial intelligence/machine learning (AI/ML)-based approaches. By analyzing more than 100 studies published between 2012 and 2024, we show that AI/ML methods in simulation environments can achieve detection accuracies of 95–98% with response times under 10 ms, while these values are case-specific and depend on the evaluation setting such as network scale, sampling configuration, noise levels, inverter control mode, and whether results are obtained in simulation, hardware in loop (HIL)/real-time digital simulator (RTDS), or field conditions. Nevertheless, the absence of standardized datasets and limited field validation remain key barriers to industrial adoption. Likewise, existing cybersecurity frameworks provide acceptable protection timing but lack resilience against emerging threats, while conventional methods underperform in clustered and islanded scenarios. Therefore, the future of HMG protection requires the integration of traditional schemes, resilient cybersecurity architectures, and explainable AI models, along with the development of benchmark datasets, hardware-in-the-loop validation, and implementation on platforms such as field-programmable gate array (FPGA) and μPMU. Full article

This paper proposes a collaborative optimization strategy of regenerative braking in heavy-duty electric logistics vehicles under complex driving conditions to improve energy recovery efficiency. Based on the actual operational data of 18-ton electric trucks in the southwestern region of China, three driving scenarios for heavy commercial vehicles are determined via the K-Means clustering algorithm. Key features are extracted using Recursive Feature Elimination and employed to train a Learning Vector Quantization neural network for precise real-time condition recognition. The identified driving condition parameters, including vehicle speed, remaining battery power, and braking force, collectively regulate the intensity of regenerative braking. Simulation results under double-WTVC (World Transient Vehicle Cycle) conditions indicate that the proposed strategy can effectively adapt regenerative braking behavior to diverse road conditions. In comparison with conventional control methods, this approach enhances battery energy recovery efficiency by 5.8% while preventing control discontinuities. Full article

Seismic monitoring is a crucial step in ensuring the safety and resilience of building structures. The implementation of effective monitoring systems, particularly across large-scale, complex building clusters, is currently hindered by the limitations of traditional sensor placement methods, which suffer from low efficiency, high subjectivity, and difficulties in replication. This paper proposes an innovative AI-based Automated Layout Method for seismic monitoring devices, leveraging building geometric recognition to provide a scalable, quantifiable, and reproducible engineering solution. The core methodology achieves full automation and quantification by innovatively employing a dual-channel approach (images and vectors) to parse architectural floor plans. It first converts complex geometric features—including corner coordinates, effective angles, and concavity/convexity attributes—into quantifiable deployment scoring and density functions. The method implements a multi-objective balanced control system by introducing advanced engineering metrics such as key floor assurance, central area weighting, spatial dispersion, vertical continuity, and torsional restraint. This approach ensures the final sensor configuration is scientifically rigorous and highly representative of the structure’s critical dynamic responses. Validation on both simple and complex Reinforced Concrete (RC) frame structures consistently demonstrates that the system successfully achieves a rational sensor allocation under budget constraints. The placement strategy is physically informed, concentrating sensors at critical floors (base, top, and mid-level) and strategically utilizing external corner points to maximize the capture of torsional and shear responses. Compared with traditional methods, the proposed approach has distinct advantages in automation, quantification, and adaptability to complex geometries. It generates a reproducible installation manifest (including coordinates, sensor types, and angle classification) that directly meets engineering implementation needs. This work provides a new, efficient technical pathway for establishing a systematic and sustainable seismic risk monitoring platform. Full article

The integrated pump-gate is a hydraulic facility that integrates a pumping station and a gate, playing a vital role in urban drainage systems, flood control, and other scenarios. Although integrated pump-gates are widely used, their internal flow presents different forms depending on the application scenarios, such as backflow, vortices, and cavitation. These effects markedly influence the pump’s hydraulic performance, operational stability, and overall reliability. This study investigates the cavitation characteristics and internal flow fields within the complex geometry of the integrated pump-gate and numerically simulates the cavitation phenomenon using the SST turbulence model. Specifically, the influence of the impeller, guide vanes, and structural supports on the cavitation performance and internal flow state was analyzed. The results show that the geometric characteristics of the impeller’s leading edge significantly influence the cavitation structure. Regarding cavitation performance, NPSH_c was determined to be 5.3 m. At the leading edge of the guide vanes, cavitation usually occurs at the axial diffusion position of the flow channel, and the degree of cavitation is affected by the relative position of the guide vanes and the impeller blades. The structural supports and protrusions significantly affect the vortex structures in the flow field, with protrusion-induced vortex clusters dominating the guide vane region. Full article

Identifying floating residual feed is a critical technology in recirculating aquaculture systems, aiding water-quality control and the development of intelligent feeding models. However, existing research is largely based on ideal indoor environments and lacks adaptability to complex outdoor scenarios. Moreover, current methods for this task often suffer from high computational costs, poor real-time performance, and limited recognition accuracy. To address these issues, this study first validates in outdoor aquaculture tanks that instance segmentation is more suitable than individual detection for handling clustered and adhesive feed residues. We therefore propose LMFF–YOLO, a lightweight multi-scale fusion feed segmentation model based on YOLOv8n-seg. This model achieves the first collaborative optimization of lightweight architecture and segmentation accuracy specifically tailored for outdoor residual feed segmentation tasks. To enhance recognition capability, we construct a network using a Context-Fusion Diffusion Pyramid Network (CFDPN) and a novel Multi-scale Feature Fusion Module (MFFM) to improve multi-scale and contextual feature capture, supplemented by an efficient local attention mechanism at the backbone’s end for refined local feature extraction. To reduce computational costs and improve real-time performance, the original C2f module is replaced with a C2f-Reparameterization vision block, and a shared-convolution local-focus lightweight segmentation head is designed. Experimental results show that LMFF–YOLO achieves an mAP50 of 87.1% (2.6% higher than YOLOv8n-seg), enabling more precise estimation of residual feed quantity. Coupled with a 19.1% and 20.0% reduction in parameters and FLOPs, this model provides a practical solution for real-time monitoring, supporting feed waste reduction and intelligent feeding strategies. Full article

Dermatophytosis is a common zoonotic fungal infection in companion animals, most frequently caused by Microsporum canis, while the geophilic species Nannizzia gypsea may occasionally infect cats. Conventional morphological identification of dermatophytes is often challenging due to phenotypic similarities, underscoring the importance of molecular confirmation. In this study, dermatophyte field isolates obtained from cats with suspected dermatophytosis were identified using cultural characteristics and Internal Transcribed Spacer (ITS) region sequencing. Phylogenetic analysis based on ITS sequences showed that the isolates were highly similar to each other and clustered closely with reference strains and previously reported dermatophyte strains from different geographical regions. Subsequently, the in vitro antifungal activity of a propolis extract collected from the Muğla region (Türkiye) was evaluated using the agar dilution method at concentrations ranging from 6.25 to 100 mg/mL. At all tested concentrations, propolis inhibited mycelial growth in all four molecularly confirmed dermatophyte field isolates, whereas substantial growth was observed in the negative control plates. These findings indicate that Muğla propolis exhibits in vitro antifungal activity at the tested concentrations against dermatophyte field isolates and warrants further investigation as a potential natural antifungal source. Full article

Bacterial canker, caused by Pseudomonas syringae pv. actinidiae (Psa) is a major threat to global kiwifruit production. Copper-based bactericides remain widely used, but increasing resistance highlights the urgency of developing sustainable alternatives. Understanding the functional capabilities of phyllosphere bacteria under copper pressure is critical for designing microbiome-informed management strategies. This study provides a culture-based functional inventory of bacteria associated with Actinidia chinensis var. deliciosa leaves from Portuguese orchards under long-term copper management, aiming to identify native taxa with traits relevant to plant health and resilience. A total of 1058 isolates were recovered and grouped into 261 Random Amplification of Polymorphic DNA (RAPD) clusters, representing 58 species across 29 genera. Representative strains were screened for Plant Growth-Promoting (PGP) traits (Indole-3-acetic acid (IAA), siderophore production, phosphate solubilization, ammonia production), copper tolerance, and in vitro antagonism against Psa. Copper resistance was widespread (53.3% of isolates with MIC ≥ 0.8 mM), including the first evidence of a highly copper-resistant PSA strain in Portuguese kiwifruit orchards and an exceptionally resistant non-pathogenic strain closely related to Erwinia iniecta (MIC 2.8 mM). A subset of 25 isolates combined all four PGP traits, and several also exhibited antagonism against Psa in vitro, among them Bacillus pumilus consistently supressed pathogen growth. Notably, antagonistic and multifunctional traits co-occurred in some isolates, highlighting promising candidates for integrated biocontrol strategies. Overall, the findings reveal a functionally diverse and copper-resilient collection of cultured bacteria, offering both challenges and opportunities for microbiome-based disease management. This work establishes a robust functional basis for subsequent in planta validation and the development of sustainable, microbiome-informed approaches for Psa control. Full article

Climate warming impacts arctic and subarctic lands, subjecting it to a generalized rise in soil temperature and causing changes in the surface cover. Land cover is a key control parameter for soil hydrothermal states, and its study by satellite imagery is necessary for monitoring boreal surface changes over time at large scales. Understanding the links between land cover and environmental conditions is also crucial to anticipate the impacts of atmospheric changes on continental surfaces. Sentinel-1 and Sentinel-2 data combined with a field campaign in July 2024 were used to produce a 10 m spatial resolution land cover map in the Abisko region, northern Sweden, covering 2180 km² and including three watersheds with an overall accuracy exceeding 94%. In parallel, temperature and precipitation fields were statistically downscaled at 100 m spatial resolution using topography, ordinary kriging based on weather stations and reanalysis. The relationships between surface areas and average summer temperature–precipitation clusters reveal that the vegetation distribution closely reflects the recent atmospheric conditions with the treeline following the 10.2 °C July–August isotherm in the considered area. This study provides a spatial basis for investigating the complex atmosphere–surface interactions and for assessing the sensitivity of boreal landscapes to ongoing climate warming. Full article

Automotive System-on-Chips (SoCs) must meet stringent functional safety standards, such as ISO 26262 and IEC 61508, to ensure reliable operation under hardware faults. FPGA-based fault injection has emerged as a practical and cost-effective technique for functional safety verification. However, instrumentation-based methods face scalability challenges when applied to the high fault densities typical of automotive SoCs. To address these challenges, we propose a hybrid cascaded fault-injection controller architecture (HCCA-SAFE) that simultaneously reduces high-fanout global nets and eliminates long serial propagation paths. The architecture constrains enable-signal cluster width and distributes control across cascaded stages, improving timing results and routability under limited FPGA resources. The proposed architecture is evaluated on multiple open-source RISC-V processor cores. On openE902, HCCA-SAFE reduces net delay from 27.276 ns to 22.535 ns and achieves 32.2% and 63.8% lower net delay compared with the representative centralized and shift-chain approaches, respectively. On openE906, the proposed HCCA-SAFE limits the net delay to 12.959 ns and reduces the maximum control-signal fanout to 1763, respectively, compared with 25.825 ns and 40.442 ns in the conventional method. On openC906, the proposed design lowers the maximum control-signal fanout from 7725 to 570 and reduces the net delay to 7.506 ns. Furthermore, HCCA-SAFE produces results fully consistent with software-based RTL simulation, while delivering substantial performance gains. Speed-up factors of $127\times$, $206\times$, and $2123\times$ are achieved on openE902, openE906, and openC906, respectively, with efficiency improvements scaling with processor complexity These results confirm that HCCA-SAFE delivers scalable, timing-robust fault-injection control suitable for large automotive SoCs. Full article

Signal intersections are key nodes in urban road traffic networks, and real-time queue length information serves as a core performance indicator for formulating effective signal management schemes in modern adaptive traffic signal control systems, thereby enhancing traffic efficiency. In this study, a roadside Light Detection and Ranging (LiDAR) sensor is employed to acquire 3D point cloud data of vehicles in the road space, which acts as an important method for queue length detection. However, during queue-length detection, vehicles in different lanes are prone to occlusion because of the straight-line propagation of laser beams. This paper proposes a queue-length detection method based on variations in vehicle point cloud features to address the occlusion of queue-end vehicles during detection. This method first preprocesses LiDAR point cloud data (including region-of-interest extraction, ground-point filtering, point cloud clustering, object association, and lane recognition) to detect real-time queue lengths across multiple lanes. Subsequently, the occlusion problem is categorized into complete occulusion and partial occlusion, and corresponding processing is performed to correct the detection results. The performance of the proposed queue length detection method was validated through experiments that collected real-world data from three urban road intersections in Suzhou. The results indicate that this method’s average accuracy can reach 99.3%. Furthermore, the effectiveness of the proposed occlusion handling method has been validated through experiments. Full article