Search Results (29)

Welding is a critical joining process in civil and transportation infrastructure, enabling the fabrication of complex steel structural systems used in bridges, buildings, and other essential infrastructures. Despite strict adherence to established welding codes and standards, such as AWS D1.1 and AASHTO/AWS D1.5, welding flaws and service-induced defects can occur in welded components. Cause of defects and their structural impact, along with detection, sizing, and localization of these anomalies and flaws, are crucial for adequate maintenance, repair, or replacement planning without compromising the functionality of in-service components. Among available NDT techniques, ultrasonic testing (UT) remains one of the most widely adopted methods of weld inspection due to its depth of penetration, sensitivity to internal defects, and suitability for field deployment. Recent advancements in ultrasonic technologies, particularly Phased Array Ultrasonic Testing (PAUT), along with its emerging approaches such as Full Matrix Capture (FMC) and the Total Focusing Method (TFM), have significantly enhanced inspection accuracy, repeatability, and interpretability. These techniques enable flexile beam steering, multi-angle interrogation, and improved imaging of complex geometries. This paper presents a comprehensive review of PAUT for the inspection of welded steel infrastructure adhering to the recommendations and requirements of the relevant codes and standards, synthesizing the current literature on PAUT principles, wave modes, probe configurations, and data acquisition strategies. Emphasis is placed on the practical implementation of PAUT in civil infrastructure inspection, its advantages over conventional NDT methods, and its potential to support informed decisions related to quality acceptance, repair, and long-term maintenance planning. This paper concludes by identifying current challenges and future research directions for advanced ultrasonic inspection of welded steel structures. Full article

In the field of intelligent inspection, high-definition video data collected by quadruped robot dogs face severe transmission and storage constraints. Although existing advanced lossy video coding standards can significantly improve compression efficiency, they inevitably introduce severe compression artifacts in low-bit-rate scenarios. To address this issue, this paper proposes a video decoding quality enhancement network named Video Quality Restoration Network (VQRNet), based on a dual-stream architecture. Specifically, the Local Feature Extraction component incorporates a Progressive Feature Fusion Module (PFFM) with a four-stage progressive structure. By integrating reparameterized convolution and attention mechanisms, PFFM focuses on capturing high-frequency texture details to repair small-scale distortions. Simultaneously, the Multi-Scale Lightweight Spatial Attention Module (MLSA) performs spatial feature recalibration, leveraging multi-scale convolution to adaptively identify and enhance key spatial regions, specifically addressing multi-scale distortion. In the Global Feature Extraction component, the State-Space Attention Module (SSAM) combines State-Space Models (SSMs) with attention mechanisms to capture long-range dependencies and contextual information, for large-scale distortions caused by high-intensity compression. To verify the performance of the proposed algorithm, a dedicated dataset comprising 20 real-world video sequences captured by quadruped robot dogs (partitioned into 15 training and 5 testing sequences) was constructed, and the VTM 23.4 reference software was employed to simulate compression degradation using four quantization parameters (QP 30, 35, 40, and 45). Experimental results demonstrate that VQRNet outperforms state-of-the-art quality enhancement methods in terms of core metrics, including PSNR and SSIM, specifically including MIRNet, NAFNet, TRRHA, and CTNet. In the QP = 30 scenario, VQRNet achieves an average PSNR of 40.33 dB, a significant improvement of 3.32 dB over the VTM 23.4 baseline (37.01 dB), while demonstrating significant advantages in computational complexity and parameter efficiency—requiring only 5.27 G FLOPs and 1.40 M parameters, with an average inference latency of only 11.82 ms per 128 × 128 patch. This work provides robust technical support for the efficient video perception of quadruped robot dogs. Full article

Large Language Model (LLM) inference engines are becoming critical system infrastructure, yet their increasing architectural complexity makes defects difficult to be diagnosed and repaired. Existing reliability studies predominantly focus on model behavior or training frameworks, leaving inference engine bugs underexplored, especially in settings where execution-based debugging is impractical. We present a static, issue-centric approach for automated root cause analysis and repair suggestion generation for LLM inference engines. Based solely on issue reports and developer discussions, we construct a real-world defect dataset and annotate each issue with a semantic root cause category and affected system module. Leveraging text-based representations, our framework performs root cause classification and coarse-grained module localization without requiring code execution or specialized runtime environments. We further integrate structured repair patterns with a large language model to generate interpretable and actionable repair suggestions. Experiments on real-world issues concerning vLLMs demonstrate that our approach achieves effective root cause identification and module localization under limited and imbalanced data. A cross-engine evaluation further shows promising generalization to TensorRT-LLM. Human evaluation confirms that the generated repair suggestions are correct, useful, and clearly expressed. Our results indicate that static, issue-level analysis is a viable foundation for scalable debugging assistance in LLM inference engines. This work highlights the feasibility of static, issue-level defect analysis for complex LLM inference engines and explores automated debugging assistance techniques. The dataset and implementation will be publicly released to facilitate future research. Full article

Laser-cladding directed energy deposition enables both the repair and fabrication of complex metallic components with curved surfaces. However, during multi-axis deposition on curved substrates, sharp transient feed-rate fluctuations at corner segments—together with an approximately constant powder feed rate—readily cause local over-deposition and geometric defects (e.g., nodules and humps). These defects compromise surface-profile fidelity, thereby creating a major barrier to practical deployment. To overcome this limitation, we propose a corner-oriented path-optimization strategy based on geometric code parsing. By operating directly on the toolpath without modifying the Computer-Aided Design model or slicing workflow, the proposed method suppresses corner overbuild and associated morphological distortion in curved-surface directed energy deposition, substantially improving dimensional consistency and surface quality. Overall, this strategy provides a scalable and broadly applicable route toward high-precision, high-reliability, industrial-scale curved-surface additive manufacturing. Full article

This article examines how K-Pop Demon Hunters (2025) portrays women’s agency and sorority while curating Korean cultural specificity within the context of global streaming. Adopting a Gender Media Studies approach, the study conducts a scene-indexed close reading of nine key sequences, applying a coding scheme (co-presence, agency, solidarity, body framing, choreography–camera, colour) and a cultural-codes matrix that classifies elements as retained, hybridised, or globalised. Findings show a consistent pattern: when two or more women protagonists appear together, agency and sorority co-occur; this is visible in the narrative arcs and through full-body staging, ensemble composition, and a persistent we/together rhetoric. Korean local specificity is divided by purpose: English-led song hooks extend transnational reach; retained social anchors (space, ritual, foodways, and folklore) preserve locality; and hybridised cues (stylised folklore; idol/traditional blends) manage cultural density without erasure. Authorship and industry context align with this encoding, combining a women centred creative core and Korean cast with on-screen emphasis on women’s friendship, repair, and shared agency. Two tensions remain: traditional attire in spectacle numbers, and the narrow body diversity in the idol-slim body ideal, inviting comparative and interpretative scrutiny. Overall, the case demonstrates how an animated musical can emphasise women’s empowerment and cultural specificity without reducing either to mere marketing tools. Full article

Background/Objectives: Short inverted repeats (SIRs) are abundant DNA motifs capable of forming secondary structures, such as hairpins and cruciforms, that can induce genome instability. However, their mutational consequences in cancer, particularly in osteosarcoma (OS), remain largely unexplored. Methods: In this study, we systematically identified over 5.2 million SIRs in the human genome and analyzed their mutational patterns across six common cancer types. Results: We found that increased small insertion and deletion (INDEL) density within SIR spacer regions represents a consistent feature across cancers, whereas elevated single nucleotide variant (SNV) and structural breakpoint density is cancer-type specific. Integrating whole-genome sequencing data from 13 OS patients, we found that both SNVs and INDELs are significantly enriched within SIR spacer regions in OS. Notably, genomic regions with higher SIR density tend to accumulate more somatic mutations, suggesting a link between SIR abundance and local genome instability. SIR-associated mutations frequently occur in oncogenes and tumor suppressor genes, including TP53, NFATC2, MECOM, LRP1B, RB1, CNTNAP2, and PTPRD, as well as in long non-coding RNAs. Mutational signature analysis further suggests that defective DNA mismatch repair and homologous recombination may act in concert with SIR-induced DNA structural instability to drive OS development. Conclusions: Our findings highlight SIRs as mutational hotspots and potential drivers of osteosarcoma pathogenesis. Full article

This work quantifies downtime caused by cyberattacks for eight critical urban services in Milan by coupling sectoral Continuous-Time Markov Chains (CTMCs) with an approximately equal-area H3 hexagonal grid of the city. The pipeline ingests OpenStreetMap infrastructure, simulates coupled failure/repair dynamics across sectors (power, telecom, hospitals, ambulance stations, banks, ATMs, surveillance, and government offices), and reports availability, outage burden (area under the infected/down curve, or AUC), and multi-sector distress probabilities. Cross-sector dependencies (e.g., power→telecom) are modeled via a joint CTMC on sector up/down states; uncertainty is quantified with nested bootstraps (inner bands for stochastic variability, and outer bands for parameter uncertainty). Economic impacts use sector-specific cost priors with sensitivity analysis (PRCC). Spatial drivers are probed via hotspot mapping (Getis–Ord $G_i^{*}$, local Moran’s I) and spatial regression on interpretable covariates. In a baseline short decaying attack, healthcare remains the most available tier, while power and banks bear a higher burden; coupling increases P(≥$k\mathrm{sectors}\mathrm{down})$ and per-sector AUC relative to an independent counterfactual, with paired-bootstrap significance at $\alpha =0.05$ for ATMs, banks, hospitals, and ambulance stations. Government offices are borderline, and telecom shows the same direction of effect but is not significant at $\alpha =0.05$. Under a persistent/adaptive attacker, citywide downtime and P(≥2) rise substantially. Costs are dominated by telecom/bank/power under literature-informed penalties, and uncertainty in those unit costs explains most of the variance in total loss. Spatial analysis reveals statistically significant hotspots where exposure and dependency pressure are high, while a diversified local service mix appears protective. All code and plots are fully reproducible with open data. Full article

Locally Repairable Codes (LRCs) have become the dominant design in wide-stripe erasure coding storage systems due to their excellent locality and low repair bandwidth. In such systems, the repair degree—defined as the number of helper nodes contacted during data recovery—is a key performance metric. However, as stripe width increases, the probability of multiple simultaneous node failures grows, which significantly raises the repair degree in traditional LRCs. Addressing this challenge, we propose a new family of codes called TFR-LRCs (Locally Repairable Codes for balancing fault tolerance and repair efficiency). TFR-LRCs introduce flexible design choices that allow trade-offs between fault tolerance and repair degree: they can reduce the repair degree by slightly increasing storage overhead, or enhance fault tolerance by tolerating a slightly higher repair degree. We design a matrix-based construction to generate TFR-LRCs and evaluate their performance through extensive simulations. The results show that, under multiple failure scenarios, TFR-LRC reduces the repair degree by up to 35% compared with conventional LRCs, while preserving the original LRC structure. Moreover, under identical code parameters, TFR-LRC achieves improved fault tolerance, tolerating up to $g+2$ failures versus $g+1$ in conventional LRCs, with minimal additional cost. Notably, in maintenance mode, where entire racks may become temporarily unavailable, TFR-LRC demonstrates substantially better recovery efficiency compared to existing LRC schemes, making it a practical choice for real-world deployments. Full article

Many urbanised areas of the Apennines, in Italy, have complex soil stratifications. A typical example is the outskirts of the city of L’Aquila, which is founded on highly heterogeneous soil layers and was severely affected by a strong earthquake in 2009. In such conditions, shear wave velocity profiles (V_S) obtained from in situ tests such as the Seismic Dilatometer Marchetti Test (SDMT) provide reliable analyses of the local seismic response. This article presents the mono-dimensional (1D) and two-dimensional (2D) seismic response analyses conducted to characterise the soil foundation of the hospital complex and adjacent university buildings in L’Aquila before their seismic retrofitting. This study emphasises the importance of accurate soil characterisation prior to repair interventions, especially in deposits where there are V_S inversions and in the presence of geometrically irregular and large structures. Under these conditions, estimating the motion amplitudes of the deposit’s higher modes beyond the fundamental level is essential in accurately characterising the seismic response, especially for buildings where higher structural modes play a significant role. The results show that approximating the V_S profile with simplified procedures, as proposed by the Italian Building Code of 2018 (equivalent V_S, similar to average), leads to incorrect estimates of seismic action. Full article

In this paper, we generalize the construction of locally repairable codes (LRCs) by leveraging pairwise balanced designs (PBDs) and balanced incomplete block designs (BIBDs) to construct codes with nonuniform locality or nonuniform availability. Our constructions prioritize binary implementations for practical deployment while achieving optimal or near-optimal performance in terms of rate, minimum distance, and repair efficiency. Specifically, we propose distance-optimal LRCs with nonuniform localities and message-symbol $(r,t)$-availability. These binary constructions achieve optimal minimum distance under known bounds and have higher code rates than existing works. We also address open problems in the literature, including constructions where $r\nmid k$, and demonstrate that our constructions encompass or outperform several prior works. Full article

Detachable circular hollow sections (CHSs) offer an innovative solution to tackle the complexities of installation, maintenance, upgrades, and repairs in offshore monopile systems, particularly in challenging environments with limited access. As an alternative to traditional tubular joints, the PIP slip joint presents advantages in terms of ease of installation, time efficiency, and reduced susceptibility to failure. This study conducts an experimental investigation on PIP (Pile-in-Pile) slip joints under pure bending conditions, accompanied by comprehensive numerical analyses to examine the relationship between section slenderness, contact properties, and structural performance. The results highlight a strong correlation between force-displacement curves and include a comparison of compressive and tensile strain values for both experimental and numerical models. The experimental and numerical models showed strong agreement across all results, demonstrating the robustness of the findings. Additionally, numerical models were utilized to investigate various D/t ratios, revealing insights into the normalized moment, rotational capacity, and the impact of local buckling and contact mechanics. Furthermore, a comparison of these findings with established code guidelines, such as Eurocode and AISC-LRFD, has been conducted and reviewed in the context of this study. From analysis, it was found that the rise in the D/t ratio prompted a transformation in the buckling mode, which substantially altered the rotational ratio. This shift indicates the importance of understanding how these variables interact in engineering applications. These findings significantly enhance the understanding of PIP slip joints and emphasize their potential as a compelling alternative for offshore wind turbine support structures. Full article

Skeletal muscle satellite cells (SMSCs), a type of myogenic stem cell, play a pivotal role in postnatal muscle regeneration and repair in animals. Circular RNAs (circRNAs) are a distinct class of non-coding RNA molecules capable of regulating muscle development by modulating gene expression, acting as microRNAs, or serving as protein decoys. In this study, we identified circ_14820, an exonic transcript derived from adenosine triphosphatase family protein 2 (ATAD2), through initial RNA-Seq analysis. Importantly, overexpression of circ_14820 markedly enhanced the proliferation of goat SMSCs while concomitantly suppressing their differentiation. Moreover, circ_14820 exhibited predominant localization in the cytoplasm of SMSCs. Subsequent small RNA and mRNA sequencing of circ_14820-overexpressing SMSCs systematically elucidated the molecular regulatory mechanisms associated with circ_14820. Our preliminary findings suggest that the circ_14820-miR-206-CCND2 regulatory axis may govern the development of goat SMSCs. These discoveries contribute to a deeper understanding of circRNA-mediated mechanisms in regulating skeletal muscle development, thereby advancing our knowledge of muscle biology. Full article

Unmanned Aerial Vehicles (UAVs) have emerged as efficient tools in disaster-stricken areas, facilitating efficient data dissemination for post-disaster rescue operations. However, the limited onboard energy of UAVs imposes significant constraints on their operational lifespan, thereby presenting substantial challenges for efficient data dissemination. Therefore, this work investigates a data dissemination scheme to enhance the UAVs’ bandwidth efficiency in multi-UAV-enabled Internet of Vehicles, thereby reducing UAVs’ energy consumption and improving overall system performance when UAVs hover along designated flight trajectories for data dissemination. Specifically, first, we present a software-defined network-based framework for data dissemination in multi-UAV-enabled IoV. According to this framework, we formulate a problem called C2BS (Coding-based Cooperative Broadcast Scheduling) that focuses on optimizing the UAVs’ bandwidth efficiency by leveraging the combined benefits of coding and caching. Furthermore, we demonstrate the NP-hardness of the C2BS problem by employing a polynomial time reduction technique on the simultaneous matrix completion problem. Then, inspired by the benefits offered by genetic algorithms, we propose a novel approach called the Genetic algorithm-based Cooperative Scheduling (GCS) algorithm to address the C2BS problem. This approach encompasses a coding scheme for representing individuals, a fitness function for assessing individuals, operators (i.e., crossover and mutation) for generating offspring, a local search technique to enhance search performance, and a repair operator employed to rectify infeasible solutions. Additionally, we present an analysis of the time complexity for the GCS algorithm. Finally, we present a simulation model to evaluate the performance. Experimental findings provide evidence of the excellence of the proposed scheme. Full article

Software bugs are a noteworthy concern for developers and maintainers. When a failure is detected late, it costs more to be fixed. To repair the bug that caused the software failure, the location of the bug must first be known. The process of finding the defective source code elements that led to the failure of the software is called bug localization. Effective approaches for automatically locating bugs using bug reports are highly desirable, as they would reduce bug-fixing time, consequently lowering software maintenance costs. With the increasing size and complexity of software projects, manual bug localization methods have become complex, challenging, and time-consuming tasks, which motivates research on automated bug localization techniques. This paper introduces a novel bug localization model, which works on two levels. The first level localizes the buggy classes using an information retrieval approach and it has two additional sub-phases, namely the class-level feature scoring phase and the class-level final score and ranking phase, which ranks the top buggy classes. The second level localizes the buggy methods inside these classes using an information retrieval approach and it has two sub-phases, which are the method-level feature scoring phase and the method-level final score and ranking phase, which ranks the top buggy methods inside the localized classes. A model is evaluated using an AspectJ dataset, and it can correctly localize and rank more than 350 classes and more than 136 methods. The evaluation results show that the proposed model outperforms several state-of-the-art approaches in terms of the mean reciprocal rank (MRR) metrics and the mean average precision (MAP) in class-level bug localization. Full article

Recently, advancements in image sensor technology have paved the way for the proliferation of high-dynamic-range television (HDRTV). Consequently, there has been a surge in demand for the conversion of standard-dynamic-range television (SDRTV) to HDRTV, especially due to the dearth of native HDRTV content. However, since SDRTV often comes with video encoding artifacts, SDRTV to HDRTV conversion often amplifies these encoding artifacts, thereby reducing the visual quality of the output video. To solve this problem, this paper proposes a multi-frame content-aware mapping network (MCMN), aiming to improve the performance of conversion from low-quality SDRTV to high-quality HDRTV. Specifically, we utilize the temporal spatial characteristics of videos to design a content-aware temporal spatial alignment module for the initial alignment of video features. In the feature prior extraction stage, we innovatively propose a hybrid prior extraction module, including cross-temporal priors, local spatial priors, and global spatial prior extraction. Finally, we design a temporal spatial transformation module to generate an improved tone mapping result. From time to space, from local to global, our method makes full use of multi-frame information to perform inverse tone mapping of single-frame images, while it is also able to better repair coding artifacts. Full article