Search Results (37,221)

For the challenging scenario in which only normal node labels are available in semi-supervised graph anomaly detection, existing generative methods usually synthesize abnormal nodes through random perturbation or feature interpolation. However, these methods fail to consider node abnormality comprehensively from both structural and attribute perspectives, resulting in generated pseudo-anomalies of limited quality and insufficient reliability. In order to address this problem, we propose DGAM (dual-guided anomaly mining) , a framework for selecting pseudo-anomaly nodes based on the dual-index measurement of topological anomaly and feature consistency. The core of the framework is the joint anomaly evaluation module, which quantifies node anomaly through two computable metrics. The topological boundary score (TBS) measures the boundary of a node’s topological position based on the proportion of connections between a node and labeled normal nodes in its K-hop neighborhood. The feature deviation score (FDS) evaluates the consistency of a node’s local features by calculating the average cosine similarity between its features and those of its K-hop neighbors. The module selects a fixed set of nodes with higher comprehensive anomaly scores from the labeled normal nodes as pseudo-anomalies, so as to construct a training set containing explicit supervision signals. The model adopts a shared encoder architecture and jointly optimizes the classification loss based on pseudo-labels and the embedding regularization loss of the graph nodes to learn a more discriminative node representation. Experimental results on multiple real-world graph datasets show that DGAM can stably improve anomaly detection performance, effectively verifying the effectiveness of the proposed screening mechanism and joint training strategy. Full article

Implantation of a femoral stem in total hip arthroplasty alters physiological load transfer within the proximal femur. Short-stem designs aim to preserve bone stock and maintain proximal load sharing, yet the influence of femoral neck resection height and its interaction with femoral morphology on primary stability remain insufficiently understood. This in vitro biomechanical study investigated these effects using 33 human femora classified as Dorr B or C. In a paired design, a cemented calcar-guided short stem was implanted with either a low (standard) or +5 mm higher femoral neck resection. Specimens underwent cyclic fatigue loading to assess reversible and irreversible micromotion and interface strain, followed by ultimate compression to quantify global fixation strength. Primary stability was assessed by reversible and irreversible translation of the prosthetic head center of rotation and by cortical interface strain measurements using digital image correlation. Overall fixation strength and irreversible deformation remained comparable across resection heights and Dorr types. In contrast, resection height and femoral morphology influenced reversible micromotion and interface strain, with higher resection reducing reversible micromotion, particularly in Dorr C femora and shifting lateral interface strain toward compression. These findings suggest that surgical technique and femoral morphology mainly affect local, reversible bone–cement–implant mechanics rather than global fixation strength. Full article

Radiation detection technology is critical in medical diagnosis, high-energy physics experiments, nuclear environmental monitoring, and radiation safety protection. Its technological iteration stems from innovations in high-performance radiation detection materials. Traditional materials often have narrow dose–response intervals, insufficient high-precision measurement capability, low spatial resolution, and poor stability, failing to meet high-precision detection requirements. Ag-doped phosphate glass (Ag-PG), based on radio-photoluminescence (RPL), effectively addresses these limitations with its comprehensive advantages: high radiation sensitivity, a wide linear dose–response range, submicron spatial resolution for radiation imaging, write-erase-rewrite capability, and visualized dose monitoring potential, and it also boasts significant fundamental research value and engineering application prospects. Specifically, while existing RPL reviews mainly provide a comprehensive analysis from the perspective of RPL and present typical RPL material systems, this paper systematically analyzes the structural characteristics of the Ag-PG matrix and the coordination configuration and site occupation of Ag ions. It clarifies RPL luminescence properties, dose–response mechanisms, and the evolution of luminescence centers, while reviewing advancements in applications such as radiation dose detection and high-resolution X-ray imaging. By summarizing the current research status, technical advantages and existing challenges of Ag-PG, this study provides theoretical references and conceptual insights to promote breakthroughs in its fundamental research and practical applications in high-precision radiation dose detection, advanced medical imaging, micro-nano-scale radiation detection, and nuclear industry non-destructive testing. Full article

Visual explainable artificial intelligence (XAI) is an important mechanism for connecting analytically complex decision models with practitioners who must interpret and act upon their outputs in industrial supply chains. In facility localization problems, wafer foundries and other capital-intensive manufacturers must evaluate geographically dispersed candidate sites against multiple uncertain criteria. The ability to communicate fuzzy group decision-making (FGDM) outcomes in a transparent, interpretable form has direct operational relevance. The literature has introduced hanging gradient bar charts, gradient bidirectional scatterplots, and traceable aggregation charts as visual XAI instruments for semiconductor supply chain localization that show substantial reductions in interpretation error versus conventional plots. However, the quantitative assessment of explanation quality itself remains underdeveloped. To address such a gap, this research proposes a quantitative explainability quality index (XQI) that formalizes visual explanation quality in FGDM as a composite measurable construct. XQI integrates two complementary layers: (1) An objective explainability layer (OEI), consisting of normalized fuzzy interpretation deviation, response time, ranking fidelity, and interpretation accuracy, and (2) a subjective explainability layer (SEI), consisting of perceived understanding, perceived transparency, decision confidence, and cognitive load. Trust, acceptance, and decision quality are downstream outcome constructs rather than components of the index. A weighted linear combination of OEI and SEI produces a single index for systematic, reproducible comparison across competing visualization designs. A structural equation model is specified as a planned validation mechanism for examining how explanation quality may relate to trust, acceptance, and downstream decision quality. The proposed validation framework includes a semiconductor facility localization scenario, three visualization conditions, and a planned participant pool of 150–240 supply chain managers, engineers, and graduate students. The XQI framework transforms visual XAI from a descriptive communication aid into a testable decision-support construct, thereby addressing a key evaluation gap in the FGDM visualization literature. Full article

With the rapid growth of the digital economy, the application of artificial intelligence (AI) technology has injected new momentum into persistent green innovation. Using data on Chinese A-share listed companies from 2010 to 2023, this article aims to investigate whether senior executives’ adoption of AI technology influences companies’ persistent green innovation and to identify the specific mechanisms underlying this relationship. To improve measurement accuracy, this paper employs the BERT model to conduct an in-depth analysis of corporate annual report texts to construct an executive AI adoption metric. The findings reveal that executive AI adoption significantly promotes corporate persistent green innovation, and this effect is primarily achieved through enhanced data factor allocation capabilities. Moreover, strategic agility positively moderates the relationship between executive AI adoption and corporate persistent green innovation. Specifically, the higher the level of strategic agility, the stronger the mediating role of data factor allocation in the relationship between executive AI adoption and corporate persistent green innovation. In particular, executive AI adoption plays a more significant role in fostering persistent green innovation among firms with higher total factor productivity and those facing intense market competition. Full article

Background: Acinetobacter baumannii (A. baumannii) is a critical-priority pathogen of major concern in healthcare settings. Colistin remains a last-resort antibiotic for multidrug-resistant (MDR) A. baumannii infections; however, resistance is increasingly reported worldwide yet remains understudied in Bahrain. Reliable detection methods and understanding clonal dissemination are essential for infection control. Objectives: This study aimed to (1) determine the rate of colistin resistance in 102 clinical A. baumannii isolates from Bahrain, (2) evaluate the diagnostic performance of the colistin agar test (CAT) and E-test against broth microdilution (BMD method), and (3) assess clonal relationships using BOX-PCR fingerprinting. Methods: 102 clinical isolates from multiple hospitals in Bahrain underwent susceptibility testing via the BMD method, CAT, and E-test; screening for mcr-1 to mcr-5 genes; and BOX-PCR DNA fingerprinting. Results: Colistin resistance was detected in 14.7% of isolates by BMD method, higher than regional and global averages. All resistant isolates were mcr-negative, suggesting chromosomally mediated resistance. CAT showed 86.7% sensitivity, 98.8% specificity, and a 13.3% very major error rate. The E-test failed to detect resistant isolates (very major error 100%). BOX-PCR revealed predominant clonal relatedness with intra- and inter-hospital spread. Conclusions: Colistin resistance in A. baumannii from Bahrain exceeds regional and global levels, likely driven by chromosomal mechanisms under selective pressure. The BMD method remains the gold standard for colistin testing, while CAT may serve as a screening tool requiring confirmation. Strengthened stewardship and infection control measures are vital to contain dissemination. Full article

Deep learning-powered Street View Imagery (SVI) analytics provides a critical mechanism for smart city perception within the framework of Sustainable Development Goal 11 (SDG 11), effectively bridging the gap left by traditional remote sensing in fine-grained street-level observation. Over the years, deep learning-based semantic segmentation of urban streetscapes has become the dominant paradigm. However, when scaling to megacity measurements, current research faces the dual bottlenecks of “computational redundancy” and the “geographical domain shift” caused by the blind application of pre-trained models based on Western datasets. To address these challenges, this study is the first to systematically quantify the performance trade-off between Multi-Task Learning (MTL) and Single-Task Learning (STL) in megacity scenarios. Using this as a baseline, we constructed and validated a “low-computation, high-robustness” framework for streetscape semantic perception and spatial measurement. Relying on an integrated ResNeXt101-FPN MTL architecture and an ultra-low-cost fine-tuning strategy to overcome geographical domain shift, we extracted and analyzed the spatial heterogeneity of five core semantic elements—vegetation, sky, building, road, and vehicle—across the road network within Beijing’s 5th Ring Road. The results indicate the following: (1) We explicitly defined the computation-accuracy trade-off of MTL and STL in megacity perception. While utilizing only 1/5 of the parameters of STL, the MTL framework achieved a 5.34-fold increase in inference speed with a negligible 0.1% loss in overall mean Intersection over Union (mIoU); however, a 27.13% decrease in boundary segmentation accuracy was observed. (2) We established a low-cost, localized correction paradigm to overcome domain shift. Utilizing a minimal annotation cost (only 200 local images) significantly improved cross-domain adaptability, boosting the overall mIoU by 8.92% and significantly mitigating the geographical domain shift problem. (3) Multi-dimensional measurement and spatial analysis revealed a significant spatial decoupling pattern in Beijing’s streetscapes. The visual proportion of vegetation exhibited a pronounced “north-high, south-low” spatial differentiation, whereas built environment elements (e.g., building and road) displayed a typical “center-periphery” concentric gradient. This objectively reflects the spatial inequality of urban street greenery resources and the monocentric development characteristics of the built environment. The proposed framework therefore serves as a low-cost, AI-driven computational paradigm for smart city perception in resource-constrained regions. Furthermore, the revealed spatial heterogeneity offers data-driven insights for formulating sustainable urban renewal policies aligned with SDG 11. Full article

The horizontal bearing mechanism of pile–soil composite foundations adjacent to retaining walls is significantly affected by asymmetric lateral constraints caused by retaining wall movement, a scenario that remains inadequately explored in conventional design. This study employs a validated three-dimensional finite element model to investigate the response of such foundations to rotational displacement of a nearby wall. A comprehensive parametric analysis quantifies the influence of pile configuration, cushion properties, soil modulus, and loading conditions. The results demonstrate that rotational displacement (RB mode) induces a highly non-uniform load distribution within the pile group. The middle-front row piles emerge as critical load-bearing components, experiencing significant load amplification (load-transfer coefficients η_p up to 2.3). Key parameters, including pile length and cushion stiffness, selectively regulate system stiffness or optimize load sharing. Increasing the pile–wall distance is identified as an effective measure to reduce load concentration on front-row piles. The findings provide quantitative insights and practical guidance for the performance-based design of composite foundations under asymmetric constraints. Full article

Background: This in vitro study evaluated the influence of macro and microscopic implant design, drilling protocol, and bone density on the primary stability of 4 mm ultrashort dental implants, aiming to provide evidence-based guidance for their use in severely atrophic posterior jaws. Methods: Two implant systems were compared: test group (Klockner Essential Cone^® conical implants with polished neck, diameters 4.0 mm [B1] and 4.5 mm [B2], shot-blasted and acid-passivated surface) and control group (Straumann Standard Plus^® 4.1 mm parallel-walled implants with SLA^® surface). A total of 722 implants (n = 30 per condition) were inserted into natural bone blocks simulating Lekholm and Zarb type II (cortical-dominant) and type III (medullary-dominant) bone qualities. Fifteen experimental conditions were tested, combining three main drilling protocols: (1) manufacturer’s standard preparation, (2) horizontal under drilling (final diameter 3.5 mm), (3) vertical overpreparation (1 mm deeper), and (4) combined vertical + horizontal restriction. Primary stability was assessed by insertion torque (measured with a calibrated Tohnichi^® torque wrench) and Implant Stability Quotient (ISQ) using Penguin^® resonance frequency analysis (RFA) in two perpendicular directions. Subjective insertion ease and complications were also recorded. Conclusions: The conical macrogeometry with progressive, dense V-shaped threads provides significantly better primary mechanical anchorage than parallel-walled designs in ultrashort (4 mm) implants. Within the limitations of this ex vivo animal bone model study, the results indicate that different drilling protocols significantly influence the primary mechanical stability with insertion torque ≥ 25 Ncm and ISQ ≥ 55 of ultra-short implants, as measured by insertion torque and ISQ values. Certain drilling protocols resulted in higher insertion torque and ISQ compared to others, particularly in Type II and Type III bone. Full article

Surface hardness is a fundamental parameter influencing wear resistance, durability, and the interaction of occlusal ramps with opposing enamel during orthodontic treatment. Five commercially available materials (Harmonize, Leone F3172-01, Transbond™ XT, Band and Build LC, and Ultra Band-Lok) and one experimental material (Composite RK-F10) were evaluated for bite ramps. Twelve standardized specimens (n = 2 per material) were prepared using EVA molds and polymerized according to manufacturers’ instructions or internal protocols. Vickers microhardness (HV) was measured following ASTM E384-16 using a 500 g load, 20 s dwell time, and ten indentations per specimen. Load dependence was assessed (25–2000 g). Surface morphology was analyzed by SEM, and cytotoxicity of eluates was evaluated on dental pulp stem cells (DPSCs) and monocyte/macrophage cell lines using CCK-8 assays (ISO 7405, ISO 10993). Significant differences in hardness were observed among materials (p < 0.05). Harmonize (64.5 ± 1.6 HV), Band and Build LC (64.4 ± 1.9 HV), and Ultra Band-Lok (64.1 ± 2.0 HV) showed the highest values, whereas Transbond™ XT exhibited the lowest value (53.7 ± 6.0 HV). Composite RK-F10 demonstrated intermediate hardness and good cytocompatibility. SEM analysis revealed differences in surface homogeneity and filler distribution. Overall, the materials exhibited distinct mechanical and biological profiles. The combined Vickers microhardness, short-term (24 h) cytotoxicity, and SEM data provide an integrated preliminary in vitro characterization of materials for bite ramps. The observed differences contribute to a comparative description of their physico-biological behavior. Full article

Achieving efficient and high-quality surface processing of single-crystal diamond (SCD) remains challenging due to its extreme hardness and chemical inertness. Traditional Fenton-based slurries using H₂O₂ suffer from poor stability, safety risks, and strict acidic pH requirements. In this study, peroxymonosulfate (PMS) is introduced as an alternative oxidant to develop a novel chemical mechanical polishing (CMP) slurry for SCD. Compared with H₂O₂, PMS exhibits higher stability and generates sulfate radicals (SO₄⁻·) with stronger oxidation capability when activated by Fe²⁺. The proposed slurry achieves efficient material removal over a wider pH range (2–6). Under optimal conditions (pH = 3), a maximum material removal rate (MRR) of 676 nm/h is obtained, along with an ultra-smooth surface (Sa = 0.177 nm in the measuring area of 868 × 868 μm²). Notably, the slurry maintains high MRR (>400 nm/h) even under weakly acidic conditions (pH 5–6). XPS and radical quenching experiments confirm that continuous generation of reactive radicals promotes surface oxidation and stable material removal. This work provides a stable and efficient CMP slurry for SCD with enhanced pH adaptability. Full article

The coordinated variation between plant size and functional traits is a critical link connecting individual ecological strategies and community assembly. However, unlike angiosperms, the drivers of trait–size coordination in coexisting fern species remain unclear. This study sampled seven coexisting fern species in a subtropical secondary forest, measuring biomass (an indicator of plant size) and functional traits related to leaf and root morphology and elemental composition. The coordinated relationship between plant individual size and functional traits was investigated using regression and principal component analysis, while the relative contributions of phylogeny, species identity, and individual biomass to trait variation were quantified via Bayesian phylogenetic generalized linear mixed models. Results indicated that there is a clear trait–size coordination relationship. Specifically, significant linear or nonlinear relationships were identified between plant size and multiple functional traits (e.g., elemental concentrations, specific leaf area, and specific root length), indicating a transition from “fast-acquisitive” to “conservative” strategies. However, variance partitioning indicated that phylogeny and species identity together explained the majority of variation in leaf and root traits (71.4% on average), whereas the independent contribution of individual biomass was minimal (7.1% on average). The results suggest that although significant trait–size coordination exists in understory fern communities, this coordination is statistically dominated by evolutionary history (phylogeny and species identity), though the ecological significance of plant size remains evident in significant trait–size coordination patterns. Overall, the coordinated variation between plant size and functional traits is pivotal in forging resource-allocation strategies and fostering fern species coexistence, highlighting that evolutionary background must be foregrounded when disentangling the mechanisms of functional community assembly. Full article

Time-of-Flight (ToF) 3D imaging suffers from diverse systematic and non-systematic errors that limit its practical performance and reliability. Reliable simulation is critical for understanding these error mechanisms and guiding performance improvement. Therefore, this paper proposes a multi-error coupling simulation framework for ToF 3D imaging based on optical path unit decomposition. By decomposing the full light propagation chain and systematically integrating established typical error mechanisms into their corresponding physical stages, we produce simulation results that closely match real-world sensor measurements. Validated through laboratory and real-scene experiments, the proposed method outperforms mainstream approaches in RMSE, PSNR, and relative error metrics, accurately reproducing the depth distortion and noise characteristics of real ToF sensors. This multi-error coupled modeling method effectively bridges the gap between simulation and actual measurement, offering a credible reference for ToF system error evaluation, parameter optimization, and performance enhancement. Full article

Office openness comprises two physically distinct dimensions—spatial openness and visual openness—yet studies quantifying their independent contributions to cognitive efficiency at the individual level remain scarce. Prior research has predominantly reported group-mean effects, leaving bidirectional individual responses insufficiently examined. This study independently manipulated both dimensions and measured individual-level EEG responses in 24 adults using a 3 × 3 within-subject factorial design. The beta/alpha ratio change rate was computed as an index of cognitive efficiency. Substantial neurophysiological variation across conditions was confirmed in every participant. The absence of significant group-level effects was interpreted not as a lack of environmental influence but as the result of bidirectional individual responses canceling each other out in group averages. Spatial and visual openness induced response ranges of equivalent magnitude at the individual level, and individually optimal conditions were widely distributed across the nine experimental conditions. The correspondence rate between subjective preferences and EEG-identified optimal conditions did not exceed chance, and this bidirectional cancellation mechanism is proposed as an explanation for the contradictory findings that have long characterized open-office research. These results support design strategies that offer diverse combinations of spatial and visual openness within activity-based working environments, paired with feedback systems grounded in objective cognitive performance data. Full article

Diffusion probabilistic models are powerful generative tools but are purely data-driven, limiting their ability to incorporate domain knowledge—such as physical laws, degradation trends, or engineering priors—in scientific and engineering applications. We introduce Prior-Guided Diffusion Processes (PGDPs), a unified mathematical framework that integrates arbitrary differentiable prior knowledge into the reverse diffusion dynamics by augmenting the score function with a guidance term derived from a prior potential V(x,t) and weighted by a time-dependent strength γ_t. This formulation subsumes existing mechanisms (classifier guidance, model-based diffusion, physics-informed corrections) as special cases. We analyze the guided path measures, providing an upper bound on the Kullback–Leibler divergence between guided and unguided marginals (Theorem 1), quantifying the inherent trade-off between data fidelity and prior satisfaction. Experiments on synthetic data confirm the predicted dependence on γ_t. On the NASA C-MAPSS turbofan benchmark, we enforce compressor-oriented physical constraints (e.g., speed–pressure consistency, monotonicity) within PGDP; remaining useful life scores are reported only as reference metrics under transparent protocols. A cross-domain study on the NASA IGBT accelerated aging dataset, using the same backbone with a replaced physics module, achieves a 99.98% reduction in monotonicity loss, demonstrating generality across distinct degradation mechanisms. PGDP provides a principled, extensible template for knowledge-informed generative modeling with theoretical guarantees and verifiable physical consistency. Full article